# Exploring the floristic diversity of tropical Africa

Marc S. M. Sosef, Gilles Dauby, Anne Blach-Overgaard, Xander van Der Burgt, Luís Catarino, Theo Damen, Vincent Deblauwe, Steven Dessein, John Dransfield, Vincent Droissart, et al.

► To cite this version:

Marc S. M. Sosef, Gilles Dauby, Anne Blach-Overgaard, Xander van Der Burgt, Luís Catarino, et al. Exploring the floristic diversity of tropical Africa. BMC Biology, 2017, 15 (1), 10.1186/s12915-017-0356-8. doi: 10.1186/s12915-017-0356-8

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# Exploring the floristic diversity of tropical Africa

Marc S. M. Sosef<fnref n="1" />*†, Gilles Dauby<fnref n="2" />,¹²,²¹†, Anne Blach-Overgaard³, Xander van der Burgt⁴, Luís Catarino⁵,
Theo Damen⁶, Vincent Deblauwe<fnref n="2" />,⁷,⁸,²²,²³, Steven Dessein<fnref n="1" />, John Dransfield⁴, Vincent Droissart⁷,⁹,²⁰,
Maria Cristina Duarte⁵, Henry Engledow<fnref n="1" />, Geoffrey Fadeur⁷, Rui Figueira¹⁰,¹¹, Roy E. Gereau⁹, Olivier J. Hardy¹²,
David J. Harris¹³, Janneke de Heij¹⁴,¹⁵, Steven Janssens<fnref n="1" />, Yannick Klomberg¹⁴,¹⁶, Alexandra C. Ley¹⁷,
Barbara A. Mackinder⁴,¹³, Pierre Meerts⁷,¹⁸, Jeike L. van de Poel¹⁴, Bonaventure Sonké⁸, Tariq Stévart<fnref n="1" />,⁷,⁹,
Piet Stoffelen<fnref n="1" />, Jens-Christian Svenning³, Pierre Sepulchre¹⁹, Rainer Zaiss²⁰, Jan J. Wieringa⁶,¹⁴ and
Thomas L. P. Couvreur<fnref n="2" />,⁸,¹⁴*

## Abstract

**Background:** Understanding the patterns of biodiversity distribution and what influences them is a fundamental pre-requisite for effective conservation and sustainable utilisation of biodiversity. Such knowledge is increasingly urgent as biodiversity responds to the ongoing effects of global climate change. Nowhere is this more acute than in species-rich tropical Africa, where so little is known about plant diversity and its distribution. In this paper, we use RAINBIO – one of the largest mega-databases of tropical African vascular plant species distributions ever compiled – to address questions about plant and growth form diversity across tropical Africa.

**Results:** The filtered RAINBIO dataset contains 609,776 georeferenced records representing 22,577 species. Growth form data are recorded for 97% of all species. Records are well distributed, but heterogeneous across the continent. Overall, tropical Africa remains poorly sampled. When using sampling units (SU) of 0.5°, just 21 reach appropriate collection density and sampling completeness, and the average number of records per species per SU is only 1.84. Species richness (observed and estimated) and endemism figures per country are provided. Benin, Cameroon, Gabon, Ivory Coast and Liberia appear as the botanically best-explored countries, but none are optimally explored. Forests in the region contain 15,387 vascular plant species, of which 3013 are trees, representing 5–7% of the estimated world’s tropical tree flora. The central African forests have the highest endemism rate across Africa, with approximately 30% of species being endemic.

**Conclusions:** The botanical exploration of tropical Africa is far from complete, underlining the need for intensified inventories and digitization. We propose priority target areas for future sampling efforts, mainly focused on Tanzania, Atlantic Central Africa and West Africa. The observed number of tree species for African forests is smaller than those estimated from global tree data, suggesting that a significant number of species are yet to be discovered. Our data provide a solid basis for a more sustainable management and improved conservation of tropical Africa’s unique flora, and is important for achieving Objective 1 of the Global Strategy for Plant Conservation 2011–2020.

**Keywords:** Herbarium specimens, Digitization, Tropical forests, Botanical exploration, Plant growth form, Species richness, Floristic patterns

<footnote>* Correspondence: marc.sosef@botanicgardenmeise.be;
thomas.couvreur@ird.fr
Marc S.M. Sosef and Gilles Dauby are co-first authors.</footnote>

<footnote>†Equal contributors</footnote>

<footnote n="1">¹Botanic Garden Meise, Nieuwelaan 38, BE-1860 Meise, Belgium</footnote>

<footnote n="2">²DIADE, Université Montpellier, IRD, Montpellier, France
Full list of author information is available at the end of the article</footnote>
## Background

Documenting the distribution of biodiversity is the first and most fundamental step for effective conservation and sustainable utilisation of natural resources for the future [1]. Tropical Africa [2] (Fig. 1) is home to some of the most important species-rich biodiversity regions in the world [3]. From the second largest extent of continuous rain forest in the world, the Congo basin, to the Namib dessert, tropical Africa is a land of strong biodiversity contrasts [4]. Yet, it has already lost large amounts of its ‘wilderness areas’ [5], i.e. areas where ecological and evolutionary processes are little affected by human disturbance. In addition, future climate change is expected to have important negative effects on sub-Saharan ecosystems, with an estimated 90% of species losing part or most of their areas of suitable climate by 2085 [6]. These results call for immediate international policies to be put in place [5, 7, 8]; however, they will be hard to achieve without a better understanding of plant diversity and its distribution.

Herbaria around the world are the keepers of such essential data for plants [7, 9, 10]. Together, they curate a vast databank of about 350,000,000 physical dried plant specimens [11]. Any conserved specimen provides unique proof of where and when a particular species was present. With ongoing and intensified efforts of natural history institutions to digitize their scientific heritage, the possibility of providing large amounts of data to a much wider audience has rapidly expanded over the last few years [12–16] (see [17] for an overview of the number of scanned herbarium specimens in the world’s largest virtual herbaria).

Relying on such data, several large-scale initiatives have been undertaken focusing on, for example, the tree flora of the Amazon basin [18–20], enabling access to more detailed estimates on plant diversity and distribution in this species-rich region. To date, however, our knowledge of tropical plant distribution remains limited [21–23] and tropical Africa is no exception to the rule [24, 25]. Indeed, total species estimates for different

<figure><img src="image_3.png" /><figcaption>**Fig. 1** Distribution of botanical records across tropical Africa. Number of specimens (a) and observed species richness (b) per 0.5° sampling units. Dashed lines represent the limits for tropical Africa as defined in our study. Map based on georeferenced herbarium records, silica gel samples and plot data</figcaption></figure>
countries, regions and biomes are lacking or are ap-
proximate estimates at best. For example, White [26] in
his much-cited *The Vegetation of Africa* provided broad
estimates of plant species diversity for each of his recog-
nised 'phytochoria', numbers that are still used today.
However, these values were not based on any formal
analyses of biodiversity data, but rather on expert opin-
ion. In addition, there have been numerous efforts to
estimate the total number of tree species across the tro-
pics, either in general for the tropics [27] or for specific
regions such as the Amazon basin [20]. Few estimates of
total tree diversity for Africa have been advanced. Using
extrapolations from global plot data, Slik et al. [27] esti-
mated a minimum number of 4626–5984 tree species
for Africa (including Madagascar). In general, tropical
Africa is deemed to be botanically less diverse than
regions at similar latitudes in South America and South
East Asia [27–29]. Numerous hypotheses exist which try
to explain this ‘odd man out’ pattern (reviewed in [28])
but no clear conclusive explanation has yet been provided.

During the last 15 years, efforts to database tropical African plants have been undertaken, but generally with the purpose of analysing large scale phytogeographical patterns across Africa [30–32]. Thus, these datasets are characterised by an overrepresentation of well-known and comparatively widespread species. More recently, Stropp et al. [25] extracted and analysed all flowering plant data openly available via the Global Biodiversity Information Facility (GBIF, http://www.gbif.org) for Africa (including Madagascar). They concluded that the quality and completeness of species-occurrence data of flowering plant species available through Biodiversity Information Systems such as GBIF is low. In addition, over half of the records they retrieved did not relate to tropical Africa but to South Africa and Madagascar. Thus, to date, large uncertainties remain about plant species diversity and distribution across tropical Africa. The digitization and georeferencing of major African-specialised herbarium collections have substantially progressed these past years [16, 17]. When these independent efforts are compiled and concatenated into a single quality-checked comprehensive mega-database, they will provide new and detailed insights into tropical African plant diversity and distribution as never achieved before [33].

The goal of this paper is to document the main floristic
patterns and information gaps for tropical Africa. For this,
we use the largest and highest quality mega-database of
vascular plant species distributions ever compiled for
tropical Africa (RAINBIO, [33]). First, we explore the
spatial distribution of records and species diversity statis-
tics at regional and country levels and ask the following
questions: how is plant species diversity distributed across
tropical Africa? How many plant species are there in

tropical African forests? How is this diversity partitioned
in terms of growth form? Which regions have the
highest floristic turnover rates? Second, we analyse
the level of botanical exploration across tropical
Africa and ask the following questions: have tropical
African countries been adequately explored? What re-
gions are well sampled and which are not? Can we
identify areas where future sampling would be the
most efficient? Finally, we conclude with a series of
recommendations in order to improve our knowledge
of the floristic diversity of tropical Africa.

Methods

Data and study area

All analyses were performed using different subsets (i.e. with or without silica gel information, see below) of the RAINBIO database [33]. In short, RAINBIO is a compilation of:

* publicly available datasets mainly from international herbaria;

* personal ones such as datasets on palms, legumes, orchids, Rubiaceae, Marantaceae, flora of Gabon, flora of the Dzanga-Sangha region (Central African Republic);

* tree plot inventory data from Gabon;

* georeferenced silica-dried samples for tree species from Central Africa.

The data were checked automatically and manually via
experts for quality in several ways, including geographic
and taxonomic standardisation, merging of duplicate
records (originating from different datasets), and exclu-
sion of cultivated/non-native plants. A full description of
RAINBIO, how it was compiled and how the data were
checked and verified can be found in Dauby et al. [33].
The RAINBIO dataset contains important additions and
a significant increase in data quality compared to the
data available through the GBIF (see, [25] for biodiver-
sity analyses of data only available via GBIF), and has a
strong focus on tropical Africa [33].

Our study area is tropical Africa, which Klopper et al.
[2] broadly defined as sub-Saharan Africa and excludes
southern Africa and Madagascar (Fig. 1, roughly between
16°N and 20°S). For delimitating this study area, we relied
on the ecoregions defined by Olson et al. [34] and used
the eco-region 'south Saharan steppe and woodlands' as
the northern limit and ecoregions 'Namibian savanna
woodlands', 'Kalahari xeric savanna', 'Kalahari Acacia-
Baikiaea woodland', 'Highveld grasslands' and 'Drakensberg
montane grassland' as the southern limits. We filtered the
RAINBIO dataset so species having all of their occurrence
records outside this region were excluded.
## Sampling unit size

In our study, we used two different sampling unit (SU) sizes. First, we simply used a 'fixed-size' SU of 0.5° (ca. 55 × 55 km at the equator). For certain analyses, however, we used an 'adaptive resolution' SU method [35]. This approach adapts the size of the SU as a function of a user-defined threshold of minimum occurrence records (see [35] for details). This method allows one to consider smaller SUs where record density is high, while increasing SUs size where records are sparsely distributed. The advantage is that record numbers remain broadly constant across SUs. In order to create an adaptive SU grid we uploaded the filtered RAIN-BIO database to the Infomap Bioregions [35] website (http://bioregions.mapequation.org). We used the following parameters: maximum cell capacity = 1000; minimum cell capacity = 500; maximum cell size = 8°; minimum cell size = 0.5°. The SU grid was then downloaded as a shapefile and processed in the R environment for excluding unsuitable areas (ocean and lakes) in each SU.

## Species diversity patterns

Species richness was estimated at two different levels: (1) for each fixed-size SU of 0.5° containing at least 100 records, and (2) at country level. Only herbarium specimens were included for this estimation. Plot data and silica gel-dried DNA samples were excluded because of their stronger focus on particular taxa, which are often over-sampled and would introduce a bias. In each case, an observed and estimated total species number was calculated. For the observed species number per country we used georeferenced and non-georeferenced specimens. As species richness estimation is strongly affected by sampling efforts, we computed two complementary richness estimators. First, we calculated a non-parametric species richness estimator [36], known as the Chao1 [37], Š, defined as:

$$ \hat{S} = S_{obs} + \frac{a_1^2 N - 1}{2a_2 N} $$

Where $a_1$ is the number of species represented by one specimen (singleton), $a_2$ is the number of species represented by two specimens (doubleton), $S_{obs}$ is the observed number of species and $N$ is the total number of specimens. Second, we estimated species richness using a subsampling procedure. This approach has proven to be robust when dealing with incomplete and heterogeneous sampling [38–40]. We used the Nielsen's estimator $N_e$ [41] of effective number of species [39], which corresponds to a nearly unbiased estimator of the popular Gini-Simpson diversity index [37] converted into effective number of species [38].

This estimator is defined as [41]:

$$ N_e = \frac{(N-1)^2}{3 - N + (N+1)(N-2) \sum_{s}^{i} p_i^2} $$

Where $N$ is the total number of specimens, $p_i$ is the frequency of the $i$th species and $S$ is the observed number of species. This estimator does not estimate total species richness, but expresses the diversity in terms of 'effective number of species'. In addition to its good statistical properties [38], this metric has the advantage of satisfying the 'replication principle' [37]. This basically means that the ratio of diversity values reflects 'true' differences in diversity just as actual species richness would do [42].

## Floristic turnover rates

* We assessed the level at which floristic composition changes in space by computing a 'neighbourhood' species turnover rate applying a three-step procedure (Additional file 1). For this parameter, we used the adaptive resolution SU grid as defined above.

* **Step 1.** For a focal SU, a convex hull was drawn around all specimen occurrences it contained. A given buffer of distance $\mu$ was added to the hull convex. All other SUs with at least 500 records included in this buffered area were selected for comparison.

* **Step 2.** The pairwise floristic similarity was computed as $1 - \beta_{sim}$ between all selected SUs. The turnover index $\beta_{sim}$ was defined as [43]: $\beta_{sim}^{min(b,c)}$ where $b$ and $c$ are the number of species restricted to the first and second SU, respectively, while $a$ is the number of species shared by the two cells.

* **Step 3.** The geographical distance among all pairs of selected cells was computed based on centroids of occurrences within each SU.

The decay of the floristic similarity with geographical distance was then approximated by a linear model. From this model, we extract the halving distance which was the geographical distance at which the floristic similarity was reduced to 50% [44]. Hence, low halving distances indicated high floristic turnover per distance unit. By changing the distance which determines the neighbourhood SUs that are taken into account ($\mu$), one can investigate different scales of floristic turnover (small $\mu$ = fine-scale; large $\mu$ = large-scale) (see Additional file 1 for illustration of the method). Here, we investigate two values of $\mu$: $\mu = 1°$, representing a meso-scale floristic turnover, and $\mu = 2°$, representing a large scale floristic turnover.
## Sampling completeness

Two estimates of sampling completeness were computed. First, the relative exploration was estimated as the percentage of species already discovered: observed species divided by the estimate of total number of species by Chao1. Second, the sampling coverage estimator of [45] was calculated as:

$$ \hat{C}_N = 1 - \frac{a_1}{N} \left[ \frac{(N-1)a_1}{(N-1)a_1 + 2a_2} \right] $$

A sampling coverage of 1 indicates that all species have been collected twice or more, while for a value of 0 all species are known by a single record only.

We identified the ‘best-explored’ countries, namely countries with a relative exploration value above 0.85 and sampling coverage higher than 0.95. However, this does not imply they were botanically well known, but rather, compared to others, they had the best data available to date. Figures for countries where the number of specimens was lower than the estimated number of species were deemed unreliable and thus not included in the results; obviously, these countries were poorly explored (or our data for them was largely incomplete).

Using adaptive resolution SUs we identified three major categories of SUs depending on their level of sampling and coverage. First, we defined ‘well-sampled units’ (WSU) representing SUs with a minimum collection density of 100 records for 100 km² [46] and a sampling coverage estimator equal to or higher than 0.5. Second, we defined ‘top sampled units’ (TSU) as the top 25% of SUs with the highest density records and sampling coverage estimator equal to or higher than 0.5 and excluding WSUs. Third, we proposed a number of ‘priority sampling units’ (PSU) defined as a SU where meso-scale species turnover (see Floristic turnover rates section) was estimated to be high (the top 20% of SUs with lower halving distances), but where sampling completeness was low (smaller than 0.5). Thus, PSUs translate the idea that sampling should focus on poorly known areas with sharp floristic gradients. For example, SUs with low coverage but located in low turnover regions will be important to explore but might not lead to the addition of many new species for the region, if a nearby SU has already been well sampled. This approach should be viewed as complementary to other methods that have identified data-deficient areas in Africa [24, 25].

Finally, we estimated the level of sampling completeness across all tropical African species. For that, we assessed the number of specimens for each species as a function of the number of fixed-sized SUs occupied by that species. A scatter plot was then produced where each point represented a species, and the slope represented the average number of specimens per grid cell for all species. In order

to have an idea of the historical evolution of this value through time, we generated the same plots but at 13 different time slices in the past, going back every 10 years until 1900.

All computations were done within the R statistical software (R Core Team 2015) using the *vegan* package [47] for the Chao1 estimator, *entropart* [48] for the sampling completeness estimator, *betapart* [49] for the $\beta_{sim}$ computation and an R function built by G. Dauby to estimate the Nielsen parameter (Additional file 2).

## Estimation of species distributions

The distribution surface, or range size, of each species was estimated by calculating the surface within the convex hull formed by all specimen localities of that species. Large salt water bodies were excluded from the surface measurements. Species with a single georeferenced specimen were assigned an arbitrary range size of 1 km². For species known from only two georeferenced specimens, the range size was estimated arbitrarily by multiplying the distance between the two localities by 0.1.

## Growth form diversity

Most species (91%) recorded in RAINBIO were able to be categorised into nine different growth form types (tree, shrub, herb, liana, vine, aquatic herb, epiphyte, mycoheterotroph and parasitic) [33]. For detailed information on how this was achieved, please see [33]. In short, main categories were automatically assigned through an ad hoc custom R script by extracting keywords for each habit from available herbarium specimen label note information. A total of 4751 species names (22% of the total filtered dataset, see below) were manually checked by experts in order to confirm automatic assignments (when two or more habits were suggested, 2823 species) and to fill in missing assignments (1928 species). Besides these specific cases, a large majority of habit assignments were confirmed (but no proportion can be provided). We considered species to be woody if they were classified as trees, shrubs or lianas, and as herbaceous when they were classified as herbs, vines, aquatics or mycoheterotrophs (parasitic species were not included because they are a mixture of both woody and herbaceous species). For each adaptive resolution SU we generated the total number of species, and the ratio of the number of species in each of five growth forms: (1) herbs, vines and mycoheterotrophs, (2) shrubs, (3) liana, (4) trees, and (5) epiphytes.

## African tropical forest diversity

In order to provide species diversity values for African tropical forests, we used the map of Mayaux et al. [50] depicting land cover types across Africa and Madagascar for the year 2000. The map consists of 27 different land
cover layers. We selected six of them that consisted mostly, or originally, of tropical forests, namely evergreen forests, degraded evergreen forests, submontane forests, montane forests, swamp forests and mosaic forest-croplands. The original resolution of the map is 1 km². We aggregated the selected land cover at a resolution of 0.1° (ca. 11 km²) because our georeferenced records are generally not that precise. The resulting forest area layer was then arbitrarily divided into a West African block (west to the Dahomey Gap), a central African block (east of the Dahomey Gap, west of the East African rift), and an eastern African block (east of the East African rift) (Additional file 3). Using these sub-units we estimated the number of records, species richness, and number of sub-endemics (considered as a species with 90% or more of its records located within the forest layer) in total and for five different growth forms defined above for African forests in general and for each forest bloc (west, central and east).

# Results

## Spatial distribution of data

The original unfiltered RAINBIO database comprises distribution data for 25,356 species of vascular plants, 3158 genera and 273 families from a total of 614,022 records. The filtered dataset for tropical Africa that was used for our analyses had a total of 609,776 specimens representing 22,577 species. This represents the largest and highest quality dataset ever compiled for tropical African plants. The records are well distributed but heterogeneous across the continent (Fig. 1a). The highest collecting efforts are concentrated in West Africa (Liberia to Benin), south-western Cameroon, Equatorial Guinea, Gabon, the region covering the eastern part (Kivu) of Democratic Republic of the Congo (DRC) plus Rwanda and Burundi, and the Eastern Arc Mountain range in Kenya and Tanzania (Fig. 1a). Although to a lesser extent, records are also well represented in western and south-eastern DRC and Ethiopia. In contrast, there are comparatively few records in most of the Sahelian region (Senegal to South Sudan), the Ogaden (south-eastern Ethiopia), the Central African Republic, Nigeria, the Republic of Congo and Angola.

Our data indicates that just 21 SUs of 0.5° contain more than 3000 records or 100 records per 100 km² (Additional file 4). The observed species richness highlights several regions (Fig. 1b), with the following areas standing out: the Nimba Mountains in north Liberia, the Cameroonian Volcanic Line in western Cameroon, the regions around Kribi, Bipindi and Yaoundé in Cameroon, Rio Muni (continental part of Equatorial Guinea), several areas in Gabon such as the surroundings of Libreville and Makokou, around Yangambi in DRC, the Eastern Arc Mountain range in Tanzania, Abidjan in Ivory Coast and the south

of Benin (Fig. 1b). The observed species richness is highly correlated with specimen density (Pearson correlation *R* = 0.91; compare Fig. 1a). Estimated total species richness for each SU based on the Chao1 (Fig. 2a) is highly correlated to both the number of records per SU (Fig. 1a; Pearson correlation *R* = 0.85) and the observed species richness (Fig. 1b, Pearson correlation *R* = 0.91). In contrast, the Nielsen diversity estimator computed for each 0.5° SU (Fig. 2b) is much less correlated with specimen density (Pearson correlation *R* = 0.28) than the Chao1 estimator.

A total of 3438 species have been collected just once and 8026 collected less than 5 times, while 3172 species have been collected more than 50 times (Additional file 5a). A similar picture is found when looking at the number of 0.5° SUs occupied per species (Additional file 5b). Over 4294 species are only recorded in a single SU (narrow endemics), while 1607 species occur in 50 SUs or more (widespread). The range size distribution of all species (Additional file 5c) shows a relatively low number of exceptionally widespread species (i.e. more than 16,777,216 km²). Species with an ‘intermediate’ range size (i.e. between 8192 and 16,777,216 km²) are most common, while species with small distribution areas are again less common. However, because of the quadratic scale used on the x-axis (to show more detail in the lower categories), the picture actually shows an ever increasing number of species along the x-axis.

The average number of records collected per species per SU is 1.84, calculated as the slope of the linear fit of the numbers of specimens for each species as a function of the number of SUs it occupies (Fig. 3). Species that cluster on the lower left of the graph are limited in distribution while those to the right are comparatively widespread. Species that fall above the red line are collected more often than average while those under the red line are relatively under-collected. Rare species (left end of the plot) that fall above the red line are either locally common (clustered) or have been well-collected (e.g. by collectors focussing on specific groups), while those below the red line are comparatively scattered. Widespread species (right end of the plot) falling above the red line are comparatively abundant (widespread common) or have a positive collecting bias, those below the red line occur as scattered individuals (widespread rare) or are otherwise inconspicuous (minute or seasonal plants, like small saprophytes) or more difficult to collect (palms, large trees, aquatic plants or plants only flowering in the canopy) or have a negative collecting bias (e.g. weeds).

## Temporal distribution of collections

Collecting dates ranged from 1782 to 2015. Collecting intensity across tropical Africa through time has generally
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Estimated botanical diversity of tropical Africa. **a** Estimated species diversity based on the Chao1 estimator for each 0.5° sampling units (SUs) with more than 100 records. **b** Effective number of species estimated using the Nielsen statistic per 0.5° SUs with more than 100 records. Dashed lines represent the limits for tropical Africa as defined in our study. This map is based on georeferenced herbarium records, silica gel samples and plot data</figcaption></figure>

increased, though with lower collecting efforts in the early 1980s, to 2005, after which there has been a significant decline to the present (Fig. 4a). Collecting intensity per country shows different histories, with different periods of intense and low collecting efforts (Additional file 6). For example, DRC knew its highest period of botanical collections from the 1930s to the 1960s (Additional file 6). In contrast, Benin has experienced intense collecting over the last 20 years (Additional file 6). Other countries, such as Cameroon, have known a sustained and important collecting intensity since the 1960s up until 2010.

By plotting the average number of records collected per species per SU in function of 10-year time slices over 130 years, the slope is increasing, except for two periods (1941–1950 and 1961–1970) where the slope decreased (Fig. 4b). This is corroborated by a steady decrease of newly explored SUs since 1980 (Additional file 7).

In order to have a 'time lapse' view of the collecting history throughout tropical Africa, we plotted the year of the oldest collection per SU from 1780 up to 2015 (Fig. 7, see Additional file 8). The first regions to be prospected (shades of blue in Fig. 5) are along the coast lines of East Africa (Kenya and Tanzania), central Africa (Cameroon, Equatorial Guinea, Gabon and Nigeria) and West Africa (Benin, Ghana, Guinea, Ivory Coast and Sierra Leone). Main rivers, such as the Congo River, were also explored during that time as well as mountain regions such as the Eastern Arcs in Tanzania, the Cameroonian Volcanic Range in Cameroon and the Crystal mountains in Gabon. The map also reveals areas that have been prospected only recently (shades of red in Fig. 5) such as the north of Benin, the Republic of Congo and the Haut-Ogooué region in Gabon. Some large areas that are still to be botanically explored (white
<figure><img src="image_5.png" /></figure>

in Fig. 5) are present along the southern margin of the Sahara, in eastern Chad, Sudan and South Sudan, northern and eastern Angola, and parts of the Ogaden desert in Ethiopia.

## Tropical African turnover rates

High large-scale turnover rates (low halving distance values, Fig. 6a) are present in areas with high topographical heterogeneity, for example, along the Cameroonian Volcanic Range, the Albertine Rift or around the Nimba

Mountains area, and forest-savannas transitional areas. The meso-scale turnover map (Fig. 6b) shows broadly similar areas, but also some striking differences. Some additional heterogeneous areas are identified, such as the regions around Libreville in Gabon and around Kribi and Bipindi in Cameroon, while others become homogeneous at the large scale.

## Botanical exploration intensity

The observed number of species and level of exploration of a selection of countries are provided in Table 1. The estimated botanically best-explored countries (Table 1, relative exploration index ≥ 0.85) are Cameroon, Benin, Gabon, Liberia and Ivory Coast. When looking at the sampling completeness, DRC and Tanzania can be added to this list (Table 1). The botanically least explored countries (relative exploration index ≤ 0.65) are Angola, Somalia, Botswana, Republic of Congo, Guinea-Bissau and Zimbabwe.

Our results show that big portions of tropical Africa remain poorly explored, which is illustrated by the numerous large grey coloured SUs (areas that do not attain our threshold values for adequate botanical exploration; Fig. 7) located in, for example, Angola, DRC or Nigeria. Only 34 variable sized SUs can be qualified as being 'well sampled' (density above 100 records/100 km² and coverage over 0.5 WSUs). These are mainly concentrated in Cameroon, Gabon and Equatorial Guinea. The TSUs are mainly located in Atlantic central Africa (Cameroon, Gabon and Equatorial Guinea), parts of Tanzania, parts of the Kivu region, and south Benin. Finally, we highlight several regions as PSUs, mainly occurring in regions in West Africa, along the Cameroon Volcanic Line, eastern Gabon, and the Eastern Arcs and coastal forests of Tanzania. It is important to underline that this does not

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 5** Time lapse of botanical collecting history across tropical Africa. The map represents the date of the first botanical collection made within each 0.5° sampling unit. Dashed lines represent the limits for tropical Africa as defined in our study. Map based on georeferenced herbarium records, silica gel samples and plot data. An animated gif version of this map is available at: http://rainbio.cesab.org</figcaption></figure>

mean that other areas are not a priority, but simply indicates that, based on our data, the exploration of these SUs is more likely to improve our knowledge about tropical African plant biodiversity.

## Growth form information

Growth form data were recorded for 21,901 species or 97% of the filtered species list of RAINBIO. Our results suggest that the number of herbaceous species is around the same as the number of woody species (Table 2). The geographic distribution of plant growth form types across tropical Africa in terms of the proportion of species of a certain type shows contrasting patterns (Fig. 8). The herbaceous growth form has a high proportion in drier regions (Sahel, East Africa) where savannah prevails (Fig. 8a). In contrast, the tree and liana growth forms have high ratios within the rain forest regions where climate seasonality is lowest (Fig. 8c, d). Shrubs recorded average proportions in the Sahel region and high proportions in the horn of Africa region, for example, Somalia and northern Kenya (Fig. 8b). Finally, epiphytes recorded high proportions in São Tomé and Príncipe, and montane areas such as the Cameroon Volcanic Line, Crystal Mountains area in northwest Gabon or Nimba Mountains in Liberia (Fig. 8e).

## African tropical forest diversity

When limited to tropical African forests (Additional file 3), RAINBIO records relate to a total of 15,387 vascular plant species, of which 3013 are scored as trees, 5755 as herbs, 1637 as lianas and 3158 as shrubs (Table 3). As expected, the central African forests represent the most species rich block with 10,306 species, followed by the east African forests with 6789 species and West Africa with 4396 species.

The endemism rate for Central African forests is 29.1% (2997 out of 10,306 species endemic), 7.4% for east African forests (504 out of 6789 species) and 11.4% West African forests (503 out of 4396 species). The top 20 most species-rich families found in tropical African forests are provided in Table 4.

## Discussion

### The RAINBIO mega-database

The basis of the current analyses relies on data available through the RAINBIO mega-database [33]. As indicated in the methods section, we suggest that RAINBIO is the largest and most accurate database for tropical African plant distributions, as we undertook numerous data validation routines, automatic as well as manual via numerous African flora taxonomic experts. Recently, another dataset has been published [51], comprising 3.1 million records for 40,401 vascular plant species across the whole of Africa. However, this dataset was assembled to test a new method of identifying areas of important biodiversity conservation (Star ratings) and has not been specifically used to explore tropical African plant biodiversity.

We stress that, like all existing biodiversity databases, RAINBIO is not perfect. First, specific data associated with specimens can be erroneous, for example, misidentifications or errors in geo-referencing. Nevertheless, by having expert taxonomists validate large parts of the identifications and by treating information from duplicated herbarium specimens in a systematic manner (see recommendations section), we significantly limited errors and improved the overall quality of RAINBIO. Second, several important herbaria were not directly included such as the Muséum National d'Histoire Naturelle (P), the Conservatoire et Jardin botaniques, Geneva (G), the East African
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 6** Floristic turnover rates across tropical Africa. Values based on adaptive resolution sampling unit (explanation see text). Pairwise floristic similarity is computed as 1-βsim turnover index using two values of μ (see section in Methods and Additional file 1). **a** Meso-scale floristic turnover rate with μ = 1°; **b** Large-scale turnover rate with μ = 2°. Dashed lines represent the limits for tropical Africa as defined in our study. Maps based on georeferenced herbarium records, silica gel samples and plot data</figcaption></figure>

herbarium (EA) and partly the Royal Botanical Gardens, Kew (K) (acronyms follow, [11]). In general, specimens within those herbaria have not yet been databased or were not available at the time of this project. The impact of missing data has yet to be explored and could affect the results in various regions such as East Africa (EA, K) or West/Central Africa (P). However, it is important to underline that (1) a significant part of the data within these herbaria are indirectly available in RAINBIO (via duplicates distributed to other herbaria or via specimen databased with the framework of regional/country floras (e.g. [52, 53]) or monographic revisions, information of which is included in RAINBIO), and that (2) our paper explores general tropical African plant biodiversity patterns for which RAINBIO provides a sound representation of all data.

## The floristic diversity of tropical Africa

Botanical records across tropical Africa are well distributed but highly heterogeneous (Fig. 1a). Continental or regional scale biodiversity data are typically distributed in an uneven and patchy way [20, 24, 40], related to differences in accessibility (presence of roads or rivers), focus of researchers, projects [54] or to colonial history. In tropical Africa, areas of higher record density are partly explained by focused collecting and databasing efforts of the main contributing herbarium institutions (acronyms follow [11]): BRLU for Equatorial Guinea, São Tomé and Príncipe and Gabon; MO for the Eastern Arc Mountains in Tanzania and Gabon; WAG for West Africa, Cameroon, Gabon and Ethiopia; BR for western DRC and the Kivu-Rwanda-Burundi region; and K for western Cameroon. In contrast, low record density is
<table><caption>**Table 1** Floristic diversity parameters and exploration level per country</caption><thead><tr><th>Country</th><th>Observed species richness</th><th>Estimated species richness (Chao1)</th><th>Number of records</th><th>Record density/ 100 km²</th><th>Endemism %</th><th>Relative exploration</th><th>Degree of sampling completeness (from 0-1)</th></tr></thead><tbody><tr><td>Angola</td><td>2262</td><td>4310 (4232-4388)</td><td>5439</td><td>0.44</td><td>18.8</td><td>0.51</td><td>0.76</td></tr><tr><td>Somalia</td><td>1267</td><td>2329 (2277-2381)</td><td>2892</td><td>0.45</td><td>32</td><td>0.54</td><td>0.75</td></tr><tr><td>Botswana</td><td>920</td><td>1378 (1342-1413)</td><td>2368</td><td>0.41</td><td>4.7</td><td>0.56</td><td>0.79</td></tr><tr><td>Republic of Congo</td><td>2403</td><td>3795 (3741-3848)</td><td>6439</td><td>1.88</td><td>1.5</td><td>0.63</td><td>0.82</td></tr><tr><td>Guinea-Bissau</td><td>841</td><td>1329 (1298-1360)</td><td>1910</td><td>5.29</td><td>0.8</td><td>0.64</td><td>0.78</td></tr><tr><td>Zimbabwe</td><td>2807</td><td>4165 (4113-4217)</td><td>8104</td><td>2.07</td><td>7.6</td><td>0.65</td><td>0.84</td></tr><tr><td>Burkina Faso</td><td>879</td><td>1309 (1280-1338)</td><td>2718</td><td>1.00</td><td>0.6</td><td>0.67</td><td>0.86</td></tr><tr><td>Zambia</td><td>3309</td><td>4887 (4831-4942)</td><td>11,048</td><td>1.47</td><td>7.2</td><td>0.68</td><td>0.87</td></tr><tr><td>Uganda</td><td>2258</td><td>3303 (3258-3348)</td><td>6818</td><td>2.83</td><td>2.8</td><td>0.68</td><td>0.86</td></tr><tr><td>Mali</td><td>903</td><td>1309 (1282-1336)</td><td>2550</td><td>0.21</td><td>1.4</td><td>0.69</td><td>0.84</td></tr><tr><td>Senegal</td><td>1342</td><td>1921 (1888-1953)</td><td>4294</td><td>2.18</td><td>4.5</td><td>0.70</td><td>0.87</td></tr><tr><td>Guinea</td><td>2533</td><td>3583 (3537-3630)</td><td>11,082</td><td>4.51</td><td>4.7</td><td>0.70</td><td>0.92</td></tr><tr><td>Rwanda</td><td>1883</td><td>2608 (2572-2644)</td><td>7740</td><td>29.39</td><td>3</td><td>0.72</td><td>0.91</td></tr><tr><td>Central African Republic</td><td>2560</td><td>3463 (3423-3503)</td><td>11,282</td><td>1.81</td><td>2.5</td><td>0.73</td><td>0.92</td></tr><tr><td>Sierra Leone</td><td>1883</td><td>2513 (2482-2545)</td><td>6470</td><td>8.95</td><td>2.3</td><td>0.75</td><td>0.89</td></tr><tr><td>Nigeria</td><td>3378</td><td>4487 (4443-4530)</td><td>14,907</td><td>1.61</td><td>2.2</td><td>0.75</td><td>0.92</td></tr><tr><td>Malawi</td><td>3340</td><td>4371 (4330-4413)</td><td>12,410</td><td>10.47</td><td>6.5</td><td>0.76</td><td>0.91</td></tr><tr><td>Mozambique</td><td>4095</td><td>5264 (5220-5309)</td><td>23,181</td><td>2.89</td><td>8.4</td><td>0.77</td><td>0.94</td></tr><tr><td>Burundi</td><td>2788</td><td>3556 (3521-3591)</td><td>12,118</td><td>43.54</td><td>2.9</td><td>0.78</td><td>0.93</td></tr><tr><td>Ethiopia</td><td>4481</td><td>5627 (5581-5672)</td><td>31,795</td><td>2.88</td><td>19.9</td><td>0.79</td><td>0.96</td></tr><tr><td>Equatorial Guinea</td><td>3049</td><td>3821 (3785-3856)</td><td>15,341</td><td>54.69</td><td>1.8</td><td>0.80</td><td>0.94</td></tr><tr><td>Kenya</td><td>4759</td><td>5948 (5904-5991)</td><td>28,223</td><td>4.86</td><td>11.5</td><td>0.80</td><td>0.95</td></tr><tr><td>São Tomé and Príncipe</td><td>806</td><td>999 (982-1016)</td><td>3598</td><td>373.24</td><td>12</td><td>0.80</td><td>0.94</td></tr><tr><td>Ghana</td><td>2971</td><td>3634 (3601-3667)</td><td>14,428</td><td>6.05</td><td>1.5</td><td>0.81</td><td>0.94</td></tr><tr><td>Dem. Rep. Congo</td><td>8860</td><td>10,872 (10,814-10,931)</td><td>111,179</td><td>4.74</td><td>18.3</td><td>0.81</td><td>0.98</td></tr><tr><td>Tanzania</td><td>8727</td><td>10,550 (10,496-10,605)</td><td>82,850</td><td>8.75</td><td>19.4</td><td>0.82</td><td>0.98</td></tr><tr><td>Ivory Coast</td><td>3689</td><td>4344 (4311-4377)</td><td>41,666</td><td>12.92</td><td>2.6</td><td>0.85</td><td>0.98</td></tr><tr><td>Liberia</td><td>2403</td><td>2806 (2781-2830)</td><td>18,299</td><td>16.43</td><td>3.8</td><td>0.86</td><td>0.97</td></tr><tr><td>Gabon</td><td>5236</td><td>6106 (6068-6144)</td><td>93,828</td><td>35.05</td><td>10.5</td><td>0.86</td><td>0.99</td></tr><tr><td>Benin</td><td>2460</td><td>2864 (2840-2889)</td><td>21,914</td><td>19.10</td><td>1.6</td><td>0.86</td><td>0.98</td></tr><tr><td>Cameroon</td><td>6883</td><td>8015 (7972-8057)</td><td>90,222</td><td>18.98</td><td>9.3</td><td>0.86</td><td>0.99</td></tr></tbody></table>

Countries are ordered from least to best botanically explored. Values calculated based on georeferenced and non-georeferenced (when the country was indicated) specimens

not only due to low collecting efforts (Central African Republic, Republic of Congo or Angola) but also to incomplete digitization and georeferencing of existing records, for example, in Angola [55] and DRC [56].

For tropical Africa, we record occurrences for 22,577 species, 2810 genera and 258 families of vascular plants. These values are lower than previous estimates for the region. Indeed, Klopper et al. [2] recorded a total of 32,424 angiosperm taxa, while Govaerts [57] estimated around 29,887 species for the same region. However, these values are not entirely comparable. First, they are

based on databases (checklists) of published names (e.g. African Plant Checklist and Database Project; Kew World Checklist) rather than from herbarium specimens. Due to new insights in taxonomy, a significant number of those names have been marked as synonyms in our list, although we have also added many newly published species names. Second, Klopper et al.'s [2] values are for taxa (thus including infraspecific names) and for angiosperms only, while Govaerts' [57] are restricted to species and seed plants (gymnosperms and angiosperms). Our values are for species and all vascular
<figure><img src="image_10.png" /><figcaption>**Fig. 7** Level of botanical exploration across tropical Africa. Based on an adaptive resolution sampling units (SUs). This map shows priority SUs calculated based on a turnover rate using μ = 1°. Grey SUs represent SUs that did not meet any of our threshold limits (see text for explanation) and thus highlight SUs that are poorly documented. Dashed lines represent the limits for tropical Africa as defined in our study. Map based on georeferenced herbarium records, silica gel samples and plot data</figcaption></figure>

plants (pteridophytes, gymnosperms and angiosperms), and based on herbarium specimens complemented with silica gel data and plot inventories. Most dubious names or names of doubtful taxonomic status still present in checklists do not have any specimens linked to them in herbarium databases.

Several species-rich areas across tropical Africa (Fig. 2a) are highlighted based on the Chao1 estimator (but see below), namely the Cameroon/Gabon forests, including Mont Cameroon, the east Kivu region and the Eastern Arc Mountains in Tanzania, with a less pronounced diversity centred in Katanga (south-eastern DRC). These results are consistent with those of several previous studies [30–32], but our results are derived

<table><caption>**Table 2** Distribution of plant species in the RAINBIO database across growth form types</caption><thead><tr><th></th><th>Growth form</th><th># species</th><th>% of total</th></tr></thead><tbody><tr><td rowspan="4">Herbaceous</td><td>Herb</td><td>9818</td><td rowspan="4">46.3</td></tr><tr><td>Vine</td><td>493</td></tr><tr><td>Aquatic</td><td>111</td></tr><tr><td>Mycoheterotroph</td><td>27</td></tr><tr><td rowspan="3">Woody</td><td>Tree</td><td>3601</td><td rowspan="3">45.7</td></tr><tr><td>Shrub</td><td>4956</td></tr><tr><td>Liana</td><td>1756</td></tr><tr><td rowspan="4">Herbaceous/woody</td><td>Epiphyte</td><td>878</td><td rowspan="4">5.0</td></tr><tr><td>Parasitic</td><td>261</td></tr><tr><td>Unknown</td><td>676</td></tr><tr><td>Total</td><td>25,356</td></tr></tbody></table>

from a significantly larger dataset. Larger areas with high species and specimen records can be linked to specific plant inventory efforts, for example, in most of Gabon [52], the Cameroon Volcanic Line in western Cameroon [58–60], Benin [53] and the Eastern Arc mountains [61]. Some small-scale instances of high species richness (isolated red SUs in Fig. 2a) can be either attributed to single-person sampling campaigns (e.g. in south-western Tanzania, where A.F. Stolz (1871–1917) made ca. 2500 plant collections; in south-western DRC, where Masens Da Musa generally collected each species only once [62]; or in western Ghana, with specimens mainly collected by C. Jongkind) or by focused sampling and digitization efforts for the area, namely Yangambi (North-central DRC), where intense field work was undertaken by Belgian collectors combined with the digitization of the BR type specimens and large parts of the Yangambi general collections [63]; or the Nimba Mountains area (border between Liberia, Ivory Coast and Guinea), well documented by Adam [64], with more recent records from MO and WAG contained in RAINBIO.

The estimated species richness per SU based on Chao1 (Fig. 2a) is highly correlated to both the number of records (Fig. 1a) and the observed species richness (Fig. 1b) per SU. This is a well-known artefact of biodiversity analyses [39, 40] and is likely biased by the heterogeneous sampling effort. Part of the correlations can also be explained by collector's behaviour, tending to collect in areas of known high species richness [24, 54]. The Nielsen diversity estimator was proposed to correct for such heterogeneous sampling effort [39]. When computed for
<figure><img src="image_11.png" /></figure>

each 0.5° and 1° SUs (Fig. 2b), this effective number of
species is much less correlated with specimen density
(Pearson correlation R = 0.28). Despite that high species
diversity is confirmed in Lower Guinea and the Eastern
Arc Mountains, there are some discrepancies between
richness patterns shown by the Chao1 and Nielsen esti-
mators. For example, estimated species richness based
on Chao1 in the Kivu and Katanga regions of DRC and

several areas in Lower Guinea are similar, while this pat-
tern is not observed when using the Nielsen estimator.
This suggests the Chao1 does indeed more strongly re-
flect the heterogeneous sample size rather than the true
species diversity. Other differences are the high effective
number of species observed in several areas in Ethiopia
and West Africa, localities that were not emphasised by
the Chao1; it is likely that these are artefacts in the
<table><caption>**Table 3** Growth form diversity across tropical African forests</caption><thead><tr><th></th><th></th><th>Tropical African forests</th><th>West African forests</th><th>Central African forests</th><th>East African forests</th></tr></thead><tbody><tr><td rowspan="3">All growth forms</td><td>Total # records</td><td>383,414</td><td>72,753</td><td>260,695</td><td>46,711</td></tr><tr><td>Observed # of sp.</td><td>15,387</td><td>4396</td><td>10,306</td><td>6789</td></tr><tr><td># endemic sp.</td><td>4544</td><td>503</td><td>2997</td><td>504</td></tr><tr><td rowspan="3">Tree</td><td>Total # records</td><td>122,555</td><td>19,515</td><td>92,177</td><td>10,283</td></tr><tr><td>Observed # of sp.</td><td>3013</td><td>892</td><td>2264</td><td>1055</td></tr><tr><td># endemic sp.</td><td>1108</td><td>119</td><td>803</td><td>61</td></tr><tr><td rowspan="3">Herb</td><td>Total # records</td><td>97,835</td><td>19,604</td><td>60,251</td><td>16,621</td></tr><tr><td>Observed # of sp.</td><td>5755</td><td>1773</td><td>3510</td><td>3277</td></tr><tr><td># endemic sp.</td><td>1078</td><td>137</td><td>560</td><td>247</td></tr><tr><td rowspan="3">Liana</td><td>Total # records</td><td>52,546</td><td>14,630</td><td>35,039</td><td>2458</td></tr><tr><td>Observed # of sp.</td><td>1637</td><td>624</td><td>1384</td><td>354</td></tr><tr><td># endemic sp.</td><td>610</td><td>77</td><td>422</td><td>13</td></tr><tr><td rowspan="3">Shrub</td><td>Total # records</td><td>84,399</td><td>14,904</td><td>55,697</td><td>13,110</td></tr><tr><td>Observed # of sp.</td><td>3158</td><td>664</td><td>1966</td><td>1390</td></tr><tr><td># endemic sp.</td><td>999</td><td>106</td><td>718</td><td>93</td></tr></tbody></table>

The table indicates the total number of records, total number of species and endemics observed for tropical African forests, and West, Central and East African forests separately, for all species and per major growth form type

<table><caption>**Table 4** List of top 20 most species-rich plant families and genera recorded in tropical African forests</caption><thead><tr><th>Family</th><th>Total # species</th><th>Genus</th><th>Total # species</th></tr></thead><tbody><tr><td>Rubiaceae</td><td>1698</td><td>Psychotria</td><td>251</td></tr><tr><td>Fabaceae</td><td>1590</td><td>Cyperus</td><td>179</td></tr><tr><td>Orchidaceae</td><td>928</td><td>Polystachya</td><td>156</td></tr><tr><td>Asteraceae</td><td>753</td><td>Crotalaria</td><td>154</td></tr><tr><td>Poaceae</td><td>722</td><td>Pavetta</td><td>145</td></tr><tr><td>Acanthaceae</td><td>526</td><td>Vernonia</td><td>115</td></tr><tr><td>Cyperaceae</td><td>443</td><td>Combretum</td><td>108</td></tr><tr><td>Apocynaceae</td><td>430</td><td>Begonia</td><td>107</td></tr><tr><td>Malvaceae</td><td>413</td><td>Impatiens</td><td>102</td></tr><tr><td>Euphorbiaceae</td><td>402</td><td>Indigofera</td><td>102</td></tr><tr><td>Lamiaceae</td><td>367</td><td>Euphorbia</td><td>101</td></tr><tr><td>Annonaceae</td><td>306</td><td>Rinorea</td><td>99</td></tr><tr><td>Melastomataceae</td><td>231</td><td>Habenaria</td><td>92</td></tr><tr><td>Phyllanthaceae</td><td>190</td><td>Cola</td><td>91</td></tr><tr><td>Sapindaceae</td><td>176</td><td>Dichapetalum</td><td>89</td></tr><tr><td>Celastraceae</td><td>167</td><td>Justicia</td><td>88</td></tr><tr><td>Sapotaceae</td><td>164</td><td>Asplenium</td><td>82</td></tr><tr><td>Asparagaceae</td><td>159</td><td>Millettia</td><td>82</td></tr><tr><td>Convolvulaceae</td><td>148</td><td>Bulbophyllum</td><td>80</td></tr><tr><td>Polypodiaceae</td><td>141</td><td>Phyllanthus</td><td>79</td></tr></tbody></table>

The families and genera are ordered from largest to smallest

Nielsen estimator. For example, the very high effective number of species detected in western Ghana (red SU, Fig. 2b) is probably due to the intense collections of a single collector (C. Jongkind) in that area. Large parts of Gabon yield a very low Nielsen score, which must be a flaw in the Nielsen estimator, since several SUs were identified as well as top sampled units (WSU or TSU; Fig. 7), and our data (Fig. 1b) confirm they are very species rich. It is nevertheless important to highlight that the Nielsen and Chao1 estimators are not fully adequate for herbarium specimens because they rely on the relative abundance of species within each SU (even if only singletons and doubletons are taken into account for Chao1 estimator), which is estimated by the number of specimens per species. However, the number of specimens per species is not a good proxy of the species' population size [54]. Indeed, a collector generally does not collect plants at random, but usually tends to collect as many different species as possible. This would explain the unexpected high values of Nielsen estimator and Chao1 observed in SUs where many specimens were collected by a single or very few collectors. The SU in western Ghana with mainly collections by C. Jongkind does give an artefact in Nielsen, but not in Chao1, indicating that such collector-biases are not dealt with in the same way.

## The diversity of tropical African forests

Our study documents a total of 15,387 vascular plant species occurring in tropical African forests (excluding Madagascar) of which ca. 30% are strict endemics (Table 3). To date, only rough estimates have been
published, and these have generally not been restricted to forested regions but rather to certain major biogeons such as phytochoria [3, 31, 65]. White [26] estimated a total of 8000 plant species occurring in the Guineo-Congolian regional centre of endemism, 80% of which were endemics. This centre of endemism concerns West and Central Africa and encompasses species occurring in other ecosystems than forests such as savannahs and mountains. The values advanced by White [65] are based mainly on his expert opinion with little underlying quantitative information.

Adding to previous estimates of the tropical tree flora [20, 27], our results suggest a total of 3013 tree species recorded for African forests (36% strict endemics). This estimate is below the recently estimated minimum number of 4626–5984 trees for Africa [27], but it provides, for the first time, a number based on solid underlying quantitative biodiversity data. However, the values of Slik et al. [27] include Madagascar and are based on forest plots where a tree is identified as having a diameter above breast height (DBH) of more than 10 cm. Recent studies have underlined that a significant portion of tree species diversity in African forests have a DBH smaller than 10 cm [66, 67]. In our analyses, a species was considered a tree if explicitly mentioned on the specimen labels or coded as such [33], thus also includes species with DBHs smaller than 10 cm.

Our results confirm the observed ‘odd man out’ pattern [29] of relatively lower diversity of African forest tree species when compared to the Neotropics or South-East Asia [20, 27, 28, 68]. For example, there are 11,676 tree species (DBH > 10 cm) recorded for the Amazon basin, more than three and a half times our figure for tropical Africa. Overall, the tropical African forest tree flora represents between 5% and 7.5% of the estimated total number of tree species for the whole of the tropics (between 40,000 and 53,000 tropical tree species, [26]). The numbers documented here, though likely underestimates, provide for the first time values for plant diversity in tropical African forests. As indicated above, significant discrepancies between ‘true’ floral diversity and estimated/counted number of species in a given region is a common problem in the tropics, as rare species will be hard to find or document [20].

## Country-level diversity patterns

We provide, for the first time, basic plant biodiversity values for several countries across tropical Africa (Table 1). These values are based on the available data within RAINBIO, and thus should not be taken as definite. Nevertheless, they provide important insights into the plant diversity of these countries as well as levels of exploration. For some countries, these values are quite close to previous estimates. For example, Onana [69] recorded a total of 7850 vascular

plant species for Cameroon, whereas we report 6883 species with an estimated total species richness of 8015. The higher recorded total species number of Onana [69] could be related to synonymy not taken into account during that study (Onana personal communication). Another example is for São Tomé and Príncipe [70], where a total of 803 native flowering plant species (excluding 301 introduced species) plus a single endemic gymnosperm species have been recorded. This value closely matches our estimates (Table 1), which could reflect the inclusion of the vast majority of São Tomé and Príncipe records from herbaria such as BRLU and LISC in our study [33].

Our study identifies several botanically ‘best-explored’ countries with Cameroon, Benin and Gabon in the top three (Table 1). For some of those countries (i.e. Benin, Gabon and Liberia) a large percentage of existing specimen data (over 90%) is available in RAINBIO and thus could reflect values close to reality. Even for these countries, the average specimen density is well below the threshold of 100 records/100 km² (19 for Benin, 35 for Gabon and 16 for Liberia; Table 1). In addition, the estimated discover rate (percentage of species yet to be documented based on our estimate of total species richness) varies between 14% and 19% for the top seven countries (Table 1). Thus, even for the top best-explored countries new species are expected to be described or have already been collected but not yet identified. Data from the International Plant Name Index (IPNI, www.ipni.org) for the period 2000 to 2015 reveals that 8, 162 and 38 new species were described from Benin, Gabon and Liberia, respectively (0.3%, 2.7% and 1.5% of their estimated species richness, respectively). For Gabon, 4710 species were recorded in 2005 [52], meaning that no less than 526 (10%) new species or new species records have been added these past 11 years (5236 species recorded in RAINBIO). New species and even new genera are still regularly described for Gabon, even in well sampled areas [71] such as the Crystal Mountains National Park [67, 72–77]. Interestingly, São Tomé and Príncipe show the highest concentration of records (375/100 km², Table 1) yet is not amongst the best-explored countries in our study. This could be linked to the relatively high level of singletons when compared to the other most “well-explored” countries, with 233 species known from a single record out of 3598 total records (6.5%, Table 1). The Chao and Jost [45] estimator for the degree of sampling completeness adds DRC and Tanzania to the list of best-explored countries (Table 1). However, this seems counterintuitive when looking at the high number of singletons for those countries and the difference between the observed and estimated number of species (Table 1). This can be explained by the high number of specimens available for these two countries, which reduces the effect of the high number of singletons. Importantly, our results
underline that even though these countries might appear
well explored, there are still lots of efforts to provide in
terms of botanical exploration and databasing. This is a
common situation across the tropics, for example, in
South America [20], where for even some of the best
collected countries such as Ecuador, significant collecting
efforts remain to be done in order to fully record its floral
diversity [40]. To date, as far as we know, no tropical
country (except Singapore) has yet claimed it should be
considered as being botanically well-known.

In contrast, for some countries our data is far from
complete as underlined by the low values of degree of
completeness such as Angola, Somalia, Botswana,
Republic of Congo, Guinea-Bissau and Zimbabwe. For
some countries, the low value is explained by a lack of
available data in digital format. For example, Figueiredo
et al. [55] mentioned the occurrence of 6735 species of
vascular plants in Angola, whereas RAINBIO records
only 2262 species (Table 1). In the same way, the Repub-
lic of Congo is suggested to contain 4538 vascular plant
species [78, 79], whereas our dataset records just 2403
(Table 1). Despite this basic check-list, the Republic of
Congo remains botanically one of the least known coun-
tries in tropical Africa [79]; a result that is also confirmed
here (Table 1).

Botanical exploration of tropical Africa

A vital question in biodiversity inventories has been to
evaluate the level of botanical exploration across the tro-
pics. Campbell and Hammond [46] suggested that a
minimal level of botanical exploration of a tropical re-
gion should be at least 100 specimens per 100 km². Our
data indicate that just 21 of the 0.5° SUs reach this
threshold (Additional file 4, 0.5° represents a surface of
about 3000 km² around the equator, thus 3000 speci-
mens per SU). In addition, we find that just 34 variable
sized SUs (Fig. 7) are well sampled (defined as SUs with
record density higher than 100 records/100 km² and
C<sub>N</sub> > 0.5). This is much less than the number identified
by Stropp et al. [25] based on the analysis of GBIF data
(1002 SUs of 0.25° with more than 200 records). How-
ever, most of these SUs were located in South Africa, a
region not included in our study. Moreover, this differ-
ence could be linked to the data (RAINBIO versus GBIF,
although RAINBIO contains more data for tropical
Africa) or the difference in our definitions of well-
sampled SUs (larger SUs (0.5° versus 0.25°), different
density thresholds and a different method to calculate
the sampling coverage, C<sub>N</sub>). In addition, we defined
TSUs, which highlight the 25% SUs with the best data
available when compared to the others (Fig. 7). TSUs are
different from WSUσ in that they show SUs where most
botanical exploration has occurred relative to others.
Finally, the need for additional botanical exploration is

also reflected by the very high number of species known
from less than five records (Additional file 5a) or occurring
in a single SU (Additional file 5b); this is a characteristic
outcome of tropical biodiversity inventories [52, 80, 81].
For our dataset, this can partly be explained by the targeted
scanning of type specimens within the Global Plant Initia-
tive ([63], https://plants.jstor.org), artificially increasing the
number of species known from a single collection. Still,
overall, our results confirm that tropical Africa remains se-
verely under sampled [24, 25], even when using the largest
homogenised dataset ever complied for the region to date
[33]. Similar results are reported for other tropical regions
such as the Amazon basin [20] or countries such as
Ecuador [40].

In a world of limited resources, an important question
is how to identify priority sampling units [24], e.g. SUs
where we could expect to significantly add to our know-
ledge of the African flora. This is not a trivial question,
as potentially any SU across Africa deserves more in
depth exploration. Several past studies have identified
data-deficient areas using different methods. Küper et al.
[24] qualified data-deficient areas as the difference or
the ratio between predicted species richness (based on
species distribution modelling of ca. 5000 species) and
the documented species richness. Stropp et al. [25] iden-
tified areas of ‘acute data deficiency’ as areas that maxi-
mise the distance between well-sampled SUs based on
the assumption that floristic similarity decreases with
distance. Both these approaches are valid in their own
right, underlining regions either containing potentially
many uncollected species or regions that maximise the
collection of new records for a whole region, respect-
ively. Here, we used a concept similar to Stropp et al.
[25] to identify a set of PSUs. Indeed, floristic similarity
does decrease with distance; however, this relationship is
not linear, as certain regions will experience higher
floristic turnover rates than others (Fig. 6) [32]. We sug-
gest that regions of higher floristic turnover should be
more thoroughly explored as they will potentially un-
cover higher levels of botanical novelties. We defined a
PSU as being an SU with an estimated high turnover
rate relative to other neighbouring SUs associated with a
low sampling coverage. High floristic turnover rates were
identified (Fig. 6) mainly in montane areas (Cameroon
Volcanic Line, Nimba Mountains, Eastern Arcs) and
areas of vegetation transitions (e.g. coastal regions in
West, Central and East Africa, Katanga in south-eastern
DRC, Haut-Ogooué in Gabon). Not all coastal regions
are identified as having a high turnover (e.g. Liberia), but
the high turnover observed along the southern coast of
Gabon is corroborated by Harris et al. [82]. Our sug-
gested PSUs (Fig. 7) highlight very different areas than
those suggested by Stropp et al. [25] and agree more
with the priority areas suggested by Küper et al. [24].
Indeed, we identify PSUs, for example, in Guinea (Nimba Mountains), Cameroon (the Volcanic Line area above Mount Cameroon and the northern part of Korup National Park), Gabon (Haut-Ogooué), and the Eastern Arc Mountains and coastal forests in Tanzania. Areas of low estimated floristic turnover (Fig. 6) [32], such as the Congo Basin, do not contain any PSUs, in contrast to Stropp et al. [25]. Even though one could criticise our approach as it relies on the estimation of turnover rates, we applied a variable SU size approach which allows comparison of SUs based on similar amount of data [35]. All approaches to identify PSUs can be viewed as complementary but focusing on different aspects/priorities of biodiversity exploration.

## Plant growth form dominance across tropical Africa

Data on growth form in the tropics is scarce, but some studies show that the contribution of herbaceous species to tropical forest diversity is between 18% and 44% [83–85]. Our data suggests that 43.8% of species are herbaceous across Tropical Africa. This also underlines that savanna and montane vegetation types are well represented in RAINBIO, despite the initial emphasis of our data towards forested regions. It has been shown that on global or continental scales plant growth form is linked with climatic variables [19, 86, 87]. Although no formal analyses are undertaken here, our data underline general patterns possibly linked to climate (Fig. 8). For example, herb species are dominant in the drier parts of Africa (Senegal, Burkina Faso, Malawi, Tanzania, Zimbabwe) whereas trees and lianas are dominant in countries with significant areas of tropical forests (e.g. Republic of Congo, Gabon, south of Cameroon). In addition, with most of their surface being at higher elevations, Ethiopia, Rwanda and Burundi harbour vegetation with much higher proportions of herbaceous species.

As indicated above, the herbaceous component (Fig. 8a) gives a clear pattern related to the drier regions (Sahel, East Africa) where savanna prevails. Rwanda, Burundi, south-eastern DRC (Katanga), Zambia and Ethiopia, which are dominated by savanna and/or high-land vegetation, also stand out as containing a high proportion of herbaceous species. Interestingly, a large New World dataset for geographical trait patterns did not reveal montane areas (such as the Andes or the Rocky Mountains) as exceptionally dominated by herb species [19]. The moderately high ratio of herb species locally observed on the coast of Ghana and the western tip of Gabon (Port-Gentil) (Fig. 8a) relate to land surface occupied by coastal savanna where the rest of those grid cells are sea, showing that marginal SUs with only little land surface can easily result in seemingly odd results.

Shrub dominance (Fig. 8b) is in general more equally distributed than herb, liana and tree dominance; shrubs

can occur in nearly all vegetation types. The Eastern Africa Coastal Forest ecoregion is strikingly dominated by shrubs, known for its mosaic of forest, savannas and wetlands [88–90]. High shrub dominance is also observed in the Horn of Africa. The low proportion of shrub species observed in montane areas is not corroborated by a study on New World plants [19].

Liana dominance (Fig. 8c) shows a very clear pattern, being high within the major rain forest block (the Guineo-Congolian region). In addition, lianas form a high proportion of growth forms in the semi-deciduous forests of south-eastern Cameroun and south-eastern Gabon, as well as in the highly fragmented forests of Upper Guinea [91], while the wettest forests in coastal Gabon, Cameroon and Liberia contain proportionally fewer liana species. This might be explained by the wettest forests having a closed canopy all year round, rendering the understory very dark, while in the drier forests, with some of the trees being semi-deciduous, the forest floor will have some periods with more light, enabling liana seedlings to gain the energy to climb to the canopy. In contrast to west and central Africa, the drier semi-deciduous East African coastal and Eastern Arc forests contain proportionally fewer liana species, which remains unexplained.

The tree component (Fig. 8d) is highest within the rain forest regions where climate seasonality is lowest, which thus is in line with the findings of Engemann et al. [19] for the Neotropics. Gabon, having over 80% of its surface covered by possibly the most species-rich lowland rain forest of Africa [92], shows the highest proportion of tree species and the lowest proportion of herb species. Interestingly, similar high dominance in tree species is observed in northern Mozambique and south-eastern Tanzania. Recent collections from that area were made in the framework of the tree flora of Mozambique [93], creating a potential bias towards tree records. The Katanga region (south-eastern DRC) comes out as an area with an extremely poor tree species component.

Overall, the proportion of epiphytes (Fig. 8e) is highest in montane areas such as Nimba Mountains (northern Liberia), Mount Cameroon and the Bamenda Highlands (Western Cameroon), Crystal Mountains (north-west Gabon), Monte Alen (Rio Muni), the Albertine Rift region and the Eastern Arc Mountains. Orchids, which constitute 73% of all epiphytes species recorded in RAINBIO, having better dispersal abilities than most other groups, would have been favoured by elevation and humidity gradients in mountainous areas [94, 95]. The exceptionally high proportion of epiphytes observed on the island of Príncipe (up to 30% of species) is probably biased by the activities of several collectors with a strong interest in orchids [96]. Certainly, many more areas are actually under-collected for epiphytes, which are generally difficult to reach in the canopy.
source of open data for species distribution
information, it has been shown that data quality
and reliability may be low, especially in the
tropics [104, 105]. When compiling specimen
data from different sources, one is faced with the
'duplicate problem', since plant specimens have
several duplicates distributed to different herbaria
some are prone to having different identifications and
databasing quality, complicating the concatenation of
data. Estimates suggest that just under 10% of all
records in GBIF are potential duplicates [106]. Taking
into account duplicates when compiling data from
different institutes has yet to meet a good automatic
solution and has rarely been done in large plant
datasets. In the case of RAINBIO, different datasets
were identified as 'expert taxonomic benchmarks', i.e.
datasets verified by specialists for a given family,
against which all duplicates were compared too and
updated. We identified 11% of records with at least
one duplicate. Similar approaches to deal with this
problem have been undertaken, but at smaller scales
(e.g. [105]). In order to maximise the usefulness of
botanical biodiversity data more efforts should focus
on resolving this limitation and finding appropriate
automatic ways of taking duplicate information into
account, especially from important online repositories
such as GBIF that contain potentially high levels of
duplicated information. Thus, besides past and
ongoing major efforts to independently database
herbarium specimens and upload them to GBIF,
synthesising these datasets must remain a priority.

## 2. Improve data reliability

Separate, but related to the above point, the
reliability of the data is important as it will directly
influence the outcome of analyses [12, 20, 104,
105]. It is well known and understood that natural
history specimen datasets have several potential
errors [20, 54, 107]. Two major sources of errors
can influence the analyses of such data.

First, wrong species identification of specimens will
be an important source of error in any dataset. We
estimate that the identification error rate in an
average herbarium collection can be as high as 10%
or even up to 58% for specific groups [108],
although in the latter figure specimens with a
synonymous but otherwise correct name were also
counted as errors. In a dataset of 4000 individual
trees of the genus *Inga*, 7% of the identifications
proved to be wrong [109]. By comparing an expert
selection of identified duplicates, the identification
accuracy in combined botanical datasets like ours
can be improved. However, misidentifications are
an inherent part of any biodiversity dataset and can
never really be completely eliminated at least

because of changing taxonomic concepts and the
presence of incomplete specimens. The event of
DNA barcoding [110] could provide added value to
accurately identify specimens (from sterile vouchers
to species complexes) although bulk barcoding of
large collections remains expensive and time-
consuming. We must also consider the level of our
taxonomic knowledge for tropical Africa. Every year,
hundreds of new plant species are being described
(globally, on average well over 2000/year according to
data provided by IPNI, www.ipni.org), and new
taxonomic borders for species, genera, etc. are drawn.
Bebber et al. [9] showed that the majority of newly
published species names were based on specimens
collected long before. Thus, a fair number of new
species are awaiting description within one of the
world's herbaria. In the future, our advancing
knowledge will further refine and improve the quality
analyses such as the ones presented above.
In addition, reliability of the data can be improved
by a structured collaboration continuing to work
with taxonomic experts. In the case of RAINBIO,
large numbers of records were indeed checked and
validated by the project taxonomic experts from
numerous families or geographic regions. This
greatly improved the accuracy and quality of
RAINBIO. Misidentification is sometimes
considered as 'background noise' in 'big data'
datasets. Depending on the objectives of the study
this can be misleading, leading to erroneous results
(e.g. when estimating total species number per
country/region; conservation assessments). We
advocate a 'compile and check' rather than a
'compile only' approach. Directly involving expert
plant taxonomists in such large projects will greatly
improve our databases and the subsequent
biodiversity analysis. Second, specimens may be
incorrectly georeferenced, which in turn affects
biodiversity analyses [105]. However, recent tools or
software packages are now available to
automatically improve or correct wrong
georeferencing in big datasets [111, 112]. Checking
georeferencing precision using such methods
should systematically be used in order to improve
the precision of these records.

## 3. Continue the botanical exploration of tropical Africa and digitization of the related specimens

No country in tropical Africa can be regarded as
botanically well explored. Larger areas with no or
limited data are still plentiful. Given the observed
declining trends in collecting efforts (Fig. 4a) [25],
we appeal not only for additional collecting efforts,
but also for increased digitization of tropical
African plant collections. This will depend on the
availability of major funding, mostly at governmental level. In tropical Africa, the major gaps in availability of digital specimen data are in Nigeria, the Central African Republic, South Soudan, the Republic of the Congo and Angola. We believe these to be true gaps, and therefore regions for which comparatively low numbers of specimens have been collected to date. The gap in the availability of data from the DRC should at least partly be overcome soon due to major digitization efforts at BR [56]. Here, we highlight several regions (namely PSUs) we believe would provide a significant amount of new data to our understanding of the tropical African flora in the same spirit as other studies [24, 25].

## Conclusion

The RAINBIO database provides the underlining data to assist in advancing phytogeographical, systematic and ecological research, and enables a wiser sustainable usage and conservation of Africa's unique tropical plant diversity. Only when provided with sound and high quality information on biodiversity distribution, can policymakers take informed decisions about how to manage this fragile resource effectively [8]. Strangely enough, its strength is also in that it shows clear weaknesses in the amount of botanical data available for tropical Africa. To most efficiently fill the gaps and speed up the work, we should investigate how we might benefit from a much larger group of data collectors. The development of citizen science [113], i.e. public involvement in science, has proven its usefulness in a range of biodiversity-related projects (for two recent examples, see [114, 115]). For specific plant groups, field observations made by citizen scientists backed-up with photographs and verified by specialists can add valuable distributional data especially in data-deficient areas (e.g. [115]). These ideas contrast strikingly with the observation of decreasing collecting efforts in the past two decades. In tropical Africa, the paucity of reliable data on a group of organisms as important as vascular plants cries out loud for a renewed and probably unprecedentedly massive botanical exploration of the region.

## Additional files

**Additional file 1:** Two examples of turnover rate calculation. The figure shows two examples to estimate the turnover rate used herein. For $\mu = 1^\circ$ (meso-scale) and $\mu = 2^\circ$ (large-scale). The focal sampling unit (SU) is highlighted in red. A circle of distance $\mu$ is drawn around the red SU. All SUs included in the circle and with record number above 100 are then selected (in grey). In white, non-selected SUs. The geographical distance between all selected SUs is then calculated based on the centroid of the convex hull around the records for each SU (not shown). The pairwise floristic similarity between all selected SUs is then computed as $1-\beta_{sim}$.

The linear relation between the geographical distance and the floristic similarity between all comparisons is computed (line in red). The distance (in kilometres) that halves the initial floristic similarity is calculated (vertical line) and used to define the turnover rate for each SU. (PNG 917 kb)

**Additional file 2:** An R script used to compute Nielsen estimator from a matrix of samples-species. (R 808 bytes)

**Additional file 3:** The distribution of tropical African forests. Map showing the 0.1° sampling units selected as forest for our study based on the map of Mayaux et al. [116]. Light-green: west African forests; deep-green: central African forests; medium-green: east African forests. (PNG 136 kb)

**Additional file 4:** The number of 0.5° sampling units (y-axis) containing a specified number of observations (x-axis). (PNG 12 kb)

**Additional file 5:** Geographic distribution of records across tropical Africa. (a) The number of species known from a particular number of records. (b) Number of species known from a particular number of 0.5° sampling units. (c) Number of species in function of their calculated range size (convex hull). (PNG 1422 kb)

**Additional file 6:** Collecting history per country. Bar plots for a selection of tropical African countries showing the number of records collected in each period of time (5-year intervals). Plots based on herbarium records. (PNG 2657 kb)

**Additional file 7:** Temporal exploration of tropical Africa per decade. The figure shows the newly explored 0.5° sampling units per decade. (PNG 21 kb)

**Additional file 8:** A GIF animated figure of Fig. 5 by 10-year time slices. Also available at http://rainbio.cesab.org/. (GIF 2259 kb)

## Acknowledgments

We thank Alison Specht and three anonymous reviewers for critically reading a previous version of this manuscript.

## Funding

This work was co-funded by the French Foundation for Research on Biodiversity (FRB) and the Provence-Alpes-Côte d'Azur region (PACA) region through the Centre for Synthesis and Analysis of Biodiversity data (CESAB) programme, as part of the RAINBIO research project.

## Availability of data and materials

The RAINBIO dataset used for this study is freely available for download at http://rainbio.cesab.org where a "terms of use" section is available.

## Authors' contributions

GD, MS and TC conceived the study, all authors provided or entered data, GD undertook the analyses, all authors contributed to writing the paper. All authors read and approved the final manuscript.

## Competing interests

The authors declare that they have no competing interests.

## Consent for publication

Not applicable.

## Ethics approval and consent to participate

Not applicable.

## Author details

¹Botanic Garden Meise, Nieuwelaan 38, BE-1860 Meise, Belgium. ²DIADE, Université Montpellier, IRD, Montpellier, France. ³Section for Ecoinformatics & Biodiversity, Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-8000 Aarhus C, Denmark. ⁴Royal Botanic Gardens, Kew, Richmond, Surrey TW9 3AE, UK. ⁵Centre for Ecology, Evolution and Environmental Changes (CE3C), Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal. ⁶Wageningen University, Biosystematics Group, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands. ⁷Herbarium et Bibliothèque de Botanique Africaine, Université Libre de Bruxelles, Boulevard du Triomphe, B-1050 Bruxelles, Belgium. ⁸Laboratoire de Botanique systématique et d'Écologie, Département des Sciences Biologiques, École
Normale Supérieure, Université de Yaoundé I, Yaoundé, Cameroon. ⁹Missouri Botanical Garden, Africa & Madagascar Department, P.O. Box 299, St. Louis, Missouri 63166-0299, USA. ¹⁰CIBIO/InBio, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, Vairão, Portugal. ¹¹CEABN/InBio, Centro de Ecologia Aplicada "Professor Baeta Neves", Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda 1349-017, Lisboa, Portugal. ¹²Laboratoire d'Évolution biologique et Écologie, Faculté des Sciences, Université Libre de Bruxelles, Brussels, Belgium. ¹³Royal Botanic Garden Edinburgh, 20A Inverleith Row, Edinburgh, UK. ¹⁴Naturalis Biodiversity Center, Darwinweg 2, 2333 CR Leiden, The Netherlands. ¹⁵Picturae, De Droogmakerij 12, 1851XL Heiloo, The Netherlands. ¹⁶Department of Ecology, Faculty of Science, Charles University, Vinicna 7, 128 44 Prague 2, Czech Republic. ¹⁷Institut für Geobotanik und Botanischer Garten, Im Neuwerk 21, University Halle-Wittenberg, 06108 Halle (Saale), Germany. ¹⁸Laboratoire d'Écologie végétale et Biogéochimie, Université Libre de Bruxelles, Boulevard du Triomphe, B-1050 Bruxelles, Belgium. ¹⁹Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France. ²⁰AMAP, CNRS, INRA, IRD, Université Montpellier, Montpellier, France. ²¹CESAB/FRB, Domaine du Petit Arbois, Av. Louis Philibert, Aix-en-Provence 13100, France. ²²Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, Box 951496, Los Angeles, CA 90095, USA. ²³International Institute of Tropical Agriculture, BP 2008 (Messa), Yaoundé, Cameroon.

Received: 3 November 2016 Accepted: 25 January 2017

Published online: 07 March 2017

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