What determines plant species diversity in Central Africa?

Planet Earth hosts an incredible biological diversity. Estimated numbers of species occurring on Earth range from 5 to 11 million eukaryotic species including 400,000-450,000 species of plants. Much of this biodiversity remains poorly known and many species have not yet been named or even been discovered. This is not surprising, as the majority of species is known to be rare and ecosystems are generally dominated by a limited number of common species. Tropical rainforests are the most species-rich terrestrial ecosystems on Earth. The general higher level of species richness is often explained by higher levels of energy near the Equator (latitudinal diversity gradient). However, when comparing tropical rainforest biomes, African rainforests host fewer plant species than either South American or Asian ones. The Central African country of Gabon is situated in the Lower Guinean phytochorical region. It is largely covered by what is considered to be the most species-rich lowland rainforest in Africa while the government supports an active conservation program. As such, Gabon is a perfect study area to address that enigmatic question that has triggered many researchers before: “What determines botanical species richness?”. In the past 2.5 million years, tropical rainforests have experienced 21 cycles of global glaciations. They responded to this by contracting during drier and cooler glacials into larger montane and smaller riverine forest refugia and expanding again during warmer and wetter interglacials. The current rapid global climate change coupled with change of land use poses new threats to the survival of many rainforest species. The limited availability of resources for conservation forces governments and NGOs to set priorities. Unfortunately, for many plant species, lack of data on their distribution hampers well-informed decision making in conservation. Species distribution models (SDMs) offer opportunities to bridge at least partly this knowledge gap. SDMs are correlative models that infer the spatial distribution of species using only a limited set of known species occurrence records coupled with high resolution environmental data. SDMs are widely applied to study the past, present and future distribution of species, assess the risk of invasive species, infer patterns of species richness and identify hotspots, as well as to assess the impact of climate change. The currently available methods form a pipeline, with which data are selected and cleaned, models selected, parameterized, evaluated and projected to other areas and climatic scenarios, and biodiversity patterns are computed from these SDMs. In this thesis, SDMs of all Gabonese plant species were generated and patterns of species richness and of weighted endemism were computed (chapter 4 & 5). Although this pipeline enables the rapid generation of SDMs and inferring of biodiversity patterns, its effective use is limited by several matters of which three are specifically addressed in this thesis. Not knowing the true distribution limits the opportunities to assess the accuracy of models and assess the impact of assumptions and limitations of SDMs. The use of simulated species has been advocated as a method to systematically assess the impact of specific matters of SDMs (virtual ecologist). Following this approach, in chapter 2, I present a novel method to simulate large numbers of species that each have their own unique niche. One matter of SDMs that is usually ignored but has been shown to be of great impact on model accuracy is the number of species occurrence records used to train a model. In chapter 2, I quantify the effect of sample size on model accuracy for species of different range size classes. The results show that the minimum number of records required to generate accurate SDMs is not uniform for species of every range size class and that larger sample sizes are required for more widespread species. By applying a uniform minimum number of records, SDMs of narrow-ranged species are incorrectly rejected and SDMs of widespread species are incorrectly accepted. Instead, I recommend to identify and apply the unique minimum numbers of required records for each individual species. The method presented here to identify the minimum number of records for species of particular range size classes is applicable to any species group and study area. The range size or prevalence is an important plant feature that is used in IUCN Red List classifications. It is commonly computed as the Extent Of Occurrence (EOO) and Area Of Occupancy (AOO). Currently, these metrics are computed using methods based on the spatial distribution of the known species occurrences. In chapter 3, using simulated species again, I show that methods based on the distribution of species occurrences in environmental parameter space clearly outperform those based on spatial data. In this chapter, I present a novel method that estimates the range size of a species as the fraction of raster cells within the minimum convex hull of the species occurrences, when all cells from the study area are plotted in environmental parameter space. This novel method outperforms all ten other assessed methods. Therefore, the current use of EOO and AOO based on spatial data alone for the purpose of IUCN Red List classification should be reconsidered. I recommend to use the novel method presented here to estimate the AOO and to estimate the EOO from the predicted distribution based on a thresholded SDM. In chapter 4, I apply the currently best possible methods to generate accurate SDMs and estimate the range size of species to the large dataset of Gabonese plant species records. All significant SDMs are used here to assess the unique contribution of narrow-ranged, widespread, and randomly selected species to patterns of species richness and weighted endemism. When range sizes of species are defined based on their full range in tropical Africa, random subsets of species best represent the pattern of species richness, followed by narrow-ranged species. Narrow-ranged species best represent the weighted endemism pattern. Moreover, the results show that the applied criterion of widespread and narrow-ranged is crucial. Too often, range sizes of species are computed on their distribution within a study area defined by political borders. I recommend to use the full range size of species instead. Secondly, the use of widespread species, of which often more data are available, as an indicator of diversity patterns should be reconsidered. The effect of global climate change on the distribution patterns of Gabonese plant species is assed in chapter 5 using SDMs projected to the year 2085 for two climate change scenarios assuming either full or no dispersal. In Gabon, predicted loss of plant species ranges from 5% assuming full dispersal to 10% assuming no dispersal. However, these numbers are likely to be substantially higher, as for many rare, narrow-ranged species no significant SDMs could be generated. Predicted species turnover is as high as 75% and species-rich areas are predicted to loose many species. The explanatory power of individual future climate anomalies to predicted future species richness patterns is quantified. Species loss is best explained by increased precipitation in the dry season. Species gain and species turnover are correlated with a shift from extreme to average values of annual temperature range. In the final chapter, the results are placed in a wider scientific context. First, the results on the methodological aspects of SDMs and their implications of the SDM pipeline are discussed. The method presented in this thesis to simulate large numbers of species offers opportunities to systematically investigate other matters of the pipeline, some of which are discussed here. Secondly, the factors that shape the current and predicted future patterns of plant species richness in Gabon are discussed including the location of centres of species richness and of weighted endemism in relation to the hypothesized location of glacial forest refugia. Factors that may contribute to the lower species richness of African rainforests compared with South American and Asian forests are discussed. I conclude by reflecting on the conservation of the Gabonese rainforest and its plant species as well as on the opportunities SDMs offer for this in the wider socio-economic context of a changing world with growing demand for food and other ecosystem services.