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Ongoing large-scale habitat disturbance requires quick identification of conservation priorities such as targeting sites rich in species and/or endemics. Biodiversity assessments are time consuming and expensive, so surveys often rely on partial sampling. Optimal use should be made of all currently available sources of information, but methodological differences between surveys hamper direct comparison. Because diversity depends on spatial scale, diversity characteristics of different sites are best compared on the basis of species-area relationships. As a result of the incompleteness of sampling, the observed species-area relationship deviates from the "true" species-area relationship.In this paper, we identify five key factors affecting the shape of the species-area relationship due to incomplete sampling: (1) the total spatial extent of the observations, (2) the spatial distribution of the observations, (3) the proportion of the total extent sampled, (4) the proportion of the individuals in the sampled area included in the survey, and (5) the proportion of the included individuals successfully identified. We outline how methodologically different surveys can be combined to optimize the use of existing data in the evaluation of conservation needs, particularly for tropical forests.As an illustration, we analyzed four methodologically different botanical surveys in the same area of old growth lowland forest in South Cameroon with the aim of reconciling these surveys. The four surveys were (1) reconnaissance scale vegetation mapping, (2) detailed botanical assessment (all individuals), (3) incomplete botanical assessment (10% individuals), and (4) herbarium collections.By correcting for the five key factors we were able to match the results of the four different biodiversity surveys. The five key factors affected the recorded number of species and endemics differently; partial sampling of extent (3) and individuals (4) and partial identification of individuals (5) were the three most important factors.We conclude that reconciliation of biodiversity assessments is possible if the differences between methods can be accounted for. We advocate reliable documentation of survey methods, especially the five key factors, because it greatly enhances the potential of combining methodologically different surveys for comparative biodiversity analyses.

Aim Tropical rain forests are often regarded as pristine and undisturbed by humans. In Central Africa, community-wide disturbances by natural causes are rare and therefore current theory predicts that natural gap phase dynamics structure tree species composition and diversity. However, the dominant tree species in many African forests recruit poorly, despite the presence of gaps. To explain this, we studied the disturbance history of a species-rich and structurally complex rain forest. Location Lowland rain forest in Southern Cameroon. Methods We identified the recruitment conditions of trees in different diameter classes in 16 ha of species-rich and structurally complex old growth rain forest. For the identification of recruitment preference we used independent data on the species composition along a disturbance gradient, ranging from shifting cultivation fields (representing large-scale disturbance), to canopy gaps and old growth forest. Results In nine of sixteen 1-ha forest plots the older trees preferred shifting cultivation fields for recruitment while younger trees preferred gaps and closed forest conditions. This indicates that these nine sites once experienced large-scale disturbances. Three lines of evidence suggest that historical agricultural use is the most likely disturbance factor: (1) size of disturbed and undisturbed patches, (2) distribution of charcoal and (3) historical accounts of human population densities. Main conclusions Present-day tree species composition of a structurally complex and species-rich Central African rain forest still echoes historical disturbances, most probably caused by human land use between three to four centuries ago. Human impact on African rain forest is therefore, contrary to common belief, an issue not of the last decades only. Insights in historical use will help to get a more balanced view of the pristine rain forest, acknowledging that the dualism between old growth and secondary forest may be less clear than previously thought.

Secondary forests in Central Africa are increasing in importance for biodiversity conservation as old growth forests outside the few protected areas are disappearing rapidly. We examined vegetation recovery in a lowland rain forest area in Cameroon based on a detailed botanical survey of old growth forest and different-aged logging gaps (5-27 years) and shifting cultivation fields (10-60 years). Our analysis focuses on the long-term recovery of botanical conservation values by analysing trends in vegetation structure, species composition, species diversity and levels of endemism and rarity. In the total survey (4.25 ha), we recorded 834 species of which 23% were endemic to the Lower Guinea forest region. The proportion of endemic species was high in shrubs and low in herbs. Geographic range and (local) rarity were not significantly associated. The proportion of rare species (relative frequency ,10%) was high in woody climbers and low in trees. In logging gaps, recovery of all vegetation characteristics was relatively quick (5-14 years). Recovery in shifting cultivation sites took longer (30-60 years). Endemic species were found to be highly sensitive to shifting cultivation practices and even after 50-60 years the level of endemism was still significantly lower compared to old growth forest. The proportion of rare species was not significantly different between disturbed sites and old growth forest. We conclude that secondary forests can contribute to biodiversity conservation, e.g. as buffer zones around protected areas. However, this contribution should be assessed differently between land use types and widespread versus endemic species.

1. Comparative analyses of diversity variation among and between regions allow testing of alternative explanatory models and ideas. Here, we explore the relationships between the tree alpha-diversity of small rain forest plots in Africa and in Amazonia and climatic variables, to test the explanatory power of climate and the consistency of relationships between the two continents. 2. Our analysis included 1003 African plots and 512 Amazonian plots. All are located in old-growth primary non-flooded forest under 900 m altitude. Tree alpha-diversity is estimated using Fisher's alpha calculated for trees with diameter at breast height >= 10 cm. Mean diversity values are lower in Africa by a factor of two. 3. Climate-diversity analyses are based on data aggregated for grid cells of 2.5 x 2.5 km. The highest Fisher's alpha values are found in Amazonian forests with no climatic analogue in our African data set. When the analysis is restricted to pixels of directly comparable climate, the mean diversity of African forests is still much lower than that in Amazonia. Only in regions of low mean annual rainfall and temperature is mean diversity in African forests comparable with, or superior to, the diversity in Amazonia. 4. The climatic variables best correlated with the tree alpha-diversity are largely different in the African and Amazonian data, or correlate with African and Amazonian diversity in opposite directions. 5. These differences in the relationship between local/landscape-scale alpha-diversity and climate variables between the two continents point to the possible significance of an array of factors including: macro-scale climate differences between the two regions, overall size of the respective species pools, past climate variation, other forms of long-term and short-term environmental variation, and edaphics. We speculate that the lower alpha-diversity of African lowland rain forests reported here may be in part a function of the smaller regional species pool of tree species adapted to warm, wet conditions. 6. Our results point to the importance of controlling for variation in plot size and for gross differences in regional climates when undertaking comparative analyses between regions of how local diversity of forest varies in relation to other putative controlling factors.