One of the greatest challenges that tropical biologists are facing now is how to conserve biological diversity within the context of population growth, with its increase in needs, overexploitation of resources, and the external forces of climate change and economic crisis. Under these threats and without effective protection, much of tropical biodiversity is unlikely to survive. Wild palms are amongst the most diverse plant groups in the world. Many species provide significant cultural, social, economic, and ecological uses. They also serve as bio-indicators in many Latin-American countries, and their occurrences could thus be used as climate-trend proxies. In sub-Saharan Africa, and especially in Benin, wild palms are not well documented. The species diversity is not well known and ecological studies on the species are rare. These data, together with a complete richness inventory, are however critical to plan informed conservation actions about palms. Robust estimates of inventory completeness could help alleviate the problem. This study aimed at identifying areas representing gaps in current knowledge of African palms, with a focus on Benin (West Africa). We carried out this study using both available accessible digital knowledge bases and extensive fieldwork inventory of wild palms (i) to describe the national species richness of this group, and (ii) to estimate the completeness of the inventory within the group. We used the inventory completeness calculated for each of half-degree cells across Benin in two different ways to differentiate well-known from poorly-sampled areas, and estimated geographic and environmental distance between those two types of areas across Benin to bring out potential gaps in the inventory. We calculated geographic distances from all grid squares in Benin to the nearest of the well-known squares, and used the Proximity (Raster Distance) function in QGIS to create a parallel view of the environmental difference of a dense set of random points from well-sampled areas, calculated over raster coverages summarizing annual mean temperature and annual precipitation. Finally, the environmental distances were linked back to the geographical distances and represented over the territory, producing a detailed map of potential coverage gaps. Results showed that inventory completeness was high across the country. Most of the country ecosystems hosting palm species are thus well sampled. However, poorly-known areas were identified and correlated with remote locations with low accessibility. This study revealed insightful information that will potentially impact scientific knowledge and conservation efforts. Even if exhaustive inventories of African palms are somehow feasible objectives for short-term fieldwork, our results demonstrate that with the addition of digitally accessible knowledge and bioinformatics resources over existing survey data (or vice versa), a more complete picture about the group of interest could be obtained, reducing gaps that would otherwise exist. We thus recommend the combination of digital accessible knowledge and fieldwork, coupled with expert knowledge, to ensure the completeness of inventories in tropical ecosystems.