Distribution of tree species with high economic and livelihood value for Zambia

The sustainable management of dry forests and woodlands of Sub-Saharan Africa (SSA) is crucial for the SSA economy and people's livelihoods but it remains a key challenge to address. Maps of tree species distributions of economic value are lacking for the region; yet, this information is essential for supporting sustainable use. We capitalized on new nation-wide field survey data for the Republic of Zambia (Southern Africa) to generate the first suite of baseline species distribution models (SDMs) for 20 tree species of economic significance to support for sustainable management and conservation. We employed one regression- and two machine learning-based techniques to model and predict tree distribution. For each species, we compared the three model results for the relative importance of different predictive variables, revealing the most important predictors of each species niche and providing insight into how human activities influence current tree species distribution. Overall, environmental predictors that best explained tree species distribution were related to water availability, including mean potential evapotranspiration (PET), annual rainfall, and the variation in PET, as well as elevation and soil fertility. Human impact on distribution was notable for tree species used for charcoal and timber, including the proximity to roads for charcoal-favored species and the proximity to settlement for timber species. For all tree species, fire did not stand out as a variable of importance, contrary to expectations. The SDMs generated from this study will provide essential baseline information to support national conservation and management efforts, especially for preferred timber and charcoal species for which selective harvesting has had an impact on their distribution. Our results highlight the importance of rainfalls for the distribution of tree species in this seasonally dry region and calls for future research to forecast the impacts of climate change on habitat suitability.