Forest and woodland landscape restoration is a key undertaking of renewed interest for forestry and conservation practitioners, but is hampered by the lack of information on the distributions of tree species and of patterns of intra-specific genetic variation. Through the first meta-analysis of its type, we here tested the utility of a high-resolution potential natural vegetation (PNV) map for eastern Africa (vegetationmap4africa) for supporting restoration activities by comparison with 20 molecular marker genetic datasets, identified through literature review and other sources, for ten indigenous tree species. Our analysis indicated that site suitability and stability values from PNV-based ecological niche modelling involving current and past climate scenarios were positively related to population genetic diversity values revealed by molecular markers, supporting the value of PNV maps for the practical planning of restoration activities accounting for anthropogenic climate change. Furthermore, population pairwise genetic divergence was strongly positively correlated with population pairwise geographic distances for most datasets, indicating generalizable sampling implications for tree genetic resource conservation in the region. Population pairwise genetic divergence was however not well explained by sampling across PNV and wider physiognomic types, possibly due to molecular markers’ adaptive neutrality and high rates of recombination in trees, among other factors. Patterns of neutral molecular marker variation are thus no substitute for trials of adaptive variation for confirming or refuting the utility of vegetation boundaries in defining tree planting zones. We discuss the importance of results for eastern Africa and more widely.
Dawson, I. K., van Breugel, P., Coe, R., Kindt, R., van Zonneveld, M., Lillesø, J. P. B., … Jamnadass, R. (2017). A meta-analysis of molecular marker genetic datasets for eastern Africa trees supports the utility of potential natural vegetation maps for planning climate-smart restoration initiatives. Tree Genetics and Genomes, 13(4). https://doi.org/10.1007/s11295-017-1155-7