This data package contains data from: African savanna-forest boundary dynamics: a 20-year study
This dataset is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-SA 4.).
When using this data, please cite the original article:
Additionally, please cite the data package:
Cuni-Sanchez A, White LJT ,Calders K, Jeffery KJ, Burt A, Disney M, Gilpin M, Gomez-Dans JL, Lewis SL. 2016. Data from: "African savanna-forest boundary dynamics: a 20-year study"
ForestPlots.NET DOI: 10.5521/FORESTPLOTS.NET/2016_1
Recent studies show widespread encroachment of forest into savannas with important consequences for the global carbon cycle and land-atmosphere interactions. However, little research has focused on in situ measurements of forest-savanna boundary change over time. Using long-term inventory plots we quantify changes in above-ground biomass (AGB), vegetation structure and biodiversity over 20 years for five vegetation types (savanna, colonising forest or F1, successional monodominant forest or F2, Marantaceae forest or F3 and mixed forest or F4) along a savanna-forest transition of central Gabon, all occurring on similar soils. Additionally, we use novel 3D terrestrial laser scanning (TLS) measurements to assess forest structure differences across the transition.
Overall, F1 and F2 forests increased in AGB, mainly as a result of adding stems (recruitment in F1) or increased Basal Area (F2). Some plots of F3 and F4 increased in AGB while some decreased. Changes in biodiversity and species’ dominance were small. After 20 years no plot could be classified as having moved to the next stage in the succession. TLS vertical plant profiles showed very distinctive differences amongst the vegetation types.
We highlight two relevant points: (i) as forest colonises, changes in biodiversity are much slower than changes in forest structure or AGB; and (ii) all forest types store important quantities of Carbon. Decades long-term monitoring is likely to be required to assess the speed of transition between vegetation types, ideally with TLS, as this provides more objective forest classifications than inventory monitoring.