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Aim: Lianas are an important component of forest structure in the tropics, accounting for up to 45% of total stems. Mounting evidence that tropical forests are undergoing structural changes, with a growing abundance of lianas reducing forest carbon storage potential, imparts a sense of urgency to study the drivers that control liana abundance and biomass, particularly in Africa where data come from a few, small-scale studies. Location: Gabon, Africa. Methods: In the first countrywide study of lianas, we implemented the most ambitious, large-scale forest inventory in tropical Africa to date, quantifying the density, basal area and biomass of large lianas (≥10 cm in diameter) using a systematic, random design of 104 plots located across Gabon. Additionally, we examined the relative importance of environmental variables (mean annual precipitation, mean annual temperature, seasonality, soil nitrogen, soil fertility), disturbance (effect of gaps, forest type) and forest structure (large tree biomass) in driving macroscale variation in the abundance of large lianas. Results: In total, we surveyed 1354 large lianas, and found the density, basal area and biomass of large lianas in Gabon to be comparable to that in other tropical forests. The success of large lianas was positively related to soil N, but most strongly correlated with forest structure, particularly large tree biomass. The strength of the association between large lianas and large trees increased with tree size class. Main conclusions: Forest structure and the availability of large trees may be more important predictors of the abundance and distribution of large lianas in African tropical forests than environmental variables and disturbance. Changing environmental conditions are likely to have little direct effect on large lianas, but climate change, defaunation and land-use activities that diminish forest structure and reduce the number of large trees could have strong indirect effects on large lianas in Central African forests.

Permanent sample plots (PSP), where trees are individually and permanently marked, have received increased interest in Central Africa as a tool to monitor vegetation changes. Although techniques for mounting PSP in tropical forests are well known, their planning still deserves attention. This study aims at defining a rationale for determining the size and number of replicates for setting up PSP in mixed tropical forests. It considers PSP as a sampling plan to estimate a target quantity with its associated margin of error. The target quantity considered here is the stock recovery rate, which is a key parameter for forest management in Central Africa. It is computed separately for each commercial species. The number of trees to monitor for each species defines the margin of error on the stock recovery rate. The size and number of replicated plots is obtained as the solution of an optimization problem that consists in minimizing the margin of error for every species while ensuring that the mounting cost remains below a given threshold. This rationale was applied using the data from the M'Baïki experimental site in the Central African Republic. It showed that the stock recovery rate is a highly variable quantity, and that the typical cost that forest managers are prone to devote to PSP leads to high margins of error. It also showed that the size and number of replicated plots is related to the spatial pattern of trees: clustered or spatially heterogeneous patterns favor many small plots, whereas regular or spatially homogeneous patterns favor few large plots.