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Forest edges that border savanna are dynamic features of tropical landscapes. Although the role of fire in determining edge dynamics has been relatively well explored, the role of mega-herbivores, specifically elephants, has not received as much attention. We investigated the role of forest elephants in shaping forest edges of the forest–savanna mosaic in Lopé National Park, Gabon. Using forty camera traps, we collected 1.2 million images between May 2016 and June 2017. These images were classified by over 10,000 volunteers through an online citizen science platform. These data were combined with a 33-year phenology dataset on elephant-favoured fruiting tree species, and field measurements of elephant browsing preferences and damage. Our results showed a strong relationship between forest elephant density at the forest edge and fruit availability. When fruit availability was high, elephant density at the edge reached values nearly double the highest densities ever reported in any other part of the landscape (7.5 elephants km−2 in this study vs the previous highest estimate of 4 elephants km−2). The highest elephant densities occurred at the end of the dry season, but even outside of this high density period elephant density at the forest edge (2.4 elephants km−2) was more than double what other studies estimate for forest interiors with low human hunting pressure (1 elephant km−2). We found forest elephants to be selective browsers, but their browsing was non-destructive (in contrast to savanna elephants) and had little effect on tree size demography. Elephant paths acted as firebreaks during savanna burning, making them inadvertent protectors of the fire-sensitive forest and contributing to the stabilising feedbacks that allow forest and savanna to coexist in tropical landscapes. [ABSTRACT FROM AUTHOR]

Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement. • A framework to assess global above-ground biomass maps at 0.1° spatial scale is developed and demonstrated. • Uncertainty assessment of reference data is a prerequisite prior to map comparisons. • Spatial uncertainty modelling reveals regional map biases and map-specific spatial correlation of errors. • Map-based AGB totals become closer to the value estimated by the Forest Resource Assessment over time. [ABSTRACT FROM AUTHOR]