Fragmented tropical forest landscapes preserve much of the remaining biodiversity and carbon stocks. Climate change is expected to intensify droughts and increase fire hazard and fire intensities, thereby causing habitat deterioration, and losses of biodiversity and carbon stock losses. Understanding the trajectories that these landscapes may follow under increased climate pressure is imperative for establishing strategies for conservation of biodiversity and ecosystem services. Here, we used a quantitative predictive modelling approach to project the spatial distribution of the aboveground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest (AF) domain. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report. Our AGB models had a satisfactory performance (area under the curve > 0.75 and p value < .05). The models projected a significant increase of 8.5% in the total carbon stock. Overall, the projections indicated that 76.9% of the AF domain would have suitable climatic conditions for increasing biomass by 2100 considering the RCP 4.5 scenario, in the absence of deforestation. Of the existing forest fragments, 34.7% are projected to increase their AGB, while 2.6% are projected to have their AGB reduced by 2100. The regions likely to lose most AGB-up to 40% compared to the baseline-are found between latitudes 13° and 20° south. Overall, although climate change effects on AGB vary latitudinally for the 2071-2100 period under the RCP 4.5 scenario, our model indicates that AGB stocks can potentially increase across a large fraction of the AF. The patterns found here are recommended to be taken into consideration during the planning of restoration efforts, as part of climate change mitigation strategies in the AF and elsewhere in Brazil.
Keywords: climate change; ecosystem service; fragmentation; human-modified landscape; neotropical carbon stocks modelling; remote sensing.
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