Potential aboveground biomass increase in Brazilian Atlantic Forest fragments with climate change

Abstract

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 baselineare 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.

Quantifying landscape fragmentation and forest carbon dynamics over 35 years in the Brazilian Atlantic Forest

“…Another important point is the need for not only preserve forests in lower fragmentation class, but also those in higher fragmentation classes to increase the resilience of AF functions to climate change. A recent model predicting the impact of climate change on AF above-ground biomass (AGB) showed that the most significant loss of AGB, up to 40%, compared to the baseline value, is likely to occur between latitudes 13 • and 20 • south (Ferreira et al 2023). Our results showed deforestation and degradation leading to core area loss in southern and south-eastern forests at these latitudes, which may decrease the capacity of forests to counteract the effects of changes in climate.…”

Assessment of fire hazard in Southwestern Amazon

“…The algorithm has been commonly used for species distribution and habitat modeling, and it is based on the probabilistic inference method to estimate the maximum entropy distribution from incomplete datasets (Phillips et al, 2006(Phillips et al, , 2017. The method has been successfully applied to estimate the occurrence of fires in the landscape (Parisien and Moritz, 2009;Fonseca et al, 2016) and the aboveground biomass distribution (Saatchi et al, 2011;Ferreira et al, 2023).…”

Application of MaxEnt Model in Biomass Estimation: An Example of Spruce Forest in the Tianshan Mountains of the Central-Western Part of Xinjiang, China

“…This study found that the contribution of precipitation seasonality and precipitation of the warmest quarter, related to moisture, exceeded that of temperature-related parameters in the model. In addition, the spatial variation in above-ground biomass driven by climate change is unclear, and the MaxEnt model is an important approach to exploring this issue [32].…”

“…While few applications of the MaxEnt model for estimating biomass have been made to date, notable examples include the work of Saatchi et al [27], who applied the model to estimate forest biomass in Latin America, South Africa, and Southeast Asia, obtaining an overall mean uncertainty of ±30% for AGB at the pixel scale. Since then, scholars have made further applications in regions such as Mexico [28], the Congo [29], the United States [30,31], and the Brazilian Atlantic Forest [32]. However, the established MaxEnt model for biomass estimation has largely been applied at large scales, such as intercontinental and national scales, with limited applications to small-scale regions.…”