Abstract
Disturbances, such as extreme weather events, fires, floods, and biotic agents, can have strong impacts on the dynamics and structures of tropical forests. In the future, the intensity of disturbances will likely further increase, which may have more serious consequences for tropical forests than those we have already observed. Thus, quantifying aboveground biomass loss of forest stands due to tree mortality (hereafter biomass loss) is important for the estimation of the role of tropical forests in the global carbon cycle. So far, the long-term impacts of altered tree mortality on rates of biomass loss have been described little. This study aims to analyse the consequences of long-term elevated tree mortality rates on forest dynamics and biomass loss. We applied an individual-based forest model and investigated the impacts of permanently increased tree mortality rates on the growth dynamics of humid, terra firme forests in French Guiana. Here, we focused on biomass, leaf area index (LAI), forest height, productivity, forest age, quadratic mean stem diameter, and biomass loss. Based on the simulations, we developed a multiple linear regression model to estimate biomass losses of forests in different successional states from the various forest attributes. The findings of our simulation study indicated that increased tree mortality altered the succession patterns of forests in favour of fast-growing species, which changed the forests’ gross primary production, though net primary production remained stable. Tree mortality intensity had a strong influence on the functional species composition and tree size distribution, which led to lower values in LAI, biomass, and forest height at the ecosystem level. We observed a strong influence of a change in tree mortality on biomass loss. Assuming a doubling of tree mortality, biomass loss increased (from 3.2 % y−1 to 4.5 % y−1). We also obtained a multidimensional relationship that allowed for the estimation of biomass loss from forest height and LAI. Via an example, we applied this relationship to remote sensing data of LAI and forest height and mapped biomass loss for French Guiana. We estimated a mean biomass loss rate of 3.2 % per year. The approach described here provides a novel methodology for quantifying biomass loss, taking the successional state of tropical forests into account. Quantifying biomass loss rates may help to reduce uncertainties in the analysis of the global carbon cycle.
Highlights
Tropical forests represent an important pool in the global carbon cycle, as they store approximately 55% of the amount of global forest carbon (471 ± 93 PgC) in their living biomass (Pan et al, 2011)
We address the following research questions in detail: 1. What are the consequences of permanently increased tree mortality levels on the dynamics of forest attributes in tropical forests?
3 Results 3.1 Influence of increased tree mortality on forest succession dynamics To analyse the influences of varying tree mortality intensities, we simulated succession dynamics, which were affected by 240 competition among individual tree species belonging to species groups
Summary
Tropical forests represent an important pool in the global carbon cycle, as they store approximately 55% of the amount of global forest carbon (471 ± 93 PgC) in their living biomass (Pan et al, 2011). Intact tropical forests assimilate an average of 0.96 ± 0.46 PgC of carbon per year (Hubau et al, 2020). This carbon sink behaviour of tropical forests has considerably 40 reduced the growth rate of atmospheric carbon dioxide (Friedlingstein et al, 2019; Le Quéré et al, 2016). Increased tree mortality due to disturbances has been related to a reduction in the carbon sink of tropical forests (Brienen et al, 2015; Hubau et al, 2020). Higher levels of tree mortality present a major risk to climate mitigation efforts (e.g., REDD+: Reducing Emissions from Deforestation and Forest Degradation), as reductions in carbon assimilation rates and a decrease in the carbon stocks of tropical forests could counteract attempts to compensate for climate change (Gumpenberger et al, 2010; Körner, 2017; Le Page et al, 2013)
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