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. Disturbances, such as extreme weather events, fires,floods, and biotic agents, can have strong impacts on the dynamics andstructures of tropical forests. In the future, the intensity of disturbanceswill likely further increase, which may have more serious consequences fortropical forests than those we have already observed. Thus, quantifyingaboveground biomass loss of forest stands due to stem mortality (hereafterbiomass loss rate) is important for the estimation of the role of tropicalforests in the global carbon cycle. So far, the long-term impacts of alteredstem mortality on rates of biomass loss have not been adequately described. This study aims to analyse the consequences of long-term elevated stemmortality rates on forest dynamics and biomass loss rate. We applied anindividual-based forest model and investigated the impacts of permanentlyincreased stem mortality rates on the growth dynamics of humid, terra firmeforests in French Guiana. Here, we focused on biomass, leaf area index(LAI), forest height, productivity, forest age, quadratic mean stemdiameter, and biomass loss rate. Based on the simulation data, we developeda multiple linear regression model to estimate biomass loss rates of forestsin different successional states from the various forest attributes. The findings of our simulation study indicated that increased stem mortalityaltered the succession patterns of forests in favour of fast-growingspecies, which increased the old-growth forests' gross primary production,though net primary production remained stable. The stem mortality rate had astrong influence on the functional species composition and tree sizedistribution, which led to lower values in LAI, biomass, and forest heightat the ecosystem level. We observed a strong influence of a change in stemmortality on biomass loss rate. Assuming a doubling of stem mortality rate, thebiomass loss rate increased from 3.2 % yr−1 to 4.5 % yr−1 atequilibrium. We also obtained a multidimensional relationship that allowedfor the estimation of biomass loss rates from forest height and LAI. Via anexample, we applied this relationship to remote sensing data on LAI andforest height to map biomass loss rates for French Guiana. We estimated acountrywide mean biomass loss rate of 3.0 % yr−1. The approach described here provides a novel methodology for quantifyingbiomass loss rates, taking the successional state of tropical forests intoaccount. Quantifying biomass loss rates may help to reduce uncertainties inthe analysis of the global carbon cycle.

Importance of the forest state in estimating biomass losses from tropical forests: combining dynamic forest models and remote sensing