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 stem mortality (hereafter biomass loss rate) is important for the estimation of the role of tropical forests in the global carbon cycle. So far, the long-term impacts of altered stem mortality on rates of biomass loss have not been adequately described.
This study aims to analyse the consequences of long-term elevated stem mortality rates on forest dynamics and biomass loss rate. We applied an individual-based forest model and investigated the impacts of permanently increased stem 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 rate. Based on the simulation data, we developed a multiple linear regression model to estimate biomass loss rates of forests in different successional states from the various forest attributes.
The findings of our simulation study indicated that increased stem mortality
altered the succession patterns of forests in favour of fast-growing
species, which increased the old-growth forests' gross primary production,
though net primary production remained stable. The stem mortality rate 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 stem
mortality on biomass loss rate. Assuming a doubling of stem mortality rate, the
biomass loss rate increased from 3.2 % yr
The approach described here provides a novel methodology for quantifying biomass loss rates, 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.
Tropical forests represent an important pool in the global carbon cycle, as
they store approximately 55 % of the amount of global forest carbon
(
Mortality is a complex process because the causes leading to tree death can be diverse. Trees can die naturally from senescence or from forest disturbances, which may be abrupt or continuous and may have abiotic or biotic, allogenic or autogenic, and extrinsic or intrinsic causes (Franklin et al., 1987; McDowell et al., 2018). Furthermore, drivers of stem mortality often occur in combination, so the primary factors of death are not obvious (Franklin et al., 1987; McDowell et al., 2018). Stem mortality leads to temporal changes in stand structure, tree species composition, and releases of resources, particularly biomass (Franklin et al., 1987; Hülsmann et al., 2018). Consequently, tree death affects important forest growth processes, including tree growth and establishment, which are influenced by species-specific competition strategies (Snell et al., 2014) as well as by environmental and competitive factors such as light availability (Kuptz et al., 2010; Poorter, 1999; Uriarte et al., 2004). The influence of stem mortality on forest growth dynamics is determined by the disturbance intensity, which can range from the temporary loss of vitality to the mortality (Kindig and Stoddart, 2003) of individual trees, forest stands, and entire landscapes. Finally, stem mortality events are heterogeneously distributed such that spatial patterns can be scattered or clustered (Franklin et al., 1987). Empirical studies have already analysed the effects of short-term disturbances (i.e. intra-annual or over a few years) on increases in tropical stem mortality (e.g. Barlow et al., 2003; Brando et al., 2014; Chambers et al., 2009, 2013; Doughty et al., 2015; Holzwarth et al., 2013; Magnabosco Marra et al., 2016; Marra et al., 2014; McDowell et al., 2018; Negrón-Juárez et al., 2010, 2017; Nepstad et al., 2007; Phillips and Brienen, 2017; Slik et al., 2010; Stovall et al., 2019; Wright et al., 2015). Nevertheless, using empirical studies that are limited in space and time, it is difficult to quantify the long-term effects of permanently increased stem mortality levels and to assess the consequences of such alterations on the dynamics, structures, and successional states of forests. Also, new remote sensing technologies offer enhanced potential for measuring the vertical and horizontal structures of forests at country to global scales (e.g. Bi et al., 2015; Hall et al., 2011; Lefsky et al., 2002, 2005; Myneni et al., 2015; Simard et al., 2011; Le Toan et al., 2011). Remote sensing products have previously been used for large-scale identification of stem mortality following disturbances (e.g. Pugh et al., 2019; Senf and Seidl, 2020); however, the estimation of biomass loss rates due to stem mortality for forests at different states remains still uncertain.
In this context, individual-based forest gap models offer an approach to analysing forest dynamics (Botkin et al., 1972; Bugmann, 2001; Bugmann et al., 2019; Fischer et al., 2016; Shugart, 2002). Individual-based forest models are parameterized with forest inventory data to allow for the investigation of forest growth dynamics over longer periods. By simulating the growth, establishment, mortality, and competition among trees within a forest, these models can contribute to estimating the biomass gain and loss of tropical forests (e.g. Hiltner et al., 2018, 2021; Maréchaux and Chave, 2017). As a result of gap formation after tree falling (Fischer et al., 2016; Huth et al., 1998), simulation areas consist of a mosaic of forest stands on which the vertical and horizontal structures and dynamics of forests in different successional states are modelled (Botkin, 1993; Botkin et al., 1972; Bugmann, 2001; Fischer et al., 2016; Shugart, 1984). Structural state variables describing successional states of forests, such as tree size distributions and functional tree species compositions, play a major role in the estimation of the carbon budgets of forest stands and entire landscapes (Bohn and Huth, 2017; Fischer et al., 2018, 2019; Rödig et al., 2017, 2018, 2019; Rüger et al., 2020). Successional state variables of forests can be derived on large spatial scales (e.g. country to global levels) through a combination of individual-based forest gap modelling and remote sensing (Rödig et al., 2017, 2019; Shugart et al., 2015, 2018), as this allows for a quantification of the spatial variation in forest structure due to stem mortality (Rödig et al., 2017). The combination of individual-based forest gap models and remote sensing methods may also provide information on the spatial distribution of the annual rates of aboveground biomass loss due to stem mortality (hereafter biomass loss rate).
The aims of this study are to investigate the impacts of permanently
increased stem mortality rates on forest dynamics, to provide a framework
for estimating biomass loss rates in terra firme forests at different
successional states, and to derive a sample map of biomass loss for an
entire country (i.e. French Guiana). This biomass loss map represents an
application example to demonstrate the synergetic benefits of linking an
artificial dataset derived from an individual-based forest model with remote
sensing data. Here, we address the following research questions in detail.
What are the consequences of permanently increased stem mortality rates on
the dynamics of forest attributes (e.g. aboveground biomass, forest height,
gross primary production, net primary production, leaf area index, quadratic
mean stem diameter, mean forest age, and biomass loss rates) in tropical
forests? Can the biomass loss rates of tropical forests be estimated using various
forest attributes that can also be derived from remote sensing?
We applied the “terra firme” version of the dynamic individual-based forest
model FORMIND
(Fischer
et al., 2016; Hiltner et al., 2018; Köhler and Huth, 2004) and simulated
the effects of long-term increased stem mortality levels on the dynamics of
multiple forest attributes (Fig. 1). This artificial dataset of forest
dynamics covers a wide range of possible forest states such as the
variability in tree species composition, successional state, and tree size
distribution. We assume that we can use it to partially cover almost every
state of forest stands in French Guiana (the so-called forest factory approach,
see Bohn and Huth, 2017). We included aboveground biomass (hereafter biomass),
mean forest height, gross primary production (GPP), net primary production
(NPP), leaf area index (LAI), biomass turnover time (
Framework developed for estimating biomass loss rates by linking a dynamic forest model and remote sensing. (1) A forest model was applied to (2) simulate the succession dynamics of forest stands of various forest attributes, such as LAI, forest height, and biomass loss rate, in a set of different stem mortality scenarios (results used to answer research question 1). A simulated forest stand has the area of 1 ha, with the forest states of each simulated hectare differing at each simulation time step and scenario. (3) Then, we developed a multiple linear regression model for the simulated forest states with LAI and forest height as proxy variables and biomass loss rate as the response (results to answer research question 2). (4) In addition, we applied the multiple linear regression model to remote sensing maps containing the values of the investigated forest attributes (LAI and forest height) to (5) derive a sample map of biomass loss.
The study region is French Guiana, 95 % of which is covered by humid, lowland terra firme forests (Hammond, 2005; Stach et al., 2009). These forests are characteristic for the Guiana Shield (Grau et al., 2017). The forests are generally species-rich, with an average of 150 to 200 tree species per hectare (Gourlet-Fleury et al., 2004), and are dense in biomass stock (Johnson et al., 2016; Rödig et al., 2017; Saatchi et al., 2011).
To analyse the forest dynamics under the impacts of different levels of disturbance, we applied the “terra firme parameterization” of the forest model FORMIND v3.2 (Fischer et al., 2016) and took relevant parameter values from Hiltner et al. (2018), including tree growth, mortality, and establishment (see Tables S1 and S2 in the Supplement). FORMIND is an individual-based forest gap model that describes forest dynamics, tree growth, and changes in forest structures on a simulation area (1 ha to multiple square kilometres) consisting of 20 by 20 m patches that interact with each other (see Fig. S1 in the Supplement), wherein trees are not positioned explicitly within a patch.
Every tree with a stem diameter at breast height (DBH)
For the generic forest model parameterization of French Guiana's terra firme
forests, tree species were classified into eight plant functional types
(PFTs) according to species-specific traits, i.e. the maximum incremental
rates of DBH and maximum tree height. We assume here that major parts of the
terra firme forests can be characterized on the basis of three functional
species groups: light-requiring species, species with intermediate light
requirements, and shade-tolerant species (see Table S2). This functional
species diversity is considered to be sufficient to capture forest
succession dynamics in tropical forests
(Fischer et al.,
2018; Rödig et al., 2017; Rüger et al., 2020). Detailed model
descriptions can be found in
Fischer
et al. (2016), in Hiltner et al. (2018), and online at
To investigate the effects of different stem mortality intensities on the
dynamics and the structure of terra firme forests, we developed seven
simulation scenarios: a baseline scenario and six scenarios with permanently
altered stem mortality rates (Table 1). The baseline scenario is based on
observed mortality rates (
Average stem mortality rate per simulation scenario and
specification (see Eq. 1). Background mortality rate
From the model outputs of all scenarios, we analysed the average development
of multiple forest attributes (averaged over 16 ha), such as
aboveground biomass (AGB), LAI, forest height (mean height of the tallest three
trees per 40 m
In addition, we computed the time over which each forest attribute reached
the stable state (hereafter: equilibrium time) as well as the mean stand
biomass turnover times (
To estimate the biomass loss rates, we analysed a number of forest
simulations which produced a large number of different forest stands each of
1 ha (averaged over 16 ha simulation areas) in different successional states
(per simulated year) with unique functional species compositions and tree
size distributions. Thus, we generated a total of 33 600 terra firme forest
stands. We assumed that the rate of biomass loss can be related to other
forest attributes (e.g. biomass, LAI, forest productivity, and forest
height). For a multiple linear regression model, the temporal and spatial
components are not important since forest states are considered
independently of either. We acknowledged that when fitting linear regression
models, it is important that the proxy variables do not strongly correlate.
We tested this by using a covariance matrix with the Pearson's correlation
coefficient of the proxy variables (Table S4). Then, we tested different
linear and non-linear statistical models using different combinations of the
proxy variables (see Table S3 and Fig. S15). Important selection criteria for
the model type were good regression statistics and interpretability of the
model equation. Furthermore, remote sensing products should be available for
all proxy variables. Taking all of these criteria into account, the most
suitable estimate was made by a multiple linear regression model which
describes variations in
To estimate forest height, we used a global map in the WGS-84 geographical
projection with a pixel size of approximately 1 km
(Simard et al., 2011; Fig. S5a). For the mapping of the
forest height, Simard et al. (2011) used data from the Geoscience Laser
Altimeter System (GLAS) aboard ICESat (Ice, Cloud, and land Elevation
Satellite) collected in 2005. To create an LAI map, we used 139 data layers
from the MCD15A2H version 6 Moderate Resolution Imaging Spectroradiometer
(MODIS) Level 4 with a pixel size of 500 m and averaged the LAI values
between 31 January 2004 and 31 December 2006 to reduce the overall LAI variance
(Myneni et al., 2015). We harmonized and stacked
the two input maps by first projecting the LAI map onto the coordinate
reference system of the forest height map using the Geospatial Data
Abstraction Library for French Guiana (
The biomass loss rate was estimated for each pixel by applying the multiple
linear regression model (Eq. 6) to the two input maps (see Fig. S5). We
compared the density distributions of both input datasets with the ranges
of FORMIND's simulation results (LAI and forest height). No correction
factors were required for the extrapolations, since the most abundant
combinations of value pairs of both datasets agree well, and only a few
combinations differ (see Fig. S9). The biomass loss rates were then averaged
over a pixel size of 2 km
We tested the reliability of the mapped biomass loss rate in the underlying
input maps for the LAI and forest height via a sensitivity analysis
regarding variations of
To analyse the influences of varying stem mortality intensities, we
simulated succession dynamics, which were affected by competition among
individual tree species belonging to species groups. Here, we show that
successional stages can be differentiated based on the development of the
total stand biomass (Fig. 2). After 40 years of forest succession, the
simulated stand biomass peaked at 500 t
The baseline scenario's aboveground biomass (AGB) per species group and the total biomass for simulations of terra firme forests (ODM: organic dry matter).
Our simulation results reveal a sensitive response of the biomass loss rate
to increased stem mortality intensities (Fig. 3a). At higher stem mortality
levels, higher biomass loss with greater variance emerged and the biomass
loss rate was on average greater than the stem mortality rate (see Fig. S4).
At the level with the highest stem mortality, a peak in the biomass loss
rates occurred at approximately 0.12 yr
Simulation results of the
Furthermore, we analysed how the stem mortality level affected the time needed to reach equilibrium (Fig. 4b). GPP responded particularly sensitively and inversely proportionally to the stem mortality rate, showing a strong decrease with rising stem mortality levels. In contrast, other forest attributes, such as biomass and NPP, had altogether shorter equilibrium times than that of GPP, responding inversely proportionally to the stem mortality level.
Finally, we evaluated the effect of increasing stem mortality rates on the
turnover time of biomass (Eq. 5) in the forest stands while taking forest
succession into account (Fig. 4c). The biomass turnover time
Influence of different mortality levels in simulated terra firme
forests on
In a further analysis, we assessed how biomass loss rates can be derived
from different proxy variables, such as the mean forest height and LAI.
Including forests at different successional states, we tested the
relationships between several single forest attributes and the rate of
biomass loss but did not find distinct relationships (Fig. 5a–c; Table S3: regression model types 3–7). The biomass loss rates showed a widely
scattered range of values and thus unclear relationships to all single
forest attributes during the early successional stage (forest age
Dependence of biomass loss rates from the single attributes
Figure 5d illustrates a three-dimensional relationship between LAI, forest
height, and the rate of biomass loss. Only when combined in a multiple
linear regression model did the LAI (
The one-to-one comparisons of biomass loss rates for the simulated forest stands estimated by the multiple linear regression model (see Eq. 7) versus those simulated within the dynamic forest model fit well (Fig. 6). Other forest attributes, such as GPP and NPP, were not included in the multiple linear regression model because they did not improve the estimation of the biomass loss rates substantially (Table S3).
One-to-one density plot of biomass loss rates simulated by the
dynamic forest model versus biomass loss rates estimated using a multiple
linear regression model, with the forest height and leaf area index as proxy
variables (Eq. 7 and Table S3). The dashed line shows the line of perfect
fit. Each dot represents a forest stand with a unique forest structure
(i.e. tree size distribution and functional species composition), while the
colours represent the density distributions of the combinations. The black
solid line indicates the mean deviation of the biomass loss rate simulated
with the forest model from the estimated ones (
By combining simulated forest states with the maps of LAI and forest height
obtained via remote sensing (Myneni et al.,
2015; Simard et al., 2011), we derived a biomass loss map for terra firme
forests of French Guiana (Fig. 7a). Based on this sample map, we obtained a
mean biomass loss rate of 0.030 yr
In this study, we analysed dynamics in tropical forests in relation to stem mortality. We demonstrated that most of the analysed forest stand attributes (biomass, forest height, LAI, GPP, biomass loss rate, QMD) had specific responses during succession. Moreover, we showed that the rate of biomass loss is strongly affected by succession dynamics as well as by the stem mortality rate. The period until the stand's equilibrium was reached differed in duration among each simulation scenario. Additionally, the mean turnover time of biomass, i.e. the reciprocal value of biomass loss rate (Eq. 5), varied considerably.
There were multiple reasons for the unique succession patterns of each forest attribute. Succession dynamics are influenced by assimilation rates (e.g. photosynthesis rates, light requirements) and physiognomic characteristics (e.g. maximum stem diameter increment rates, maximum heights, and wood densities), both of which are specific to each species group (Hiltner et al., 2018). Functional traits are crucial in simulations of the succession dynamics in forests because they determine the competitiveness of species groups (Fischer et al., 2018; Rüger et al., 2020).
The relationship between successional stages and stem mortality rate has been investigated in empirical studies to estimate mortality in tropical forests (Aubry-Kientz et al., 2013; Chambers et al., 2013; Doughty et al., 2015; Holzwarth et al., 2013). Aubry-Kientz et al. (2013) introduced a method that estimated the stem mortality probability of terra firme forests at Paracou. Similar to our results, they found that the stem mortality probability depends on the successional stages of the forests as well as on the functional traits of species, such as the specific leaf area, wood density, stem diameter increment, and potential height.
Interestingly, we observed similar NPP values at different stem mortality levels for forests in equilibrium. Erb et al. (2016) argued that the NPP of vegetation is effectively independent of the stem mortality rate, which is supported by our results. The observed stability of NPP under different disturbance regimes can be explained by shifts within the functional species compositions and tree size distributions. Pioneer species, which typically have lower wood densities (Chave et al., 2009; Zanne et al., 2009) and lower potential heights than those of slow-growing climax and intermediate species (Hiltner et al., 2018), store less carbon in their living biomasses. Since pioneer species grow faster, they can bind as much carbon per time as slow-growing climax species. Therefore, at the forest stand level, higher stem mortality rates result in similar NPP values as those observed with lower stem mortality rates, although the individual trees show different growth behaviours. Our simulation results show how the carbon storage of forests in equilibrium changes across different levels of stem mortality rates, despite constant levels of NPP. Instead, our findings indicate that carbon storage depends on the functional species composition. At high stem mortality rates (e.g. a high-impact scenario), more pioneer trees of a younger age were present in the forest stands. Thus, to achieve a high forest carbon storage capacity, there is a trade-off between large, old, and less productive trees (e.g. climax species) and smaller, younger, and more productive trees (e.g. pioneer species).
One of the main findings of this study is that the simulated biomass loss rates of terra firme forests can be estimated using multiple linear relationships among several forest attributes. The premise was that all forest attributes used could be provided by remote sensing and could give information about the forest structure and productivity. We recognized the relationship between biomass loss rates to LAI and forest height when fitting many different statistical models with different simulated forest attributes (Table S3, Fig. S15). If stem mortality rates increased, this led to a higher biomass loss rate. However, it is impossible to directly infer stem mortality rates from biomass loss rates because forest structural state variables differed for each simulated forest stand, depending on its successional stage (Bohn and Huth, 2017; Rödig et al., 2017). For example, the stem number distribution of dying trees is not evenly distributed across tree size classes (Aubry-Kientz et al., 2013; Holzwarth et al., 2013; Muller-Landau et al., 2006; Rowland et al., 2015).
In our approach for identifying appropriate forest attributes to infer biomass loss rates, we considered results from empirical studies that have investigated stem mortality in tropical forests (Aubry-Kientz et al., 2013; Esquivel-Muelbert et al., 2019; Stovall et al., 2019). Esquivel-Muelbert et al. (2020) investigated the stem mortality of the Amazon by using empirical data to show that stem diameter growth rate and tree size are strong predictors. Fast-growing species with low wood densities are at a higher risk of mortality, whereas the effect of tree size varies. Aubry-Kientz et al. (2013) used functional traits, such as potential tree height and specific leaf area, to estimate the probability of stem mortality. Based on large-scale remote sensing observations, tree height was identified as an important predictor of stem mortality during drought, with large trees having twice the mortality rate of small trees, while environmental drivers (i.e. temperature, soil water, and competition) controlled the intensity of the height–mortality relationship (Stovall et al., 2019). The results of these studies underline the importance of productivity (e.g. increment rates and tree size), biomass, and functional characteristics (e.g. wood densities, potential stem diameter increment rates, leaf areas, and potential tree heights) of trees or tree species in the context of stem mortality. In our forest model, such characteristics are included in the derivation of stem mortality rates of specific PFTs (see Tables S1, S2). In forest gap models, forest structural state variables, such as the stem number distribution, tree size distribution, and functional species composition, of the dying trees emerge rather than being specified as input parameters (Botkin et al., 1972; Bugmann, 2001; Shugart, 2002). This fact is a useful model behaviour for estimating the biomass loss rate of simulated forest stands and, moreover, holds true for the derivation of other forest attributes, which we considered when fitting our regression model. Besides the LAI and forest height, we tested GPP, NPP, and biomass as proxy variables for the rate of biomass loss. On the forest stand level, however, these variables did not improve the performance of the multiple linear regression model substantially. These results suggest using forest height and LAI as proxy variables to estimate the biomass loss rates of forest stands. Despite the simplicity of the multiple linear regression model, meaning that we included only two proxy variables, its statistical performance proved to be robust (see Eq. 6; Table S3, Figs. S6, S7, S8, S9). Thus, it was possible to derive biomass loss rates from LAI and forest height with simulated data for forests in different successional states. It was important that the signs of the regression coefficients of our linear model plausibly reflected the relationships that were observed in the field. In the regression model, forest height was directly proportional, and LAI was indirectly proportional to the biomass loss rates of the forest stands. For example, tall forests with low LAI values resulted in high biomass loss rates (see Fig. S8). We would like to note that to further improve the regression model (e.g. further minimize the residual's remaining trend), additional proxy variables could be included, non-linear components can be acknowledged, and spatially variable effects of environmental factors on simulated forest states may be investigated in more detail. In this study, we tested non-linear statistical methods (see Fig. S15) and various forest attributes available as remote sensing products as potential proxy variables for the statistical models (see Tables S3, S4). We decided to use the simplest possible linear regression model (in terms of the number of included proxies and interpretability of the model equation) that estimated biomass loss rates best.
Using a forest model to derive the relationships among different forest attributes has several advantages. First, the simulated LAI and forest height data were generated mechanistically, integrating a broad spectrum of information about forest dynamics and successional states emerging from different physiological processes. This can lead to a lower level of noise in the simulation data compared to that in the observed field data. Nevertheless, forest models also include stochastic processes, including stem mortality rates and establishment (Bugmann, 2001; Fischer et al., 2016; Hiltner et al., 2018; Shugart, 2002). By using plant functional types to simulate forest dynamics, we reduced the possible uncertainties in species traits. Simplifications allow for a transferability of the regression analysis to forests with similar characteristics and succession states. These simplifications also enabled the estimation of the biomass loss rates of terra firme forests across the entirety of French Guiana. With the approach pursued here, it might be possible to derive regression models for estimating biomass loss rates in other locations worldwide. Forest model simulation results contain structural information about the conditions of forests in different successional states, allowing the data to be used as training data for the development of statistical regression models. Whether LAI and forest height are also suitable as proxy variables for the biomass loss rates of other forest types remains to be investigated.
We combined remote sensing maps of forest height (Simard et al., 2011) and LAI (Myneni et al., 2015) with forest modelling to derive a sample map of biomass loss rates in French Guiana. In doing so, we presented an innovative approach for estimating biomass loss rates in tropical forests. A comparison of estimated biomass loss rates with census-based values for two sites showed reasonable similarity, although in perspective, it would be important to further validate such maps using more field data (not available to us at present). In another comparison of biomass loss rates obtained for French Guiana with census-based values for the entire Guiana Shield (i.e. French Guiana, Suriname, Guyana, northern Brazil, eastern Venezuela; Johnson et al., 2016), our estimate is about 50 % higher, though it is noteworthy that Johnson et al. (2016) estimated the rate of biomass loss for the entire Guiana Shield, with higher values on average in French Guiana. Capabilities for improved projections of biomass loss rates are indispensable in the context of improved estimates of the role of tropical forests in the global carbon cycle (Anderegg et al., 2020; Friedlingstein et al., 2019; Friend et al., 2007; IPCC, 2014). Remote sensing by airborne and satellite-based instruments provides large-scale data on forests, such as the forest height (Simard et al., 2011) and LAI (Myneni et al., 2015). However, remote sensors can record measurements only at certain time points; hence, the successional stages of forest variables are uncertain in remotely sensed data. Such forest dynamics can be simulated by individual-based, dynamic forest models. A combination of remote sensing data and forest models therefore has the potential to improve predictions of large-scale ecosystem dynamics (Plummer, 2000; Shugart et al., 2015).
Forests can be in different successional stages due to disturbances that influence forest height and LAI (Dubayah et al., 2010; Kim et al., 2017). In the forest height and LAI maps, disturbed regions can be detected visually (see Fig. S5); these regions have been identified as disturbed areas in other studies (Asner and Alencar, 2010; Piponiot et al., 2016a; Stach et al., 2009). Such areas include disturbed areas in the floodplains of lakes and rivers, along the coast, near roads and settlements, and in the secondary forests of French Guiana, where the forest height and the crown coverage are, on average, lower than that in primary forests (Piponiot et al., 2016a; Stach et al., 2009; forest height map from Simard et al., 2011, in Fig. S5).
Remotely sensed products often include uncertainties. In this study, we demonstrated the sensitivity of the sample biomass loss map to variations in the LAI and forest height maps (Figs. 7d, S14). Accuracy of the input remote sensing data is beneficial.
Balanced deviations of LAI and forest height (e.g. LAI
Information on the carbon balance of forests is important for quantifying
the biomass accumulation rates of trees. Various studies have estimated the
turnover time of biomass, which we defined here as the reciprocal value of
the rate of biomass loss, in forests worldwide
(Carvalhais et
al., 2014; Erb et al., 2016; Pugh et al., 2019). Carvalhais et al. (2014)
were the first to estimate biomass turnover times for forests in equilibrium
from biomass and GPP (see Eq. 4:
Erb et al. (2016) observed decreases caused by land use in the biomass turnover time. They found turnover times of 20 to 30 years for the French Guiana region, which are similar to our results (Fig. 4c). Pugh et al. (2019) showed that stand-replacing disturbances also shortened the biomass turnover times. We found that the biomass turnover time is strongly affected by succession dynamics and stem mortality rates. For our full simulation dataset, we found a mean biomass turnover time of 40 years (standard deviation of 20 years; Fig. S13). We derived an alternative framework to estimate the turnover time from biomass loss rates. This framework allows both turnover time and rate of biomass loss to be modelled in a simple way, considering succession dynamics and disturbances due to stem mortality.
Our simulation results revealed complex relationships between stem mortality rate and biomass loss rate. The growth stage of a tree evidently has an effect on stem mortality, which often results in a U-shaped relationship of stem mortality as a function of the tree size distribution in a forest (Aubry-Kientz et al., 2013; Holzwarth et al., 2013; Muller-Landau et al., 2006). With regard to tree age, it is more likely that the youngest and oldest trees will die (Aubry-Kientz et al., 2013; Rüger et al., 2011) due to intense competition for light and space between the juvenile trees in the understorey and the senescence of the old trees in the canopy layer. Such mortality processes are often represented in forest models (Bugmann et al., 2019). Although empirical mortality algorithms which mechanistically describe the main causes of stem mortality and their effects on entire ecosystems (e.g. self-thinning, death of trees by crushing, and growth-dependent mortality) have already been developed, other causes of stem mortality with unclear signals are often summarized as stochastic processes (Bugmann et al., 2019; Hülsmann et al., 2017, 2018). In our study, biomass loss rates at the stand level arose from different mortality processes that occurred at the tree level (competition due to crowding, death of other trees by crushing, growth dependency, gap formation, and stochastic stem mortality). Compared to the U-shaped stem mortality distribution across stem diameter classes, the biomass loss rates of a forest stand depended in more complex ways on the functional species composition and the levels of carbon fluxes (GPP and NPP). We analysed the relationships between different levels of stem mortality rate with biomass loss rate, GPP, NPP, and biomass stock. It would be interesting to explore simulation results for different modes of stem mortality in future studies.
In our study, the effects of disturbances were represented in a simplified manner by modifying the mortality rates of tree species. We analysed the effects of permanently increasing the stem mortality rates in the studied forests. However, it is also necessary to consider the effects of discrete or continuously changing disturbance patterns (e.g. Barlow et al., 2003; Brando et al., 2014; Chambers et al., 2009, 2013; Doughty et al., 2015; Holzwarth et al., 2013; Magnabosco Marra et al., 2016; Marra et al., 2014; McDowell et al., 2018; Negrón-Juárez et al., 2010, 2017; Nepstad et al., 2007; Phillips and Brienen, 2017; Slik et al., 2010; Stovall et al., 2019; Wright et al., 2015). The impacts of single, discrete disturbance events (e.g. selective logging) on the dynamics of terra firme forests were studied by Hiltner et al. (2018). A follow-up study investigated the impacts of repeated logging events under continuously changing air temperatures and precipitation rates (Hiltner et al., 2021). If additional effects, such as climate change and forest management, were added to the dynamic forest model's simulations of the present study, the reasons for biomass losses could be determined more accurately. Assessing this aspect would be interesting for future studies in which the methodology presented here can be applied.
It was also found that the temporal patterns of establishing trees can change after disturbances such as modifications to the seed mortality of specific tree species, as such changes influence the competitive processes of trees within communities (Dantas de Paula et al., 2018). Here, we did not consider the influences of stem mortality rates on establishment processes, though this factor should be considered in future studies.
Regarding the mapping of the biomass loss rates in French Guiana, there are four important aspects. First, it is important to verify the quality of the forest model parameterization with field data as was done for biomass loss rates in this study and by Hiltner et al. (2018, 2021), who analysed biomass dynamics, tree size distribution, and functional species composition by comparing model results with data from forest inventories in French Guiana. The amount of available “ground truth data” was small, so a comprehensive validation of the simulation results and the biomass loss map was not possible. In future studies, the addition of more data sources will allow for more extensive validation of the study results. Second, a multiple linear regression model predicting biomass loss rates can be valid only for a certain type of forest. In mapping biomass loss rates at the country level, we assumed the predominance of a similar type of forest: the terra firme forests in French Guiana (Hammond, 2005). For this forest type, Stach et al. (2009) calculated a forest cover of 95 % of the country's land area. Third, site parameters across entire landscapes can be heterogeneous, affecting forest dynamics and structure. Various studies demonstrated that natural environmental factors, such as soil properties (Rödig et al., 2017; Soong et al., 2020), relief (Guitet et al., 2018), and climatic variations (Rödig et al., 2017; Wagner et al., 2012), as well as the silvicultural history (Hiltner et al., 2018; Piponiot et al., 2016b, 2019), can affect the succession dynamics and states of tropical forests. In this study, such spatially heterogeneous environmental influences on forest dynamics in terra firme forests are indirectly considered in the forest model and the regression model via stochastic stem mortality. Some of these environmental factors, which vary at the regional level, can be considered in future studies by including further processes (see Tables S1, S2, Fischer et al., 2016). Examples are (1) implementing the effects of forest management and fire, (2) taking into account the effects of weather variables such as temperature, rainfall variability, and solar radiation, and (3) varying the relationships describing tree geometry in space and time. Moreover, species diversity could play an important role, which was aggregated in this study using the plant functional type approach (Maréchaux and Chave, 2017). In further investigations, it is recommended that climatic and topographic effects or short-term disturbance events and forest management be implemented to further improve the approach developed here. Finally, changes in climatic conditions and tree coverage have an impact on forest height, LAI, and subsequently biomass loss rates. For example, drought, uprooting due to storms or flooding, forest fire, insect calamity, and forest management can be possible drivers of variability in the LAI. Furthermore, forest height can vary, e.g. due to uprooting from storms and flooding, fire, and forest management. Those environmental drivers may also interact with each other. The effects of tree coverage and climate as well as their importance for driving the maps of forest height and LAI and subsequently the estimations of biomass loss rates should be explored in follow-up studies.
Here, we developed a framework for estimating biomass loss rates in tropical forests. We analysed the effects of stem mortality rate and its relation to forest productivity, forest structure, and biomass based on the example of terra firme forests in French Guiana. By quantifying such effects through simulation experiments, it was possible to derive complex relationships between biomass loss rates and other forest attributes. Our approach revealed the strong influences of the succession states and stem mortality rates on the biomass loss rates of forests.
We also linked individual-based forest modelling with remote sensing so that an estimation of biomass loss rates due to stem mortality was feasible. The resulting sample map of biomass loss predicted that biomass is dying at a faster rate in the central, southern, and eastern regions than in the northern parts of French Guiana. The forest areas in the north and northeast are used for timber production, agricultural activities, and housing (Bovolo et al., 2018; Stach et al., 2009), whereas the forest areas in the south are predominantly natural rainforests (Hammond, 2005).
The approach we developed here can be easily transferred to other forest biomes (e.g. boreal and temperate forests) using forest models that capture biome-specific forest dynamics and available remote sensing products. Estimating the spatiotemporal distribution of forest biomass loss rates has recently been identified as particularly relevant for the monitoring of mortality hotspots (Hartmann et al., 2018). Moreover, improved estimations of the turnover times of carbon in forest stands have been made possible so that uncertainties in the global carbon cycle (Friend et al., 2014) can be reduced.
The FORMIND parameterization (Hiltner et al., 2018) and the source code of
FORMIND can be downloaded for free on the following website:
The supplement related to this article is available online at:
UH, AH, and RF conceived and designed the experiments; UH acquired and managed the data; UH performed the simulations; UH, AH, and RF contributed to analysis and discussion; UH wrote the paper; UH, AH, and RF reviewed the paper.
The contact author has declared that neither they nor their co-authors have any competing interests.
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We would like to sincerely thank Achim Bräuning, Harald Bugmann, and Nuno Carvalhais for fruitful discussions of the simulation results and the paper. Ulrike Hiltner would like to thank Andreas Keberer for his remarkable assistance. The authors would like to thank both the reviewers, Thomas Pugh and the anonymous second reviewer, for their constructive comments during the revision process of the paper.
Ulrike Hiltner was funded by the German Federal Environmental Foundation – DBU (AZ 20015/398) and the programme “Realization of Equal Opportunities for Women in Research and Teaching” – FFL of Friedrich-Alexander University Erlangen–Nuremberg. The article processing charges for this open-access publication were covered by the Helmholtz Centre for Environmental Research – UFZ.
This paper was edited by Ben Bond-Lamberty and reviewed by Thomas Pugh and one anonymous referee.