Aboveground forest biomass varies across continents, ecological zones and successional stages: refined IPCC default values for tropical and subtropical forests

We compiled AGB data from plots in natural forests in the tropics and subtropics from (a) data from published studies, including studies from research networks and global databases; and (b) NFIs. We included plots from traceable sources only, thus with a literature reference and known geographical location (coordinates). Large-scale research networks that monitor the structure and dynamics of tropical old-growth (Malhi et al 2002, Lewis et al 2013, Anderson-Teixeira et al 2015, ForestPlots.net et al 2021) and degraded/logged forests (e.g. Rutishauser et al 2015), or that measure forest recovery on abandoned agricultural land (e.g. Poorter et al 2016), generally collect detailed information on the disturbance history and/or stand age of the plots. For plots that are established as part of NFIs, in a consistent way across a wide range of environmental conditions, such information is generally lacking. We particularly targeted plot data from NFIs in Africa and Asia, because for North and South America (hereafter the Americas) more plot data from published studies were available.

Census years of the included plots varied widely, but most measurements (96% of all plots) were done after the year 2000. Although the census year varied, by including recent data and by distinguishing successional stages, our AGB estimates represent average current AGB levels in tropical and subtropical forests. If a plot was repeatedly measured, only the most recent census was used. The minimum size of included trees was at least 10 cm diameter at breast height (dbh), or above deformity in the case of buttresses, with trees identified to the finest taxonomic resolution possible. For regrowing forests, the minimum dbh was generally 5 cm (Poorter et al 2016, Anderson-Teixeira et al 2018), because smaller trees comprise a substantial part of AGB (Hughes et al 1999). This means that we may have slightly underestimated AGB in old-growth forests. Nevertheless, trees between 5 and 10 cm dbh contribute little to AGB in old-growth forests, with a 1.178 times higher AGB for tropical dry forests when including trees between 5 and 10 cm dbh, but only a 1.033 and 1.020 times higher AGB for tropical moist and wet forests, respectively (Poorter et al 2015). We included aboveground live tree biomass, thus trees smaller than the minimum dbh, shrubs, lianas, herbs, necromass and belowground biomass were not included. For one dataset, dead trees were included as well, but this dataset comprised less than 1% of the total number of plots that was included from NFIs.

Most studies estimated tree biomass based on dbh, wood density, and in some cases tree height, based on general allometric equations for tropical trees (Chave et al 2005, 2014, Feldpausch et al 2012). If aboveground carbon was reported instead of AGB, the aboveground carbon value was divided by the IPCC conversion factor of 0.47 for conversion to AGB. If another conversion factor was used in the original source, we used the original conversion factor.

We calculated default AGB values per continent (Africa, the Americas, Asia), per global ecological zone (FAO 2012), and successional stage. Global ecological zones were defined as broad, climatic forest types, with an exception for mountain systems (here referred to as montane forests) that were classified based on altitude only (FAO 2012; appendix A (available online at stacks.iop.org/ERL/17/014047/mmedia)). Five global ecological zones were included for the tropics (rainforests, moist forests, dry forests, shrublands, montane forests), and four for the subtropics (humid forests, dry forests, steppe, montane forests). Three successional stages were distinguished: (a) old-growth forests, which we defined as forests with no record of human disturbance in the past 100 years (Anderson-Teixeira et al 2016, Poorter et al 2016), thus secondary forests >100 years old were regarded as old-growth forests; (b) older secondary forests >20 and ⩽100 years old, including forests regrowing on abandoned agricultural land, and degraded (including logged) forests; and (c) young secondary forests ⩽20 years old. Plots with information on disturbance history were assigned to a successional stage based on stand age for secondary forests, or disturbance history for degraded/logged forests. If plots lacked information on disturbance history, we determined the successional stage based on remote-sensing products (Tyukavina et al 2016, Potapov et al 2017, appendix B).

Default values were calculated per continent, ecological zone, and successional stage if plot data were available. The methodology depended on the successional stage, and the number of available plots (figure 1). For old-growth forests, default values were calculated as the weighted mean across all plots, weighted by the square root of plot size, to account for the large variation in plot size (Brienen et al 2015, Rutishauser et al 2015, Requena Suarez et al 2019). Only old-growth plots with information on disturbance history were included, to guarantee that they were not subject to anthropogenic disturbance. Default values for old-growth forest were calculated if at least ten plots with information on plot size were available per continent and ecological zone (figure 1(a)).

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For secondary forests in the Americas, the size of most individual plots was unknown. Therefore, default values for secondary forests were expressed as average AGB per 0.5° grid cell, instead of a plot-size weighted mean. As such, we accounted for spatial clustering of plots, and avoided overrepresentation of locations with many plots. We used 0.5° grid cells, because one 0.5° grid cell generally included one research site with chronosequence plots for secondary forests (Poorter et al 2016). Per continent, ecological zone, and successional stage, we first calculated average AGB per grid cell, and then averaged these to obtain the default value. If plot data with information on disturbance history were available for at least five grid cells for both young and older secondary forests, separate default values for both young and older secondary forests were calculated (figure 1(b)). If AGB data were available for secondary forest plots for at least five grid cells, with or without information on disturbance history, we calculated default values for all secondary forests together (figure 1(b)).

For continents and ecological zones for which not sufficient plot data were available to distinguish successional stages, we calculated a single default value per continent and ecological zone to represent all successional stages, if plot data (with or without information on disturbance history) were available for at least five grid cells (figure 1(c)). For combinations of continent and ecological zone for which no plot data were available, we calculated AGB default values based on an AGB map (figure 1(c), appendix B). We made three exceptions to our general methodology. First, for tropical dry forests in Africa, we did not distinguish successional stages, although plot data were available per successional stage. These disturbance-adapted ecosystems are subject to chronic and acute disturbances (natural and anthropogenic; McNicol et al 2018), thus successional stages cannot be meaningfully distinguished. Second, for tropical dry forests in Asia, we also included a default value for all successional stages together although plot data were available for three grid cells only, because we preferred to use plot data (36 plots) over an estimate from a remote sensing-derived biomass map. Third, for old-growth subtropical forests in Asia, we included a grid cell mean instead of a plot-size weighted mean (table 1), because plot sizes were unknown.

In total, we included 2318 plots for the Americas, 22 279 plots for Africa, and 1291 plots for Asia (figure 2). A total of 3880 plots with information on disturbance history was included, with an average plot size of 2.9 ha (range: 0.001–42 ha). Plots were typically one hectare or larger for old-growth forests (Sullivan et al 2017) and logged forests (Rutishauser et al 2015), and on average 0.1 ha for regrowing forests (Poorter et al 2016). Plot-level AGB was included, but for some datasets AGB estimates were based on multiple plots at the same location (Slik et al 2015, Anderson-Teixeira et al 2018). In total, 22 008 plots without information on disturbance history were included. These, generally small, plots were mostly from NFIs, and had an average plot size of 0.1 ha (range: 0.01–1.85 ha). Overall, most plots were from tropical forests (25 681 plots in total), while only 207 plots were included in subtropical forests (figure 2).

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We found large variation in AGB across continents, ecological zones, and successional stages. Our refined default values generally reflect the large-scale climatic gradients in the tropics. Forest biomass generally increases from drier to wetter forests because of a longer growing season and higher water availability (Poorter et al 2017, Sullivan et al 2020), which was also reflected in the refined default values for tropical old-growth forests, as they increased from dry, to moist, to rainforests (table 1). In addition, tropical forest biomass decreases with elevation (Leuschner et al 2007, Girardin et al 2010). Our refined default estimates were partly consistent with this pattern, as the default value for tropical montane forests was lower than the one for tropical rainforests in the Americas (table 1).

Where possible, we provided refined AGB default values per successional stage, differentiating between young and older secondary and old-growth forests. For all continents and ecological zones for which we distinguished between secondary and old-growth forests, AGB was lower in secondary forests than in old-growth forests, and increased from young to older secondary forests as forests regrow and vegetation biomass builds up, in agreement with ecological first principles and previous analyses for tropical forests (Martin et al 2013, Anderson-Teixeira et al 2016, Poorter et al 2016, N'Guessan et al 2019).

Our refined default AGB values were generally consistent with the IPCC 2006 default values for old-growth tropical rainforests and tropical moist forests, but were lower than previous values for secondary forests and for all successional stages combined, with the strongest deviations for young secondary forests (figure 4). These results suggest that IPCC 2006 default values for at least tropical rainforests and moist forests were largely defined based on data from old-growth forests (Langner et al 2014), and stress the importance of differentiation by successional stage. By specifying separate default values for young secondary, older secondary, and old-growth forests, the reality of 21st century tropical forests is better reflected. In Latin America, for example, secondary forests account for 41% of the forest area (Chazdon et al 2016), and are expected to expand in area.

Our refined default values provide a more realistic estimate of forest carbon stocks when only IPCC default AGB and AGB change values in combination with information on land cover can be applied for carbon accounting (tier 1 level). Using IPCC 2006 values almost certainly led to overestimated carbon stocks for many countries, because they reflected values for old-growth forests. Moreover, IPCC default values of net rates of AGB change have also been refined recently, with overall rates 30% lower than the 2006 default values (Requena Suarez et al 2019). Taken together, refined estimates for both AGB and net AGB change will improve national estimates of forest carbon stocks and fluxes.

To be able to apply the refined default values, countries need to distinguish successional stages per ecological zone. For countries that do not have information on the extent of young, older, and old-growth forests per ecological zone, for example from a NFI, we recommend to largely follow the approach of Harris et al (2021) for delimiting these successional stages based on remote-sensing products (appendix C).

In our effort, we included the available, high-quality data to provide refined default values, but data gaps remain (figure 5). With the refined default values, we provided the SD of the default values as a measure of uncertainty, which indicates AGB variation among plots, but does not include the uncertainty of the plot-level AGB estimates. We included both plots from research networks and NFIs. While NFI plots are usually established following a probability sampling design (e.g. Tomppo et al 2014), the design of a research plot network depends on the specific objectives of the research project, and may not cover spatial variation in environmental conditions, or plots may, for example, be established in locations that are easily accessible. We preferably included data from research networks for which detailed information on disturbance history was included, but for some default values solely based on NFI plots, the disturbance history was estimated based on remote-sensing products. In addition, the variation in allometric equations used for the biomass estimates may have introduced additional uncertainty, as biomass estimates vary with the allometric equation used (Chave et al 2004).

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We recommend future refinement by incorporating AGB data for ecological zones and successional stages that are currently undersampled, or for which data from plots with information on disturbance history were not available. The availability of AGB data from forest plots strongly varied across continents and ecological zones, with data gaps for secondary forests in Africa and Asia, and for subtropical forests in general (figure 2, table 1). Overall, availability of plot data with information on disturbance history was low for the drier areas in Africa, parts of Asia, and subtropical forests on all continents (figure 5). This may be because most dry forests in Africa are commonly used (McNicol et al 2018), which complicates determining their disturbance history. Nevertheless, targeting collection of data from plots, along with detailed information on their disturbance history in these areas should be a priority, although on a per-area basis tropical moist and wet forests in Africa and the Americas remain also relatively undersampled (figure 5). Establishing permanent forest plots is essential, for accurate estimation of both AGB and AGB change over time, for all successional stages. For future refinement, we also recommend distinguishing successional stages in more detail, as the three included stages represent broad categories. Particularly, older secondary forests could be divided into degraded/logged forests and regenerating forests after complete removal of vegetation (e.g. shifting cultivation), because they are subject to distinct levels of disturbance with differential impacts on carbon stocks (Berenguer et al 2014).

Our refined default values are well-suited for tier 1 level reporting, along with the refined IPCC default values for net biomass change (Requena Suarez et al 2019), but for tier 2 and 3 level reporting other approaches would be needed for more accurate national estimation of carbon stocks. Within ecological zones, there was large variation in AGB (as indicated by the large SD), probably because of large variation in environmental conditions within each ecological zone. We recommend accounting for variation in environmental conditions within ecological zones to increase precision in AGB estimates, as several climatic and edaphic factors influence tropical forest AGB. In old-growth tropical forests, AGB varies with climate and soil fertility (e.g. Quesada et al 2012, Poorter et al 2017, Sullivan et al 2020). For regrowing forests, AGB could be modeled as a function of stand age and climate, as these drive much of the variation in AGB in regrowing forests (Poorter et al 2016, Cook-Patton et al 2020), and soil fertility. In regrowing forests, including dry, moist, and wet tropical forests, in Latin America, such an approach has been applied on a continental scale (Chazdon et al 2016), but for Africa and Asia such information is lacking. Moreover, effects of forest cover in the landscape matrix (Bonner et al 2013) and previous land-use intensity (Jakovac et al 2021) on AGB in regrowing forests should also be considered. Similar approaches could be explored for degraded/logged forests, although quantifying forest disturbance levels for large areas remains challenging.

AGB maps based on remotely-sensed data are increasingly becoming available for the tropics, but pixel-level uncertainty is still very high, particularly for high-biomass tropical forests (Herold et al 2019), which limits their use in GHG inventories. Integrating NFI data with remote-sensing products may be an alternative approach for monitoring impacts of land-use change on AGB over large areas (Requena Suarez et al 2021), as NFIs have large geographical coverage, and should be further explored in the future (Bustamante et al 2016, Herold et al 2019).