Dataset Information

Estimating aboveground net biomass change for tropical and subtropical forests: Refinement of IPCC default rates using forest plot data.

ABSTRACT: As countries advance in greenhouse gas (GHG) accounting for climate change mitigation, consistent estimates of aboveground net biomass change (?AGB) are needed. Countries with limited forest monitoring capabilities in the tropics and subtropics rely on IPCC 2006 default ?AGB rates, which are values per ecological zone, per continent. Similarly, research into forest biomass change at a large scale also makes use of these rates. IPCC 2006 default rates come from a handful of studies, provide no uncertainty indications and do not distinguish between older secondary forests and old-growth forests. As part of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, we incorporate ?AGB data available from 2006 onwards, comprising 176 chronosequences in secondary forests and 536 permanent plots in old-growth and managed/logged forests located in 42 countries in Africa, North and South America and Asia. We generated ?AGB rate estimates for younger secondary forests (?20 years), older secondary forests (>20 years and up to 100 years) and old-growth forests, and accounted for uncertainties in our estimates. In tropical rainforests, for which data availability was the highest, our ?AGB rate estimates ranged from 3.4 (Asia) to 7.6 (Africa) Mg ha-1 year-1 in younger secondary forests, from 2.3 (North and South America) to 3.5 (Africa) Mg ha-1 year-1 in older secondary forests, and 0.7 (Asia) to 1.3 (Africa) Mg ha-1 year-1 in old-growth forests. We provide a rigorous and traceable refinement of the IPCC 2006 default rates in tropical and subtropical ecological zones, and identify which areas require more research on ?AGB. In this respect, this study should be considered as an important step towards quantifying the role of tropical and subtropical forests as carbon sinks with higher accuracy; our new rates can be used for large-scale GHG accounting by governmental bodies, nongovernmental organizations and in scientific research.

SUBMITTER: Requena Suarez D

PROVIDER: S-EPMC6852081 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Estimating aboveground net biomass change for tropical and subtropical forests: Refinement of IPCC default rates using forest plot data.

Requena Suarez Daniela D Rozendaal Danaë M A DMA De Sy Veronique V Phillips Oliver L OL Alvarez-Dávila Esteban E Anderson-Teixeira Kristina K Araujo-Murakami Alejandro A Arroyo Luzmila L Baker Timothy R TR Bongers Frans F Brienen Roel J W RJW Carter Sarah S Cook-Patton Susan C SC Feldpausch Ted R TR Griscom Bronson W BW Harris Nancy N Hérault Bruno B Honorio Coronado Eurídice N EN Leavitt Sara M SM Lewis Simon L SL Marimon Beatriz S BS Monteagudo Mendoza Abel A Kassi N'dja Justin J N'Guessan Anny Estelle AE Poorter Lourens L Qie Lan L Rutishauser Ervan E Sist Plinio P Sonké Bonaventure B Sullivan Martin J P MJP Vilanova Emilio E Wang Maria M H MMH Martius Christopher C Herold Martin M

Global change biology 20190816 11

As countries advance in greenhouse gas (GHG) accounting for climate change mitigation, consistent estimates of aboveground net biomass change (∆AGB) are needed. Countries with limited forest monitoring capabilities in the tropics and subtropics rely on IPCC 2006 default ∆AGB rates, which are values per ecological zone, per continent. Similarly, research into forest biomass change at a large scale also makes use of these rates. IPCC 2006 default rates come from a handful of studies, provide no un ...[more]

PMID: 31310673

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Project description:BackgroundReliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates.ResultsCorrecting NFI plot data for both small trees and temporal differences between field and remotely sensed measurements reduced the relative error of biomass estimates by 12.2%. Using a machine learning algorithm, Random Forest, with corrected field plot data, backscatter and surface texture from the L-band synthetic aperture radar (PALSAR) installed on the on the Advanced Land Observing Satellite-1 (ALOS), and climatic water deficit data improved the accuracy of the maps obtained in this study as compared to previous studies (R2?=?0.44 vs R2?=?0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R2?=?0.26).ConclusionsThis study reveals that the suggested approach has the potential to improve AGB maps of tropical dry forests and shows predictors of AGB that should be considered in future studies. Our results highlight the importance of using ecological knowledge to correct errors associated with both the plot-level biomass estimates and the mismatch between field and remotely sensed data.

Project description:Tropical forests are biologically diverse and structurally complex ecosystems that can store a large quantity of carbon and support a great variety of plant and animal species. However, tropical forest structure can vary dramatically within seemingly homogeneous landscapes due to subtle changes in topography, soil fertility, species composition and past disturbances. Although numerous studies have reported the effects of field-based stand structure attributes on aboveground biomass (AGB) in tropical forests, the relative effects and contributions of UAV LiDAR-based canopy structure and ground-based stand structural attributes in shaping AGB remain unclear. Here, we hypothesize that mean top-of-canopy height (TCH) enhances AGB directly and indirectly via species richness and horizontal stand structural attributes, but these positive relationships are stronger at a larger spatial scale. We used a combined approach of field inventory and LiDAR-based remote sensing to explore how stand structural attributes (stem abundance, size variation and TCH) and tree species richness affect AGB along an elevational gradient in tropical forests at two spatial scales, i.e., 20 m × 20 m (small scale), and 50 m × 50 m (large scale) in southwest China. Specifically, we used structural equation models to test the proposed hypothesis. We found that TCH, stem size variation and abundance were strongly positively associated with AGB at both spatial scales, in addition to which increasing TCH led to greater AGB indirectly through increased stem size variation. Species richness had negative to negligible influences on AGB, but species richness increased with increasing stem abundance at both spatial scales. Our results suggest that light capture and use, modulated by stand structure, are key to promoting high AGB stocks in tropical forests. Thus, we argue that both horizontal and vertical stand structures are important for shaping AGB, but the relative contributions vary across spatial scales in tropical forests. Importantly, our results highlight the importance of including vertical forest stand attributes for predicting AGB and carbon sequestration that underpins human wellbeing.

Project description:BackgroundLong-term studies of community and population dynamics indicate that abrupt disturbances often catalyse changes in vegetation and carbon stocks. These disturbances include the opening of clearings, rainfall seasonality, and drought, as well as fire and direct human disturbance. Such events may be super-imposed on longer-term trends in disturbance, such as those associated with climate change (heating, drying), as well as resources. Intact neotropical forests have recently experienced increased drought frequency and fire occurrence, on top of pervasive increases in atmospheric CO2 concentrations, but we lack long-term records of responses to such changes especially in the critical transitional areas at the interface of forest and savanna biomes. Here, we present results from 20 years monitoring a valley forest (moist tropical forest outlier) in central Brazil. The forest has experienced multiple drought events and includes plots which have and which have not experienced fire. We focus on how forest structure (stem density and aboveground biomass carbon) and dynamics (stem and biomass mortality and recruitment) have responded to these disturbance regimes.ResultsOverall, the biomass carbon stock increased due to the growth of the trees already present in the forest, without any increase in the overall number of tree stems. Over time, both recruitment and especially mortality of trees tended to increase, and periods of prolonged drought in particular resulted in increased mortality rates of larger trees. This increased mortality was in turn responsible for a decline in aboveground carbon toward the end of the monitoring period.ConclusionProlonged droughts influence the mortality of large trees, leading to a decline in aboveground carbon stocks. Here, and in other neotropical forests, recent droughts are capable of shutting down and reversing biomass carbon sinks. These new results add to evidence that anthropogenic climate changes are already adversely impacting tropical forests.

Project description:Wood density (WD) is a key functional trait related to ecological strategies and ecosystem carbon dynamics. Despite its importance, there is a considerable lack of information on WD in tropical Andean forests, particularly regarding its relationship with forest succession and ecosystem carbon cycling. Here, we quantified WD in 86 upper Andean tree and shrub species in central Colombia, with the aim of determining how WD changes with forest succession and how it is related to productivity. We hypothesized that WD will increase with succession because early successional forests will be colonized by acquisitive species, which typically have low WD, while the shaded understory of older forests should favor higher WD. We measured WD in 481 individuals from 27 shrub and 59 tree species, and quantified aboveground biomass (AGB), canopy height, net primary production (NPP) and species composition and abundance in 14, 400-m2, permanent plots. Mean WD was 0.513 ± 0.114 (g/cm3), with a range between 0.068 and 0.718 (g/cm3). Shrubs had, on average, higher WD (0.552 ± 0.095 g/cm3) than trees (0.488 ± 0.104 g/cm3). Community weighted mean WD (CWMwd) decreased with succession (measured as mean canopy height, AGB, and basal area); CWMwd also decreased with aboveground NPP and stem growth. In contrast, the percentage of NPP attributed to litter and the percent of shrubs in plots increased with CWMwd. Thus, our hypothesis was not supported because early successional forests had higher CWMwd than late successional forests. This was related to a high proportion of shrubs (with high WD) early in succession, which could be a consequence of: 1) a low seed availability of trees due to intense land use in the landscape and/or 2) harsh abiotic conditions early in succession that filter out trees. Forest with high CWMwd had a high %NPP attributed to litter because they were dominated by shrubs, which gain little biomass in their trunks. Our findings highlight the links between WD, succession and carbon cycling (biomass and productivity) in this biodiversity hotspot. Thus, WD is an important trait that can be used to understand upper Andean forest recovery and improve forest restoration and management practices.

Project description:The allocation of the net primary productivity (NPP) of an ecosystem between canopy, woody tissue and fine roots is an important descriptor of the functioning of that ecosystem, and an important feature to correctly represent in terrestrial ecosystem models. Here, we collate and analyse a global dataset of NPP allocation in tropical forests, and compare this with the representation of NPP allocation in 13 terrestrial ecosystem models. On average, the data suggest an equal partitioning of allocation between all three main components (mean 34 ± 6% canopy, 39 ± 10% wood, 27 ± 11% fine roots), but there is substantial site-to-site variation in allocation to woody tissue versus allocation to fine roots. Allocation to canopy (leaves, flowers and fruit) shows much less variance. The mean allocation of the ecosystem models is close to the mean of the data, but the spread is much greater, with several models reporting allocation partitioning outside of the spread of the data. Where all main components of NPP cannot be measured, litterfall is a good predictor of overall NPP (r(2) = 0.83 for linear fit forced through origin), stem growth is a moderate predictor and fine root production a poor predictor. Across sites the major component of variation of allocation is a shifting allocation between wood and fine roots, with allocation to the canopy being a relatively invariant component of total NPP. This suggests the dominant allocation trade-off is a 'fine root versus wood' trade-off, as opposed to the expected 'root-shoot' trade-off; such a trade-off has recently been posited on theoretical grounds for old-growth forest stands. We conclude by discussing the systematic biases in estimates of allocation introduced by missing NPP components, including herbivory, large leaf litter and root exudates production. These biases have a moderate effect on overall carbon allocation estimates, but are smaller than the observed range in allocation values across sites.

Dataset Information

Estimating aboveground net biomass change for tropical and subtropical forests: Refinement of IPCC default rates using forest plot data.

Publications

Estimating aboveground net biomass change for tropical and subtropical forests: Refinement of IPCC default rates using forest plot data.

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