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: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: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.

Project description:Understanding the effects of plant species diversity and trait composition on aboveground biomass is a central focus of ecology and has important implications for biodiversity conservation. However, the simultaneous direct and indirect effects of soil nutrients, species asynchrony, functional trait diversity, and trait composition for explaining the community temporal stability of aboveground biomass remain underrepresented in natural forests. Here, we hypothesized that species asynchrony relative to soil nutrients, functional trait diversity, and trait composition plays a central role in stabilizing the community temporal stability of natural forests. We tested this hypothesis using a structural equation model based on 10-year continuous monitoring data (i.e., three-time repeated forest inventories) in both second-growth and old-growth temperate forests in northeast China. Our results showed that the community temporal stability of aboveground biomass was driven by a strong direct positive effect of species asynchrony in both second-growth and old-growth temperate forests, whereas functional trait diversity and composition (i.e. community-weighted mean of leaf nitrogen content) were of additional importance in an old-growth forest only. Functional trait diversity decreased community-weighted mean of leaf nitrogen content in an old-growth forest, whereas this relationship was non-significant in a second-growth forest. Soil nutrients had non-significant effects on the community temporal stability of both second-growth and old-growth forests. Species asynchrony was the direct determinant of the community temporal stability of aboveground biomass in temperate forests. The direct effect of species asynchrony increased with forest succession, implying that temporal niche differentiation and facilitation increase over time. This study suggests that managing forests with mixtures of both early and late successional species or shade intolerant and tolerant species, not only species diversity, is important for maintaining forest stability in a changing environment. We argue that the species asynchrony effect is crucial to understand the underlying ecological mechanisms for a diversity-biomass relationship in natural forests.

Project description:BACKGROUND:Application of allometric equations for quantifying forests aboveground biomass is a crucial step related to efforts of climate change mitigation. Generalized allometric equations have been applied for estimating biomass and carbon storage of forests. However, adopting a generalized allometric equation to estimate the biomass of different forests generates uncertainty due to environmental variation. Therefore, formulating species-specific allometric equations is important to accurately quantify the biomass. Montane moist forest ecosystem comprises high forest type which is mainly found in the southwestern part of Ethiopia. Yayu Coffee Forest Biosphere Reserve is categorized into Afromontane Rainforest vegetation types in this ecosystem. This study was aimed to formulate species-specific allometric equations for Albizia grandibracteata Tuab. and Trichilia dregeana Sond. using the semi-destructive method. RESULTS:Allometric equations in form of power models were developed for each tree species by evaluating the statistical relationships of total aboveground biomass (TAGB) and dendrometric variables. TAGB was regressed against diameter at breast height (D), total height (H), and wood density (?) individually and in a combination. The allometric equations were selected based on model performance statistics. Equations with the higher coefficient of determination (adj.R2), lower residual standard error (RSE), and low Akaike information criterion (AIC) values were found best fitted. Relationships between TAGB and predictive variables were found statistically significant (p???0.001) for all selected equations. Higher bias was reported related to the application of pan-tropical or generalized allometric equations. CONCLUSIONS:Formulating species-specific allometric equations is found important for accurate tree biomass estimation and quantifying the carbon stock. The developed biomass regression models can be applied as a species-specific equation to the montane moist forest ecosystem of southwestern Ethiopia.

Project description:BackgroundWith the introduction of the Trillion Trees Initiative and similar programs, forests' ability to absorb carbon dioxide is increasingly in the spotlight. Many states have mandates to develop climate action plans, of which forest carbon is an important component, and planners need current information on forest carbon stocks and rates of change at relevant spatial scales. To this end, we examine rates of average annual change in live aboveground tree carbon in different forest type groups and provide state-wide and regional summaries of current live tree carbon stock and rates of change for the forests of the conterminous United States. Forest carbon summaries are presented in a format designed to meet the needs of managers, policymakers, and others requiring current estimates of aboveground live tree carbon at state and regional scales.ResultsRegional average aboveground live tree carbon stocks (represented on a per area basis) are generally between 40 and 75 tC/ha but range from 12.8 tC/ha in the Great Plains to 130 tC/ha in the Pacific Northwest West (west-side of Cascades). Regional average annual change in live aboveground tree carbon varies from a low of - 0.18 mtC/ha/y in the Rocky Mountain South to a high value of 1.74 mtC/ha/y in Pacific Northwest West. For individual states, carbon per unit area varies widely, from a low of 11.9 tC/ha in Nevada to a high of 96.4 tC/ha in Washington, with half the states falling between 50 and 75 tC/ha. Rates of average annual change in live aboveground tree carbon vary from a high of 1.82 tC/ha/y in Mississippi to a low of - 0.47 tC/ha/y in Colorado.ConclusionsAboveground live tree carbon stocks and rates of average annual change vary by forest type within regions. While softwood forest types currently exhibit a higher rate of increase in the amount of carbon in aboveground live tree biomass, the current standing stock of carbon per unit area does not consistently follow this pattern. For this reason, we recommend computing and considering both measures -standing stock and average annual change-of carbon storage. The relative importance of each component will depend on management and policy objectives and the time frame related to those objectives. Harvesting and natural disturbance also affect forest carbon stock and change and may need to be considered if developing projections of potential carbon storage.

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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