Improved allometric models to estimate the aboveground biomass of tropical trees
Terrestrial carbon stock mapping is important for the successful implementation of climate change mitigation policies. Its accuracy depends on the availability of reliable allometric models to infer oven‐dry aboveground biomass of trees from census data. The degree of uncertainty associated with pre...
Saved in:
| Published in: | Global change biology Vol. 20; no. 10; pp. 3177 - 3190 |
|---|---|
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
Blackwell Science
01-10-2014
Blackwell Publishing Ltd Wiley |
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Terrestrial carbon stock mapping is important for the successful implementation of climate change mitigation policies. Its accuracy depends on the availability of reliable allometric models to infer oven‐dry aboveground biomass of trees from census data. The degree of uncertainty associated with previously published pantropical aboveground biomass allometries is large. We analyzed a global database of directly harvested trees at 58 sites, spanning a wide range of climatic conditions and vegetation types (4004 trees ≥ 5 cm trunk diameter). When trunk diameter, total tree height, and wood specific gravity were included in the aboveground biomass model as covariates, a single model was found to hold across tropical vegetation types, with no detectable effect of region or environmental factors. The mean percent bias and variance of this model was only slightly higher than that of locally fitted models. Wood specific gravity was an important predictor of aboveground biomass, especially when including a much broader range of vegetation types than previous studies. The generic tree diameter–height relationship depended linearly on a bioclimatic stress variable E, which compounds indices of temperature variability, precipitation variability, and drought intensity. For cases in which total tree height is unavailable for aboveground biomass estimation, a pantropical model incorporating wood density, trunk diameter, and the variable E outperformed previously published models without height. However, to minimize bias, the development of locally derived diameter–height relationships is advised whenever possible. Both new allometric models should contribute to improve the accuracy of biomass assessment protocols in tropical vegetation types, and to advancing our understanding of architectural and evolutionary constraints on woody plant development. |
|---|---|
| Bibliography: | http://dx.doi.org/10.1111/gcb.12629 CNES Agence Nationale pour la Recherche Data S1. Figures. Figure S1. Distribution of the 58 study sites in environmental space of altitude, mean annual temperature, mean annual evapotranspiration (ET, mm yr−1), mean annual rainfall, and temperature seasonality (TS). The gray shades in the background represent the range of observed variation in currently forested areas in the tropical belt. Red dots indicate Latin American sites, green African sites, and blue Southeast Asian and Australian sites. Figure S2. Among-site relationship of the individual coefficient of variation to bioclimatic variables (TS: temperature seasonality; CWD: climatic water deficit) across study sites, for Model 4. Each point represents the individual coefficient of variation CV(j) of a study site j, as inferred from Model 4. Point color and size are as in Fig. . Figure S3. Among-site relationship of the site-level bias with bioclimatic variables (TS: temperature seasonality; CWD: climatic water deficit) for Model 4. Each point represents the Bias(j) for site j as inferred from Model 4. Point color and size are as in Fig. . Figure S4. Among-site relationship of the form factor (ratio AGB /ρD2H with bioclimatic variables (TS: temperature seasonality; CWD: climatic water deficit). Each point represents the mean form factor of a study site (equivalent to the fitted parameter of Model 5). Correlation tests were performed on each dataset. In panel (a) P = 0.09 (Bartlett test); in panels (b) to (d), P < 10−3, P = 0.08, P < 10−3 (Spearman correlation). Point color and size are as in Fig. . Figure S5. Forward selection for bioclimatic variables in Eqn . The first selected variable is TS (temperature seasonality), and including it results in a decline of the residual standard error (σ', noted RSE in the ordinate axis) from 0.430 to 0.292. The second selected variable is CWD (climatic water deficit), and including it results in a decline of the RSE from 0.292 to 0.272. The third selected variable is PS (precipitation seasonality), and including it results in a further decline of the RSE from 0.272 to 0.243. Additional environmental variables induced comparatively very little further decline in RSE (a gain of 0.022). Figure S6. Comparison between the pantropical allometric AGB Model 7 and a model in which Feldpausch et al. () regional diameter-height equations were used. (a) Individual coefficient of variation at each site for both types of allometries. (b) Bias at each site for both types of allometries. Point color and size are as in Fig. . The outlying sites are labeled.Data S2. Description of the study sites. Table S1. Study sites and their characteristics.Data S3. Details about the database construction. Table S2. Description of the variables included in the dataset (n = 4004).Data S4. Illustration of the goodness of fit at each of the 58 study sites. The full dataset is plotted in the background (black). The foreground points and regression line represent the best-fit regression for local datasets. istex:89877F98235631965C5D769E86B115C395F48FCF Fondation pour la Recherche sur la Biodiversité ark:/67375/WNG-GR2DCGL4-C ArticleID:GCB12629 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1354-1013 1365-2486 |
| DOI: | 10.1111/gcb.12629 |