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Title: Predicting biomass of hyperdiverse and structurally complex central Amazonian forests - a virtual approach using extensive field data

Abstract

Notice on corrigendum: This paper has a corresponding corrigendum published. Please read the corrigendum first. Old-growth forests are subject to substantial changes in structure and species composition due to the intensification of human activities, gradual climate change and extreme weather events. Trees store ca. 90 % of the total aboveground biomass (AGB) in tropical forests and precise tree biomass estimation models are crucial for management and conservation. In the central Amazon, predicting AGB at large spatial scales is a challenging task due to the heterogeneity of successional stages, high tree species diversity and inherent variations in tree allometry and architecture. We parameterized generic AGB estimation models applicable across species and a wide range of structural and compositional variation related to species sorting into height layers as well as frequent natural disturbances. We used 727 trees (diameter at breast height ≥ 5 cm) from 101 genera and at least 135 species harvested in a contiguous forest near Manaus, Brazil. Sampling from this data set we assembled six scenarios designed to span existing gradients in floristic composition and size distribution in order to select models that best predict AGB at the landscape level across successional gradients. We found that good individual treemore » model fits do not necessarily translate into reliable predictions of AGB at the landscape level. When predicting AGB (dry mass) over scenarios using our different models and an available pantropical model, we observed systematic biases ranging from -31 % (pantropical) to +39 %, with root-mean-square error (RMSE) values of up to 130 Mg ha-1 (pantropical). Our first and second best models had both low mean biases (0.8 and 3.9 %, respectively) and RMSE (9.4 and 18.6 Mg ha-1) when applied over scenarios. Predicting biomass correctly at the landscape level in hyperdiverse and structurally complex tropical forests, especially allowing good performance at the margins of data availability for model construction/calibration, requires the inclusion of predictors that express inherent variations in species architecture. The model of interest should comprise the floristic composition and size-distribution variability of the target forest, implying that even generic global or pantropical biomass estimation models can lead to strong biases. Reliable biomass assessments for the Amazon basin (i.e., secondary forests) still depend on the collection of allometric data at the local/regional scale and forest inventories including species-specific attributes, which are often unavailable or estimated imprecisely in most regions.« less

Authors:
 [1];  [2];  [3];  [2];  [2];  [2];  [2];  [4];  [5];  [6];  [7];  [8]
+ Show Author Affiliations
  1. Univ. of Leipzig (Germany). AG Spezielle Botanik und Funktionelle Biodiversitat; Max Planck Society, Jena (Germany). Max Planck Inst. for Biogeochemistry, Biogeochemical Processes Dept.; Inst. Nacional de Pesquisas da Amazonia, Manaus (Brazil). Lab de Manejo Florestal
  2. Inst. Nacional de Pesquisas da Amazonia, Manaus (Brazil). Lab de Manejo Florestal
  3. Max Planck Society, Jena (Germany). Max Planck Inst. for Biogeochemistry, Biogeochemical Processes Dept.
  4. Univ. of California, Berkeley, CA (United States). Geography Dept.
  5. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Climate Sciences Dept.
  6. Univ. of Leipzig (Germany). AG Spezielle Botanik und Funktionelle Biodiversitat
  7. Univ. of Leipzig (Germany). AG Spezielle Botanik und Funktionelle Biodiversitat; Univ. Industrial de Santander, Bucaramanga (Colombia). Escuela de Biologia
  8. Univ. of Leipzig (Germany). AG Spezielle Botanik und Funktionelle Biodiversitat; German Centre for Integrative Biodiversity Research (iDiv), Leipzig (Germany); Max Planck Society, Jena (Germany). Max Planck Inst. for Biogeochemistry
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1377415
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Biogeosciences (Online)
Additional Journal Information:
Journal Name: Biogeosciences (Online); Journal Volume: 13; Journal Issue: 5; Journal ID: ISSN 1726-4189
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Magnabosco Marra, Daniel, Higuchi, Niro, Trumbore, Susan E., Ribeiro, Gabriel H. P. M., dos Santos, Joaquim, Carneiro, Vilany M. C., Lima, Adriano J. N., Chambers, Jeffrey Q., Negrón-Juárez, Robinson I., Holzwarth, Frederic, Reu, Björn, and Wirth, Christian. Predicting biomass of hyperdiverse and structurally complex central Amazonian forests - a virtual approach using extensive field data. United States: N. p., 2016. Web. doi:10.5194/bg-13-1553-2016.
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Magnabosco Marra, Daniel, Higuchi, Niro, Trumbore, Susan E., Ribeiro, Gabriel H. P. M., dos Santos, Joaquim, Carneiro, Vilany M. C., Lima, Adriano J. N., Chambers, Jeffrey Q., Negrón-Juárez, Robinson I., Holzwarth, Frederic, Reu, Björn, & Wirth, Christian. Predicting biomass of hyperdiverse and structurally complex central Amazonian forests - a virtual approach using extensive field data. United States. https://doi.org/10.5194/bg-13-1553-2016
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Magnabosco Marra, Daniel, Higuchi, Niro, Trumbore, Susan E., Ribeiro, Gabriel H. P. M., dos Santos, Joaquim, Carneiro, Vilany M. C., Lima, Adriano J. N., Chambers, Jeffrey Q., Negrón-Juárez, Robinson I., Holzwarth, Frederic, Reu, Björn, and Wirth, Christian. Fri . "Predicting biomass of hyperdiverse and structurally complex central Amazonian forests - a virtual approach using extensive field data". United States. https://doi.org/10.5194/bg-13-1553-2016. https://www.osti.gov/servlets/purl/1377415.
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@article{osti_1377415,
title = {Predicting biomass of hyperdiverse and structurally complex central Amazonian forests - a virtual approach using extensive field data},
author = {Magnabosco Marra, Daniel and Higuchi, Niro and Trumbore, Susan E. and Ribeiro, Gabriel H. P. M. and dos Santos, Joaquim and Carneiro, Vilany M. C. and Lima, Adriano J. N. and Chambers, Jeffrey Q. and Negrón-Juárez, Robinson I. and Holzwarth, Frederic and Reu, Björn and Wirth, Christian},
abstractNote = {Notice on corrigendum: This paper has a corresponding corrigendum published. Please read the corrigendum first. Old-growth forests are subject to substantial changes in structure and species composition due to the intensification of human activities, gradual climate change and extreme weather events. Trees store ca. 90 % of the total aboveground biomass (AGB) in tropical forests and precise tree biomass estimation models are crucial for management and conservation. In the central Amazon, predicting AGB at large spatial scales is a challenging task due to the heterogeneity of successional stages, high tree species diversity and inherent variations in tree allometry and architecture. We parameterized generic AGB estimation models applicable across species and a wide range of structural and compositional variation related to species sorting into height layers as well as frequent natural disturbances. We used 727 trees (diameter at breast height ≥ 5 cm) from 101 genera and at least 135 species harvested in a contiguous forest near Manaus, Brazil. Sampling from this data set we assembled six scenarios designed to span existing gradients in floristic composition and size distribution in order to select models that best predict AGB at the landscape level across successional gradients. We found that good individual tree model fits do not necessarily translate into reliable predictions of AGB at the landscape level. When predicting AGB (dry mass) over scenarios using our different models and an available pantropical model, we observed systematic biases ranging from -31 % (pantropical) to +39 %, with root-mean-square error (RMSE) values of up to 130 Mg ha-1 (pantropical). Our first and second best models had both low mean biases (0.8 and 3.9 %, respectively) and RMSE (9.4 and 18.6 Mg ha-1) when applied over scenarios. Predicting biomass correctly at the landscape level in hyperdiverse and structurally complex tropical forests, especially allowing good performance at the margins of data availability for model construction/calibration, requires the inclusion of predictors that express inherent variations in species architecture. The model of interest should comprise the floristic composition and size-distribution variability of the target forest, implying that even generic global or pantropical biomass estimation models can lead to strong biases. Reliable biomass assessments for the Amazon basin (i.e., secondary forests) still depend on the collection of allometric data at the local/regional scale and forest inventories including species-specific attributes, which are often unavailable or estimated imprecisely in most regions.},
doi = {10.5194/bg-13-1553-2016},
journal = {Biogeosciences (Online)},
number = 5,
volume = 13,
place = {United States},
year = {Fri Mar 11 00:00:00 EST 2016},
month = {Fri Mar 11 00:00:00 EST 2016}
}
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