Suchergebnisse

While lignin geochemistry has been extensively investigated in the Amazon River, little is known about lignin distribution and dynamics within deep, stratified river channels or its transformations within soils prior to delivery to rivers. We characterized lignin phenols in soils, river particulate organic matter (POM), and dissolved organic matter (DOM) across a 4 km elevation gradient in the Madre de Dios River system, Peru, as well as in marine sediments to investigate the source-to-sink evolution of lignin. In soils, we found more oxidized lignin in organic horizons relative to mineral horizons. The oxidized lignin signature was maintained during transfer into rivers, and lignin was a relatively constant fraction of bulk organic carbon in soils and riverine POM. Lignin in DOM became increasingly oxidized downstream, indicating active transformation of dissolved lignin during transport, especially in the dry season. In contrast, POM accumulated undegraded lignin downstream during the wet season, suggesting that terrestrial input exceeded in-river degradation. We discovered high concentrations of relatively undegraded lignin in POM at depth in the lower Madre de Dios River in both seasons, revealing a woody undercurrent for its transfer within these deep rivers. Our study of lignin evolution in the soil-river-ocean continuum highlights important seasonal and depth variations of river carbon components and their connection to soil carbon pools, providing new insights into fluvial carbon dynamics associated with the transfer of lignin biomarkers from source to sink. ; This work was supported by funding from the U.S. National Science Foundation award 1227192 to A.J.W.and S.J.F., and by funding from the National Program on Key Basic Research Project (2015CB954201) and National Natural Science Foundation of China(41422304) to X.F. R.Z.W. received sup-port from USC funding for undergraduate research. For the river work, we aregrateful to field assistance from M.Torres, A. Robles, and A. Ccahuana, and river navigation by Ernesto. Logistical support was provided by Y. Malhi, J.Huaman, W. Huaraca Huasco, and other collaborators as part of the Andes Biodiversity and Ecosystems Research Group (ABERG; andesresearch.org).Thanks to students who assisted in the laboratory work: E. Rosca, K. McPherson,and G. Li (USC), and technical assistance from Nick Rollins. P.M. and A.N.acknowledge funding from Natural Environment Research Council NE/F002149/1 (soils), ARC FT110100457 to P.M. and European Union Marie Curie Fellowship (FP7-2012-329360) to A.N.Funding for the marine samples is acknowledged from NSF (OCE-0934073)to W.B. We are thankful to the captain and crew of the R/V Knorr. We would also like to acknowledge the members of the at-sea and shore-based science party and all contributors to the ANACONDAS project.

A floresta amazônica apresenta elevadas temperaturas e grandes quantidades de precipitações anuais, embora ocorram grandes variações interanuais desses elementos meteorológicos. A temperatura do ar (Tar) e a umidade relativa do ar (UR) dentro e acima de uma floresta são resultado de complexas trocas energéticas através dos processos de reflexão, transmissão e absorção da energia solar. Esse estudo foi realizado na Floresta Nacional de Caxiuanã, Pará, Brasil, e teve como objetivo principal avaliar a variabilidade sazonal da temperatura e umidade do ar a partir de uma análise de perfil vertical no interior da floresta, com medidas alturas de 2 m, 16 m, 28 m e 42 m, com registros a cada 30 minutos, durante o período de 2012 a 2016. Os resultados indicaram grande sazonalidade nestes elementos meteorológicos, e as maiores temperaturas ocorreram no nível do dossel (28 m), e as menores foram observadas próximo à superfície (2 m), devido a atenuação da radiação solar no interior da floresta. Os maiores valores da UR foram observados próximo à superfície (2 m), e os menores ocorreram acima do dossel, devido as maiores velocidades do vento neste nível. Estes resultados indicam grande variabilidade espaço-temporal destes elementos meteorológicos, o que influencia no comportamento dos organismos vivos que habitam aquele ambiente florestal.

Aim: The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location: Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1 Methods: Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. Results: The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by >25%, whereas regional uncertainties for the maps were reported to be <5%. Main conclusions: Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.

Aim: The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location: Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1 Methods: Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. Results: The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by >25%, whereas regional uncertainties for the maps were reported to be <5%. Main conclusions: Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.

Florestas tropicais são reguladoras do clima e o padrão de variabilidade dos elementos meteorológicos representa fisicamente interdependência entre os fluxos térmicos e hídricos para a floresta e a atmosfera. A presente investigação analisa uma série de dados (precipitação, temperatura do ar, umidade relativa do ar, radiação solar global, velocidade e direção do vento) na Floresta Nacional de Caxiuanã, Pará. O estudo ocorreu entre 2005 e 2013, usando-se uma classificação sazonal, definida da seguinte forma: dezembro a junho, como chuvoso, e julho a novembro, como menos chuvoso. Dois métodos estatísticos básicos foram aplicados para testar as diferenças sazonais e avaliar a "sincronicidade" entre as variações sazonais dos elementos meteorológicos: a) teste não-paramétrico (Mann-Whitney; p <0,05) para a análise horária; b) Teste de estatística circular (método Watson-Williams). Concluímos que em termos horários a direção do vento e a temperatura do ar mudaram entre períodos úmidos e secos, mas a intensidade do vento não mostrou alterações significativas. Também houve mudanças significativas na radiação global, precipitação e umidade relativa. A variabilidade da velocidade do vento foi sincronizada com variabilidade na direção do vento, precipitação e temperatura do ar, mas não com a umidade relativa e radiação solar total. Esses resultados geram uma descrição básica dos mecanismos físicos que controlam a dinâmica do tempo sazonal nas florestas tropicais.

Understanding the relationship between photosynthesis, net primary productivity and growth in forest ecosystems is key to understanding how these ecosystems will respond to global anthropogenic change, yet the linkages among these components are rarely explored in detail. We provide the first comprehensive description of the productivity, respiration and carbon allocation of contrasting lowland Amazonian forests spanning gradients in seasonal water deficit and soil fertility. Using the largest data set assembled to date, ten sites in three countries all studied with a standardized methodology, we find that (i) gross primary productivity (GPP) has a simple relationship with seasonal water deficit, but that (ii) site-to-site variations in GPP have little power in explaining site-to-site spatial variations in net primary productivity (NPP) or growth because of concomitant changes in carbon use efficiency (CUE), and conversely, the woody growth rate of a tropical forest is a very poor proxy for its productivity. Moreover, (iii) spatial patterns of biomass are much more driven by patterns of residence times (i.e. tree mortality rates) than by spatial variation in productivity or tree growth. Current theory and models of tropical forest carbon cycling under projected scenarios of global atmospheric change can benefit from advancing beyond a focus on GPP. By improving our understanding of poorly understood processes such as CUE, NPP allocation and biomass turnover times, we can provide more complete and mechanistic approaches to linking climate and tropical forest carbon cycling. ; Fieldwork was funded by grants from the UKNatural Environment Research Council (NE/D01025X/1 andNE/D014174/1) the European Union Framework 7 projectGEOCARBON (283080), and the Gordon and Betty Moore Foun-dation. YM is supported by the Jackson Foundation and by aEuropean Research Council Advanced Investigator Award. OPis supported by an ERC Advanced Grant and a Royal SocietyWolfson Research Merit Award.

UK Natural Environment Research Council ; European Research Council Advanced Investigator Award (GEM-TRAIT) ; Nature Conservancy-Oxford Martin School Climate Partnership ; NERC ; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ; Gordon and Betty Moore Foundation ; Sime Darby Foundation ; Project USA-NAS/PEER ; Project ReFlor FAPEMAT ; Empresa Brasileira de Pesquisa Agropecuaria - Embrapa ; European Research Council (H2020-MSCA-RISE-2015) ; UK government Darwin Initiative ; Nature Conservancy ; UK Natural Environment Research Council (NERC) ; Jackson Foundation ; UK Natural Environment Research Council: NE/P001092/1 ; European Research Council Advanced Investigator Award (GEM-TRAIT): 321131 ; NERC: NE/I014705/1 ; NERC: NE/K016369/1 ; NERC: NE/F005776/1 ; NERC: NE/K016385/1 ; NERC: NE/J011002/1 ; CNPq: 457914/2013-0/MCTI/ CNPq/FNDCT/LBA/ESECAFLOR ; CNPq: 403725/ 2012-7 ; CNPq: 441244/2016-5 ; CNPq: 457602/2012-0 ; Project USA-NAS/PEER: PGA-2000005316 ; Project ReFlor FAPEMAT: 0589267/2016 ; CNPq: 574008/2008-0 ; Empresa Brasileira de Pesquisa Agropecuaria - Embrapa: SEG: 02.08.06.005.00 ; European Research Council (H2020-MSCA-RISE-2015): 691053-ODYSSEA ; UK government Darwin Initiative: 17-023 ; UK Natural Environment Research Council (NERC): NE/F01614X/1 ; UK Natural Environment Research Council (NERC): NE/G000816/1 ; UK Natural Environment Research Council (NERC): NE/K016431/1 ; UK Natural Environment Research Council (NERC): NE/P004512/1 ; : PQ-2 ; Meteorological extreme events such as El Nino events are expected to affect tropical forest net primary production (NPP) and woody growth, but there has been no large-scale empirical validation of this expectation. We collected a large high-temporal resolution dataset (for 1-13 years depending upon location) of more than 172 000 stem growth measurements using dendrometer bands from across 14 regions spanning Amazonia, Africa and Borneo in order to test how much month-to-month variation in stand-level woody growth of adult tree stems (NPPstem) can be explained by seasonal variation and interannual meteorological anomalies. A key finding is that woody growth responds differently to meteorological variation between tropical forests with a dry season (where monthly rainfall is less than 100 mm), and aseasonal wet forests lacking a consistent dry season. In seasonal tropical forests, a high degree of variation in woody growth can be predicted from seasonal variation in temperature, vapour pressure deficit, in addition to anomalies of soil water deficit and shortwave radiation. The variation of aseasonal wet forest woody growth is best predicted by the anomalies of vapour pressure deficit, water deficit and shortwave radiation. In total, we predict the total live woody production of the global tropical forest biome to be 2.16 Pg C yr(-1), with an interannual range 1.96-2.26 Pg C yr(-1) between 1996-2016, and with the sharpest declines during the strong El Nino events of 1997/8 and 2015/6. There is high geographical variation in hotspots of El Nino-associated impacts, with weak impacts in Africa, and strongly negative impacts in parts of Southeast Asia and extensive regions across central and eastern Amazonia. Overall, there is high correlation (r = -0.75) between the annual anomaly of tropical forest woody growth and the annual mean of the El Nino 3.4 index, driven mainly by strong correlations with anomalies of soil water deficit, vapour pressure deficit and shortwave radiation. This article is part of the discussion meeting issue 'The impact of the 2015/2016 El Nino on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

Aim: The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location: Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1 Methods: Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. Results: The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by > 25%, whereas regional uncertainties for the maps were reported to be < 5%. Main conclusions: Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species ...