scispace - formally typeset
Search or ask a question

Showing papers by "Oliver L. Phillips published in 2019"


Journal ArticleDOI
Adriane Esquivel-Muelbert1, Timothy R. Baker1, Kyle G. Dexter2, Simon L. Lewis3, Simon L. Lewis1, Roel J. W. Brienen1, Ted R. Feldpausch4, Jon Lloyd5, Abel Monteagudo-Mendoza6, Luzmila Arroyo7, Esteban Álvarez-Dávila, Niro Higuchi8, Beatriz Schwantes Marimon9, Ben Hur Marimon-Junior9, Marcos Silveira10, Emilio Vilanova11, Emilio Vilanova12, Emanuel Gloor1, Yadvinder Malhi13, Jérôme Chave14, Jos Barlow15, Jos Barlow16, Damien Bonal17, Nallaret Davila Cardozo18, Terry L. Erwin19, Sophie Fauset1, Bruno Hérault20, Susan G. Laurance21, Lourens Poorter22, Lan Qie5, Clément Stahl23, Martin J. P. Sullivan1, Hans ter Steege24, Hans ter Steege25, Vincent A. Vos, Pieter A. Zuidema22, Everton Cristo de Almeida26, Edmar Almeida de Oliveira9, Ana Andrade8, Simone Aparecida Vieira27, Luiz E. O. C. Aragão28, Luiz E. O. C. Aragão4, Alejandro Araujo-Murakami7, Eric Arets22, Gerardo A. Aymard C, Christopher Baraloto29, Plínio Barbosa de Camargo30, Jorcely Barroso10, Frans Bongers22, René G. A. Boot31, José Luís Camargo8, Wendeson Castro10, Victor Chama Moscoso6, James A. Comiskey19, Fernando Cornejo Valverde32, Antonio Carlos Lola da Costa33, Jhon del Aguila Pasquel32, Jhon del Aguila Pasquel34, Anthony Di Fiore35, Luisa Fernanda Duque, Fernando Elias9, Julien Engel20, Julien Engel29, Gerardo Flores Llampazo, David W. Galbraith1, Rafael Herrera Fernández36, Rafael Herrera Fernández37, Eurídice N. Honorio Coronado34, Wannes Hubau38, Eliana Jimenez-Rojas39, Adriano José Nogueira Lima8, Ricardo Keichi Umetsu9, William F. Laurance21, Gabriela Lopez-Gonzalez1, Thomas E. Lovejoy40, Omar Aurelio Melo Cruz41, Paulo S. Morandi9, David A. Neill, Percy Núñez Vargas6, Nadir Pallqui Camacho6, Alexander Parada Gutierrez, Guido Pardo, Julie Peacock1, Marielos Peña-Claros22, Maria Cristina Peñuela-Mora, Pascal Petronelli14, Georgia Pickavance1, Nigel C. A. Pitman, Adriana Prieto42, Carlos A. Quesada8, Hirma Ramírez-Angulo11, Maxime Réjou-Méchain43, Zorayda Restrepo Correa, Anand Roopsind44, Agustín Rudas42, Rafael de Paiva Salomão15, Natalino Silva, Javier Silva Espejo45, James Singh46, Juliana Stropp47, John Terborgh48, Raquel Thomas44, Marisol Toledo7, Armando Torres-Lezama11, Luis Valenzuela Gamarra, Peter J. van de Meer49, Geertje M. F. van der Heijden50, Peter van der Hout, Rodolfo Vásquez Martínez, César I.A. Vela6, Ima Célia Guimarães Vieira15, Oliver L. Phillips1 
University of Leeds1, University of Edinburgh2, University College London3, University of Exeter4, Imperial College London5, National University of Saint Anthony the Abbot in Cuzco6, Universidad Autónoma Gabriel René Moreno7, National Institute of Amazonian Research8, Universidade do Estado de Mato Grosso9, Universidade Federal do Acre10, University of Los Andes11, University of Washington12, Environmental Change Institute13, Centre national de la recherche scientifique14, Museu Paraense Emílio Goeldi15, Lancaster University16, University of Lorraine17, Universidad Nacional de la Amazonía Peruana18, Smithsonian Institution19, University of Montpellier20, James Cook University21, Wageningen University and Research Centre22, Agro ParisTech23, Naturalis24, University of Amsterdam25, Federal University of Western Pará26, State University of Campinas27, National Institute for Space Research28, Florida International University29, University of São Paulo30, Tropenbos International31, Amazon.com32, Federal University of Pará33, Michigan Technological University34, University of Texas at Austin35, Polytechnic University of Valencia36, Venezuelan Institute for Scientific Research37, Royal Museum for Central Africa38, Tecnológico de Antioquia39, George Mason University40, Universidad del Tolima41, National University of Colombia42, Paul Sabatier University43, Georgetown University44, University of La Serena45, Forestry Commission46, Federal University of Alagoas47, Duke University48, Van Hall Larenstein University of Applied Sciences49, University of Nottingham50
TL;DR: A slow shift to a more dry‐affiliated Amazonia is underway, with changes in compositional dynamics consistent with climate‐change drivers, but yet to significantly impact whole‐community composition.
Abstract: Most of the planet's diversity is concentrated in the tropics, which includes many regions undergoing rapid climate change. Yet, while climate‐induced biodiversity changes are widely documented elsewhere, few studies have addressed this issue for lowland tropical ecosystems. Here we investigate whether the floristic and functional composition of intact lowland Amazonian forests have been changing by evaluating records from 106 long‐term inventory plots spanning 30 years. We analyse three traits that have been hypothesized to respond to different environmental drivers (increase in moisture stress and atmospheric CO2 concentrations): maximum tree size, biogeographic water‐deficit affiliation and wood density. Tree communities have become increasingly dominated by large‐statured taxa, but to date there has been no detectable change in mean wood density or water deficit affiliation at the community level, despite most forest plots having experienced an intensification of the dry season. However, among newly recruited trees, dry‐affiliated genera have become more abundant, while the mortality of wet‐affiliated genera has increased in those plots where the dry season has intensified most. Thus, a slow shift to a more dry‐affiliated Amazonia is underway, with changes in compositional dynamics (recruits and mortality) consistent with climate‐change drivers, but yet to significantly impact whole‐community composition. The Amazon observational record suggests that the increase in atmospheric CO2 is driving a shift within tree communities to large‐statured species and that climate changes to date will impact forest composition, but long generation times of tropical trees mean that biodiversity change is lagging behind climate change.

263 citations


Journal ArticleDOI
TL;DR: The sPlot database as mentioned in this paper contains 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015.
Abstract: Aims :Vegetation-plot records provide information on the presence and cover or abundance of plants co-occurring in the same community. Vegetation-plot data are spread across research groups, environmental agencies and biodiversity research centers and, thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. Results: sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015. We complemented the information for each plot by retrieving climate and soil conditions and the biogeographic context (e.g., biomes) from external sources, and by calculating community-weighted means and variances of traits using gap-filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. We present the first maps of global patterns of community richness and community-weighted means of key traits. Conclusions: The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale.

160 citations


Journal ArticleDOI
TL;DR: In this article, a U-net convolutional network was used to identify and segment natural forests and eucalyptus plantations, and an indicator of forest disturbance, the tree species Cecropia hololeuca, in very high resolution images (0.3 m) from the WorldView-3 satellite in the Brazilian Atlantic rainforest region.
Abstract: Mapping forest types and tree species at regional scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. Here, we assess the potential of a U‐net convolutional network, a recent deep learning algorithm, to identify and segment (1) natural forests and eucalyptus plantations, and (2) an indicator of forest disturbance, the tree species Cecropia hololeuca, in very high resolution images (0.3 m) from the WorldView‐3 satellite in the Brazilian Atlantic rainforest region. The networks for forest types and Cecropia trees were trained with 7611 and 1568 red‐green‐blue (RGB) images, respectively, and their dense labeled masks. Eighty per cent of the images were used for training and 20% for validation. The U‐net network segmented forest types with an overall accuracy >95% and an intersection over union (IoU) of 0.96. For C. hololeuca, the overall accuracy was 97% and the IoU was 0.86. The predictions were produced over a 1600 km2 region using WorldView‐3 RGB bands pan‐sharpened at 0.3 m. Natural and eucalyptus forests compose 79 and 21% of the region's total forest cover (82 250 ha). Cecropia crowns covered 1% of the natural forest canopy. An index to describe the level of disturbance of the natural forest fragments based on the spatial distribution of Cecropia trees was developed. Our work demonstrates how a deep learning algorithm can support applications such as vegetation, tree species distributions and disturbance mapping on a regional scale.

117 citations


Journal ArticleDOI
TL;DR: A wide range of anticipated user requirements for product accuracy assessment are outlined and recommendations for the validation of biomass products are provided, including the collection of new, high-quality in situ data and the use of airborne lidar biomass maps as tools toward transparent multi-resolution validation.
Abstract: Several upcoming satellite missions have core science requirements to produce data for accurate forest aboveground biomass mapping. Largely because of these mission datasets, the number of available biomass products is expected to greatly increase over the coming decade. Despite the recognized importance of biomass mapping for a wide range of science, policy and management applications, there remains no community accepted standard for satellite-based biomass map validation. The Committee on Earth Observing Satellites (CEOS) is developing a protocol to fill this need in advance of the next generation of biomass-relevant satellites, and this paper presents a review of biomass validation practices from a CEOS perspective. We outline the wide range of anticipated user requirements for product accuracy assessment and provide recommendations for the validation of biomass products. These recommendations include the collection of new, high-quality in situ data and the use of airborne lidar biomass maps as tools toward transparent multi-resolution validation. Adoption of community-vetted validation standards and practices will facilitate the uptake of the next generation of biomass products.

93 citations


Journal ArticleDOI
TL;DR: In this paper, a strategy for a coordinated and global network of in situ data that would benefit biomass remote sensing missions is proposed to build upon existing networks of long-term tropical forest monitoring.
Abstract: Several remote sensing missions will soon produce detailed carbon maps over all terrestrial ecosystems. These missions are dependent on accurate and representative in situ datasets for the training of their algorithms and product validation. However, long-term ground-based forest-monitoring systems are limited, especially in the tropics, and to be useful for validation, such ground-based observation systems need to be regularly revisited and maintained at least over the lifetime of the planned missions. Here we propose a strategy for a coordinated and global network of in situ data that would benefit biomass remote sensing missions. We propose to build upon existing networks of long-term tropical forest monitoring. To produce accurate ground-based biomass estimates, strict data quality must be guaranteed to users. It is more rewarding to invest ground resources at sites where there currently is assurance of a long-term commitment locally and where a core set of data is already available. We call these ‘supersites’. Long-term funding for such an inter-agency endeavour remains an important challenge, and we here provide costing estimates to facilitate dialogue among stakeholders. One critical requirement is to ensure in situ data availability over the lifetime of remote sensing missions. To this end, consistent guidelines for supersite selection and management are proposed within the Forest Observation System, long-term funding should be assured, and principal investigators of the sites should be actively involved.

89 citations


Journal ArticleDOI
TL;DR: This study provides a rigorous and traceable refinement of the IPCC 2006 default rates in tropical and subtropical ecological zones, and identifies which areas require more research on ∆AGB.
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 on forest biomass change at large scale also make 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 yr‐1 in younger secondary forests, from 2.3 (North and South Ameri09ca) to 3.5 (Africa) Mg ha‐1 yr‐1 in older secondary forests, and 0.7 (Asia) to 1.3 (Africa) Mg ha‐1 yr‐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, non‐governmental organisations and in scientific research.

73 citations


Journal ArticleDOI
TL;DR: In this article, a trend analysis of the annual maximum of Enhanced Vegetation Index (EVI) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) was performed to quantify woody expansion in the Cerrado biome and transitional ecotones.
Abstract: Woody encroachment is occurring in all tropical savannas of the world. However, in the Brazilian savanna (the Cerrado), the extent of this phenomenon is still poorly documented. Here, woody encroachment was quantified throughout the Cerrado biome and transitional ecotones using a trend analysis of the annual maximum of Enhanced Vegetation Index (EVI) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The associations with potential local drivers, such as fire and land use regime, were assessed using satellite data of land cover and fire regime. We found that 19% of the remaining native vegetation showed significant evidence of woody encroachment in the last 15 years, and 7% exhibited degradation processes. The local factors that favored woody expansion in 19% of the biome were a decrease of fire (34%) and land use abandonment (26%). Our study highlights that local human‐associated drivers are playing a major role in woody encroachment and savanna degradation.

60 citations


Journal ArticleDOI
TL;DR: Impacts of species on forest biomass due to wood density at all scales from the individual tree up to the whole biome are found and mean basal-area-weighted wood density values for different forests across the low and tropical biome are provided.
Abstract: The mass of carbon contained in trees is governed by the volume and density of their wood. This represents a challenge to most remote sensing technologies, which typically detect surface structure and parameters related to wood volume but not to its density. Since wood density is largely determined by taxonomic identity this challenge is greatest in tropical forests where there are tens of thousands of tree species. Here, using pan-tropical literature and new analyses in Amazonia with plots with reliable identifications we assess the impact that species-related variation in wood density has on biomass estimates of mature tropical forests. We find impacts of species on forest biomass due to wood density at all scales from the individual tree up to the whole biome: variation in tree species composition regulates how much carbon forests can store. Even local differences in composition can cause variation in forest biomass and carbon density of 20% between subtly different local forest types, while additional large-scale floristic variation leads to variation in mean wood density of 10–30% across Amazonia and the tropics. Further, because species composition varies at all scales and even vertically within a stand, our analysis shows that bias and uncertainty always result if individual identity is ignored. Since sufficient inventory-based evidence based on botanical identification now exists to show that species composition matters biome-wide for biomass, we here assemble and provide mean basal-area-weighted wood density values for different forests across the lowand tropical biome. These range widely, from 0.467 to 0.728 g cm−3 with a pan-tropical mean of 0.619 g cm−3. Our analysis shows that mapping tropical ecosystem carbon always benefits from locally validated measurement of tree-by-tree botanical identity combined with tree-by-tree measurement of dimensions. Therefore whenever possible, efforts to map and monitor tropical forest carbon using remote sensing techniques should be combined with tree-level measurement of species identity by botanists working in inventory plots.

49 citations


Journal ArticleDOI
Dmitry Schepaschenko1, Dmitry Schepaschenko2, Jérôme Chave3, Oliver L. Phillips4, Simon L. Lewis4, Simon L. Lewis5, Stuart J. Davies6, Maxime Réjou-Méchain7, Plinio Sist, Klaus Scipal8, Christoph Perger1, Bruno Hérault, Nicolas Labrière3, Florian Hofhansl1, Kofi Affum-Baffoe9, Alexei Aleinikov10, Alfonso Alonso11, C. Amani12, Alejandro Araujo-Murakami13, John Armston14, John Armston15, Luzmila Arroyo13, Nataly Ascarrunz, Celso Paulo de Azevedo16, Timothy R. Baker4, Radomir Bałazy17, Caroline Bedeau, Nicholas J. Berry, Andrii Bilous18, Svitlana Bilous18, Pulchérie Bissiengou, Lilian Blanc, K. S. Bobkova10, Tatyana Braslavskaya10, Roel J. W. Brienen4, David F. R. P. Burslem19, Richard Condit20, Aida Cuni-Sanchez21, Dilshad M. Danilina22, Dennis Del Castillo Torres, Géraldine Derroire, Laurent Descroix, Eleneide Doff Sotta16, Marcus Vinicio Neves d'Oliveira16, C. Dresel1, Terry L. Erwin23, Mikhail D. Evdokimenko22, Jan Falck24, Ted R. Feldpausch25, Ernest G. Foli26, Robin B. Foster, Steffen Fritz1, Antonio García-Abril27, A. V. Gornov10, Maria Gornova10, Ernest Gothard-Bassébé, Sylvie Gourlet-Fleury, Marcelino Carneiro Guedes16, Keith C. Hamer4, Farida Herry Susanty, Niro Higuchi28, Eurídice N. Honorio Coronado, Wannes Hubau4, Wannes Hubau29, Stephen P. Hubbell30, Ulrik Ilstedt24, Viktor V. Ivanov22, Milton Kanashiro16, Anders Karlsson24, V.N. Karminov10, Timothy J. Killeen31, Jean Claude Konan Koffi, M. E. Konovalova22, Florian Kraxner1, Jan Krejza, Haruni Krisnawati, Leonid Krivobokov22, Mikhail A. Kuznetsov10, Ivan Lakyda18, Petro Lakyda18, Juan Carlos Licona, Richard Lucas32, N. V. Lukina10, Daniel Lussetti24, Yadvinder Malhi33, José Antonio Manzanera27, Beatriz Schwantes Marimon34, Ben Hur Marimon Junior34, Rodolfo Vásquez Martínez35, Olga Martynenko, Maksym Matsala18, Raisa K. Matyashuk36, Lucas Mazzei16, Hervé Memiaghe37, Casimiro Mendoza, Abel Monteagudo Mendoza35, Olga V. Moroziuk18, Liudmila Mukhortova22, Samsudin Musa, D. I. Nazimova22, Toshinori Okuda38, Luís Cláudio de Oliveira16, P. V. Ontikov2, Andrey Osipov10, Stephan A. Pietsch1, Maureen Playfair, John R. Poulsen39, Vladimir G. Radchenko36, Kenneth Rodney40, Andes Hamuraby Rozak41, Ademir Roberto Ruschel16, Ervan Rutishauser6, Linda See1, Maria Shchepashchenko, N. E. Shevchenko10, Anatoly Shvidenko1, Anatoly Shvidenko22, Marcos Silveira42, James Singh9, Bonaventure Sonké43, Cintia Rodrigues de Souza16, Krzysztof Stereńczak17, Leonid Stonozhenko, Martin J. P. Sullivan4, Justyna Szatniewska, Hermann Taedoumg44, Hermann Taedoumg43, Hans ter Steege45, Elena B. Tikhonova10, Marisol Toledo13, Olga V. Trefilova22, Ruben Valbuena46, Luis Valenzuela Gamarra35, Sergey Vasiliev2, Estella F. Vedrova22, Sergey V. Verhovets47, Edson Vidal48, Nadezhda A. Vladimirova, Jason Vleminckx49, Vincent A. Vos, Foma K. Vozmitel2, Wolfgang Wanek50, Thales A.P. West51, Hannsjorg Woell, John T. Woods52, Verginia Wortel, Toshihiro Yamada38, Zamah Shari Nur Hajar17, Irie Casimir Zo-Bi 
International Institute for Applied Systems Analysis1, Bauman Moscow State Technical University2, Paul Sabatier University3, University of Leeds4, University College London5, Smithsonian Tropical Research Institute6, Centre national de la recherche scientifique7, European Space Agency8, Forestry Commission9, Russian Academy of Sciences10, Smithsonian Conservation Biology Institute11, Center for International Forestry Research12, Universidad Autónoma Gabriel René Moreno13, University of Queensland14, University of Maryland, College Park15, Empresa Brasileira de Pesquisa Agropecuária16, Forest Research Institute17, National University of Life and Environmental Sciences of Ukraine18, University of Aberdeen19, Morton Arboretum20, University of York21, Sukachev Institute of Forest22, Smithsonian Institution23, Swedish University of Agricultural Sciences24, University of Exeter25, Forestry Research Institute of Ghana26, Technical University of Madrid27, National Institute of Amazonian Research28, Ghent University29, University of California, Los Angeles30, World Wide Fund for Nature31, Aberystwyth University32, University of Oxford33, Universidade do Estado de Mato Grosso34, National University of Saint Anthony the Abbot in Cuzco35, National Academy of Sciences of Ukraine36, University of Oregon37, Hiroshima University38, Duke University39, Iwokrama International Centre for Rain Forest Conservation and Development40, Indonesian Institute of Sciences41, Universidade Federal do Acre42, University of Yaoundé I43, Bioversity International44, Naturalis45, Bangor University46, Siberian Federal University47, University of São Paulo48, Florida International University49, University of Vienna50, Scion51, University of Liberia52
TL;DR: The Forest Observation System (FOS) initiative is presented, an international cooperation to establish and maintain a global in situ forest biomass database that offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.
Abstract: Forest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.

47 citations


Journal ArticleDOI
TL;DR: Mining of the ATDN dataset suggests that monodominance is quite rare in Amazonia, and may be linked primarily to edaphic factors.
Abstract: Tropical forests are known for their high diversity. Yet, forest patches do occur in the tropics where a single tree species is dominant. Such "monodominant" forests are known from all of the main tropical regions. For Amazonia, we sampled the occurrence of monodominance in a massive, basin-wide database of forest-inventory plots from the Amazon Tree Diversity Network (ATDN). Utilizing a simple defining metric of at least half of the trees ≥ 10 cm diameter belonging to one species, we found only a few occurrences of monodominance in Amazonia, and the phenomenon was not significantly linked to previously hypothesized life history traits such wood density, seed mass, ectomycorrhizal associations, or Rhizobium nodulation. In our analysis, coppicing (the formation of sprouts at the base of the tree or on roots) was the only trait significantly linked to monodominance. While at specific locales coppicing or ectomycorrhizal associations may confer a considerable advantage to a tree species and lead to its monodominance, very few species have these traits. Mining of the ATDN dataset suggests that monodominance is quite rare in Amazonia, and may be linked primarily to edaphic factors.

36 citations


Journal ArticleDOI
Fernanda Coelho de Souza1, Kyle G. Dexter2, Kyle G. Dexter3, Oliver L. Phillips1, R. Toby Pennington2, R. Toby Pennington4, Danilo M. Neves5, Martin J. P. Sullivan1, Esteban Álvarez-Dávila, Átila Alves6, Iêda Leão do Amaral6, Ana Andrade, Luis E.O.C. Aragao7, Luis E.O.C. Aragao4, Alejandro Araujo-Murakami8, Eric Arets9, L. Arroyo8, Gerardo A. Aymard C, Olaf Bánki10, Christopher Baraloto11, Jorcely Barroso12, René G. A. Boot13, Roel J. W. Brienen1, Foster Brown14, José Luís Camargo, Wendeson Castro12, Jérôme Chave15, Álvaro Cogollo, James A. Comiskey16, James A. Comiskey17, Fernando Cornejo-Valverde, Antonio Carlos Lola da Costa18, Plínio Barbosa de Camargo19, Anthony Di Fiore20, Ted R. Feldpausch4, David W. Galbraith1, Emanuel Gloor1, Rosa C. Goodman21, Martin Gilpin1, Rafael Herrera22, Rafael Herrera23, Niro Higuchi6, Eurídice N. Honorio Coronado24, Eliana Jimenez-Rojas25, Timothy J. Killeen, Susan G. Laurance26, William F. Laurance26, Gabriela Lopez-Gonzalez1, Thomas E. Lovejoy27, Yadvinder Malhi28, Beatriz Schwantes Marimon29, Ben Hur Marimon-Junior29, Casimiro Mendoza30, Abel Monteagudo-Mendoza, David A. Neill, Percy Núñez Vargas, Maria Cristina Peñuela Mora, Georgia Pickavance1, John Pipoly, Nigel C. A. Pitman31, Lourens Poorter9, Adriana Prieto25, Freddy Ramirez32, Anand Roopsind33, Agustín Rudas25, Rafael de Paiva Salomão34, Natalino Silva35, Marcos Silveira12, James Singh36, Juliana Stropp37, Hans ter Steege10, Hans ter Steege38, John Terborgh26, John Terborgh39, Raquel Thomas-Caesar40, Ricardo Keichi Umetsu29, Rodolfo Vasquez, Ima Célia-Vieira34, Simone Aparecida Vieira41, Vincent A. Vos, Roderick Zagt13, Timothy R. Baker1 
TL;DR: Overall, this pan-Amazonian analysis shows that greater phylogenetic diversity translates into higher levels of ecosystem function: tropical forest communities with more distantly related taxa have greater wood productivity.
Abstract: Higher levels of taxonomic and evolutionary diversity are expected to maximize ecosystem function, yet their relative importance in driving variation in ecosystem function at large scales in diverse forests is unknown. Using 90 inventory plots across intact, lowland, terra firme, Amazonian forests and a new phylogeny including 526 angiosperm genera, we investigated the association between taxonomic and evolutionary metrics of diversity and two key measures of ecosystem function: aboveground wood productivity and biomass storage. While taxonomic and phylogenetic diversity were not important predictors of variation in biomass, both emerged as independent predictors of wood productivity. Amazon forests that contain greater evolutionary diversity and a higher proportion of rare species have higher productivity. While climatic and edaphic variables are together the strongest predictors of productivity, our results show that the evolutionary diversity of tree species in diverse forest stands also influences productivity. As our models accounted for wood density and tree size, they also suggest that additional, unstudied, evolutionarily correlated traits have significant effects on ecosystem function in tropical forests. Overall, our pan-Amazonian analysis shows that greater phylogenetic diversity translates into higher levels of ecosystem function: tropical forest communities with more distantly related taxa have greater wood productivity.

Journal ArticleDOI
TL;DR: The smaller trees that make up the understory in African tropical forests store their carbon longer as compared to sub-canopy and canopy trees and they represent a disproportionately large share of the carbon sink, in spite of their small size.
Abstract: Quantifying carbon dynamics in forests is critical for understanding their role in long-term climate regulation1-4. Yet little is known about tree longevity in tropical forests3,5-8, a factor that is vital for estimating carbon persistence3,4. Here we calculate mean carbon age (the period that carbon is fixed in trees7) in different strata of African tropical forests using (1) growth-ring records with a unique timestamp accurately demarcating 66 years of growth in one site and (2) measurements of diameter increments from the African Tropical Rainforest Observation Network (23 sites). We find that in spite of their much smaller size, in understory trees mean carbon age (74 years) is greater than in sub-canopy (54 years) and canopy (57 years) trees and similar to carbon age in emergent trees (66 years). The remarkable carbon longevity in the understory results from slow and aperiodic growth as an adaptation to limited resource availability9-11. Our analysis also reveals that while the understory represents a small share (11%) of the carbon stock12,13, it contributes disproportionally to the forest carbon sink (20%). We conclude that accounting for the diversity of carbon age and carbon sequestration among different forest strata is critical for effective conservation management14-16 and for accurate modelling of carbon cycling4.

Journal ArticleDOI
TL;DR: Stable forests may play a large role in the climate solution, due to their carbon-neutrality and their ability to resist anthropogenic disturbance as mentioned in this paper, but they are not already significantly disturbed nor facing predictable near-future risks of anthropogenic disturbances.

Journal ArticleDOI
TL;DR: This study finds that logging drove changes in canopy height ranging from −5.6 to −42.2 m, with a mean reduction of −23.5 m, and shows that VHR satellite imagery has the potential for monitoring the logging in tropical forests and detecting hotspots of natural disturbance with a low cost at the regional scale.
Abstract: Logging, including selective and illegal activities, is widespread, affecting the carbon cycle and the biodiversity of tropical forests. However, automated approaches using very high resolution (VHR) satellite data (≤1 m spatial resolution) to accurately track these small-scale human disturbances over large and remote areas are not readily available. The main constraint for performing this type of analysis is the lack of spatially accurate tree-scale validation data. In this study, we assessed the potential of VHR satellite imagery to detect canopy tree loss related to selective logging in closed-canopy tropical forests. To do this, we compared the tree loss detection capability of WorldView-2 and GeoEye-1 satellites with airborne LiDAR, which acquired pre- and post-logging data at the Jamari National Forest in the Brazilian Amazon. We found that logging drove changes in canopy height ranging from −5.6 to −42.2 m, with a mean reduction of −23.5 m. A simple LiDAR height difference threshold of −10 m was enough to map 97% of the logged trees. Compared to LiDAR, tree losses can be detected using VHR satellite imagery and a random forest (RF) model with an average precision of 64%, while mapping 60% of the total tree loss. Tree losses associated with large gap openings or tall trees were more successfully detected. In general, the most important remote sensing metrics for the RF model were standard deviation statistics, especially those extracted from the reflectance of the visible bands (R, G, B), and the shadow fraction. While most small canopy gaps closed within ~2 years, larger gaps could still be observed over a longer time. Nevertheless, the use of annual imagery is advised to reach acceptable detectability. Our study shows that VHR satellite imagery has the potential for monitoring the logging in tropical forests and detecting hotspots of natural disturbance with a low cost at the regional scale.

Journal ArticleDOI
TL;DR: In this article, the authors used high-fidelity airborne imaging spectroscopy from the Carnegie Airborne Observatory to quantify a key component of beta diversity, the distance decay in species similarity through space across three landscapes in Northern Peru.
Abstract: 1. The forests of Amazonia are among the most biodiverse on Earth, yet accurately quantifying how species composition varies through space (i.e., beta‐diversity) remains a significant challenge. Here, we use high‐fidelity airborne imaging spectroscopy from the Carnegie Airborne Observatory to quantify a key component of beta‐diversity, the distance decay in species similarity through space, across three landscapes in Northern Peru. We then compared our derived distance decay relationships to theoretical expectations obtained from a Poisson Cluster Process, known to match well with empirical distance decay relationships at local scales. 2. We used an unsupervised machine learning approach to estimate spatial turnover in species composition from the imaging spectroscopy data. We first validated this approach across two landscapes using an independent dataset of forest composition in 49 forest census plots (0.1–1.5 ha). We then applied our approach to three landscapes, which together represented terra firme clay forest, seasonally flooded forest and white‐sand forest. We finally used our approach to quantify landscape‐scale distance decay relationships and compared these with theoretical distance decay relationships derived from a Poisson Cluster Process. 3. We found a significant correlation of similarity metrics between spectral data and forest plot data, suggesting that beta‐diversity within and among forest types can be accurately estimated from airborne spectroscopic data using our unsupervised approach. We also found that estimated distance decay in species similarity varied among forest types, with seasonally flooded forests showing stronger distance decay than white‐sand and terra firme forests. Finally, we demonstrated that distance decay relationships derived from the theoretical Poisson Cluster Process compare poorly with our empirical relationships. 4. Synthesis. Our results demonstrate the efficacy of using high‐fidelity imaging spectroscopy to estimate beta‐diversity and continuous distance decay in lowland tropical forests. Furthermore, our findings suggest that distance decay relationships vary substantially among forest types, which has important implications for conserving these valuable ecosystems. Finally, we demonstrate that a theoretical Poisson Cluster Process poorly predicts distance decay in species similarity as conspecific aggregation occurs across a range of nested scales within larger landscapes.

Journal ArticleDOI
TL;DR: A demographics scheme (tree recruitment, growth, and mortality) is added to a recently developed non-demographic model - the Trait-based Forest Simulator (TFS) to explore the roles of climate and plant traits in controlling forest productivity and structure.
Abstract: Climate, species composition, and soils are thought to control carbon cycling and forest structure in Amazonian forests. Here, we add a demographics scheme (tree recruitment, growth, and mortality) to a recently developed non-demographic model - the Trait-based Forest Simulator (TFS) – to explore the roles of climate and plant traits in controlling forest productivity and structure. We compared two sites with differing climates (seasonal versus aseasonal precipitation) and plant traits. Through an initial validation simulation, we assessed whether the model converges on observed forest properties (productivity, demographic and structural variables) using datasets of functional traits, structure, and climate to model the carbon cycle at the two sites. In a second set of simulations, we tested the relative importance of climate and plant traits for forest properties within the TFS framework using the climate from the two sites with hypothetical trait distributions representing two axes of functional variation (‘fast’ versus ‘slow’ leaf traits, and high versus low wood density). The adapted model with demographics reproduced observed variation in gross (GPP) and net (NPP) primary production, and respiration. However NPP and respiration at the level of plant organs (leaf, stem, and root) were poorly simulated. Mortality and recruitment rates were underestimated. The equilibrium forest structure differed from observations of stem numbers suggesting either that the forests are not currently at equilibrium or that mechanisms are missing from the model. Findings from the second set of simulations demonstrated that differences in productivity were driven by climate, rather than plant traits. Contrary to expectation, varying leaf traits had no influence on GPP. Drivers of simulated forest structure were complex, with a key role for wood density mediated by its link to tree mortality. Modelled mortality and recruitment rates were linked to plant traits alone, drought-related mortality was not accounted for. In future, model development should focus on improving allocation, mortality, organ respiration, simulation of understory trees and adding hydraulic traits. This type of model that incorporates diverse tree strategies, detailed forest structure and realistic physiology is necessary if we are to be able to simulate tropical forest responses to global change scenarios.

Journal ArticleDOI
01 Apr 2019-Ecology
TL;DR: The results reveal that dominant species play a key role in structuring western Amazonian tree communities, which in turn has important implications, both practically for designing effective protected areas, and more generally for understanding the determinants of beta diversity patterns.
Abstract: The forests of western Amazonia are among the most diverse tree communities on Earth, yet this exceptional diversity is distributed highly unevenly within and among communities. In particular, a small number of dominant species account for the majority of individuals, whereas the large majority of species are locally and regionally extremely scarce. By definition, dominant species contribute little to local species richness (alpha diversity), yet the importance of dominant species in structuring patterns of spatial floristic turnover (beta diversity) has not been investigated. Here, using a network of 207 forest inventory plots, we explore the role of dominant species in determining regional patterns of beta diversity (community‐level floristic turnover and distance‐decay relationships) across a range of habitat types in northern lowland Peru. Of the 2,031 recorded species in our data set, only 99 of them accounted for 50% of individuals. Using these 99 species, it was possible to reconstruct the overall features of regional beta diversity patterns, including the location and dispersion of habitat types in multivariate space, and distance‐decay relationships. In fact, our analysis demonstrated that regional patterns of beta diversity were better maintained by the 99 dominant species than by the 1,932 others, whether quantified using species‐abundance data or species presence–absence data. Our results reveal that dominant species are normally common only in a single forest type. Therefore, dominant species play a key role in structuring western Amazonian tree communities, which in turn has important implications, both practically for designing effective protected areas, and more generally for understanding the determinants of beta diversity patterns.

Journal ArticleDOI
TL;DR: In this paper, a network of permanent monitoring plots at the Amazon-Cerrado transition was used to quantify recent biomass carbon changes and explore their environmental drivers, showing that biomass gains in remaining old-growth Amazonia forests have declined due to environmental change.
Abstract: Over recent decades, biomass gains in remaining old-growth Amazonia forests have declined due to environmental change. Amazonia’s huge size and complexity makes understanding these changes, drivers, and consequences very challenging. Here, using a network of permanent monitoring plots at the Amazon–Cerrado transition, we quantify recent biomass carbon changes and explore their environmental drivers. Our study area covers 30 plots of upland and riparian forests sampled at least twice between 1996 and 2016 and subject to various levels of fire and drought. Using these plots, we aimed to: (1) estimate the long-term biomass change rate; (2) determine the extent to which forest changes are influenced by forest type; and (3) assess the threat to forests from ongoing environmental change. Overall, there was no net change in biomass, but there was clear variation among different forest types. Burning occurred at least once in 8 of the 12 riparian forests, while only 1 of the 18 upland forests burned, resulting in losses of carbon in burned riparian forests. Net biomass gains prevailed among other riparian and upland forests throughout Amazonia. Our results reveal an unanticipated vulnerability of riparian forests to fire, likely aggravated by drought, and threatening ecosystem conservation at the Amazon southern margins.

Journal ArticleDOI
TL;DR: The lack of spatially congruent phylogeographic breaks across species suggests no common biogeographic history of these Amazonian tree species, which could be related to proposed Pleistocene refugia or the presence of geological arches in western Amazonia.
Abstract: Various historical processes have been put forth as drivers of patterns in the spatial distribution of Amazonian trees and their population genetic variation. We tested whether five widespread tree species show congruent phylogeographic breaks and similar patterns of demographic expansion, which could be related to proposed Pleistocene refugia or the presence of geological arches in western Amazonia. We sampled Otoba parvifolia/glycycarpa (Myristicaceae), Clarisia biflora, Poulsenia armata, Ficus insipida (all Moraceae), and Jacaratia digitata (Caricaceae) across the western Amazon Basin. Plastid DNA (trnH–psbA; 674 individuals from 34 populations) and nuclear ribosomal internal transcribed spacers (ITS; 214 individuals from 30 populations) were sequenced to assess genetic diversity, genetic differentiation, population genetic structure, and demographic patterns. Overall genetic diversity for both markers varied among species, with higher values in populations of shade‐tolerant species than in pioneer species. Spatial analysis of molecular variance (SAMOVA) identified three genetically differentiated groups for the plastid marker for each species, but the areas of genetic differentiation were not concordant among species. Fewer SAMOVA groups were found for ITS, with no detectable genetic differentiation among populations in pioneers. The lack of spatially congruent phylogeographic breaks across species suggests no common biogeographic history of these Amazonian tree species. The idiosyncratic phylogeographic patterns of species could be due instead to species‐specific responses to geological and climatic changes. Population genetic patterns were similar among species with similar biological features, indicating that the ecological characteristics of species impact large‐scale phylogeography.

Journal ArticleDOI
TL;DR: It is shown that remotely sensed and field‐derived estimates of pairwise dissimilarity in community composition are closely matched, proving the applicability of imaging spectroscopy to provide β‐diversity data for entire landscapes of over 1000 ha containing contrasting forest types.
Abstract: Data Availability Statement:: Airborne data are available via the CEDA archive (project code MA14/21); plot data is archived on forestplots (Lopez‐Gonzalez et al., 2011) (codes SEP‐03, 04, 05, 07, 08, 09, 10, 11, 12) and from the Figshare Repository: https://doi.org/10.6084/m9.figshare.8427998.v1. Acknowledgements: We are grateful to the Sabah Forestry Department and the Sabah Biodiversity Centre for allowing us to conduct our research in Sepilok as well as to the South East Asia Rainforest Research Partnership for the logistical support. This work was supported by a grant through the Human Modified Tropical Forests programme of NERC (NE/K016377/1) as well as a Cambridge NERC-DTP studentship. Resurvey of the field plots was supported by an ERC Advanced Grant (291585, T-FORCES) awarded to O.L.P. S.E.D.T was supported by the Joint Imperial-NUS PhD Scholarship. J.R. was supported by fellowships from the Natural Environment Research Council (NERC) (NE/I021179, NE/L011611/1). We thank members of the NERC Airborne Research Facility and Data Analysis Node for the collection and processing of the data (project code MA14/21). Data processing was aided by the NERCs JASMIN computing cluster and the Imperial College London computing facilities. We would also like to thank Felix May for his advice during the early stages of this study. The quality of this manuscript was greatly improved by the comments of Gabriel Arellano and two other anonymous Reviewers.

Journal ArticleDOI
TL;DR: It is shown that neutral theory not only underestimates the number of rare species but also fails in predicting the excessive dominance of species on both regional and local levels, a clear violation of the ecological equivalence assumption of neutral theory.
Abstract: Neutral models are often used as null models, testing the relative importance of niche versus neutral processes in shaping diversity. Most versions, however, focus only on regional scale predictions and neglect local level contributions. Recently, a new formulation of spatial neutral theory was published showing an incompatibility between regional and local scale fits where especially the number of rare species was dramatically under-predicted. Using a forward in time semi-spatially explicit neutral model and a unique large-scale Amazonian tree inventory data set, we show that neutral theory not only underestimates the number of rare species but also fails in predicting the excessive dominance of species on both regional and local levels. We show that although there are clear relationships between species composition, spatial and environmental distances, there is also a clear differentiation between species able to attain dominance with and without restriction to specific habitats. We conclude therefore that the apparent dominance of these species is real, and that their excessive abundance can be attributed to fitness differences in different ways, a clear violation of the ecological equivalence assumption of neutral theory.

Journal ArticleDOI
02 Feb 2019
TL;DR: The primary intact forests of the Peruvian Amazon act as a carbon sink: a key ecosystem service of international importance as mentioned in this paper, and it is necessary to include this carbon sink in the national inventory of greenhouse gas emissions for two reasons.
Abstract: The primary intact forests of the Peruvian Amazon act as a carbon sink: a key ecosystem service of international importance. This sink has been quantified as 0.54 Mg C ha-1 year-1 (1990-2017) for the intact Amazonian forest in the protected areas and associated buffer zones of Peru. In other words, the conservation of intact forests in protected areas has helped to remove 9.6 million tonnes of carbon from the atmosphere per year, which is equivalent to approximately 85% of the emissions from fossil fuel combustion in Peru during 2012. It is necessary to include this carbon sink in the national inventory of greenhouse gas emissions for two reasons. Firstly, because it is a an important flux, it would help for estimating the carbon balance of Peru more accurately. Secondly, it would strengthen the need to maintain the integrity of these forests, for their role both as a stock and sink of carbon and for their biological diversity. The provision of this service as a sink can only be assured with effective and adaptive management of the protected areas of Peru. Reporting of this environmental service at a national level should be implemented through long-term monitoring of the carbon dynamics and impact of climate change on these forests via the RAINFOR (Amazon Forest Network) network of permanent forest plots and the MonANPeru project. The establishment of this monitoring system would allow the development of the finanancial mechanisms to close the funding gap and achieve sustainable conservation of the forests of the protected area network of Peru.

Posted ContentDOI
11 Jun 2019
TL;DR: In this article, the edaphic, mineralogical and climatic controls of soil organic carbon (SOC) concentration were investigated using data from 147 pristine forest soils sampled in eight different countries across the Amazon Basin.
Abstract: . We investigate the edaphic, mineralogical and climatic controls of soil organic carbon (SOC) concentration utilising data from 147 pristine forest soils sampled in eight different countries across the Amazon Basin. Sampling across 14 different World Reference Base soil groups our data suggest that stabilisation mechanism varies with pedogenetic level. Specifically, although SOC concentrations in Ferralsols and Acrisols were best explained by simple variations in clay content – this presumably being due to their relatively uniform kaolinitic mineralogy – this was not the case for less weathered soils such as Alisols, Cambisols and Plinthosols for which interactions between Al species, soil pH and litter quality seem to be much more important. SOC fractionation studies further showed that, although for more strongly weathered soils the majority of SOC is located within the aggregate fraction, for the less weathered soils most of the SOC is located within the silt and clay fractions. It thus seems that for highly weathered soils SOC storage is mostly influenced by surface area variations arising from clay content, with physical protection inside aggregates rendering an additional level of protection against decomposition. On the other hand, most of SOC in less weathered soils is associated with the precipitation of aluminium-carbon complexes within the fine soil fraction and with this mechanism enhanced by the presence of high levels of aromatic, carboxyl-rich organic matter compounds. Also examined as part of this study were a relatively small number of arenic soils (viz. Arenosols and Podzols) for which there was a small but significant influence of clay and silt content variations on SOM storage and with fractionation studies showing that particulate organic matter may accounting for up to 0.60 of arenic soil SOC. In contrast to what were in all cases strong influences of soil and/or litter quality properties, after accounting for these effects neither wood productivity, above ground biomass nor precipitation/temperature variations were found to exert any significant influence on SOC stocks at all. These results have important implications for our understanding of how Amazon forest soils are likely to respond to ongoing and future climate changes.

DatasetDOI
D. Shchepashchenko, Jérôme Chave, Oliver L. Phillips, Simon L. Lewis, Stuart J. Davies, Maxime Réjou-Méchain, Plinio Sist, Klaus Scipal, Christoph Perger, Bruno Hérault, Nicolas Labrière, Florian Hofhansl, Kofi Affum-Baffoe, A. Aleinikov, Alfonso Alonso, C. Amani, Alejandro Araujo-Murakami, John Armston, L. Arroyo, Nataly Ascarrunz, Celso Paulo de Azevedo, Timothy R. Baker, Radomir Bałazy, Olaf Bánki, Caroline Bedeau, Nicholas J. Berry, Andrii Bilous, Svitlana Bilous, Pulchérie Bissiengou, Lilian Blanc, K. S. Bobkova, T. Braslavskaya, Roel J. W. Brienen, David F. R. P. Burslem, Richard Condit, Aida Cuni-Sanchez, Dilshad M. Danilina, Dennis Del Castillo Torres, Géraldine Derroire, Laurent Descroix, E. Doff Sotta, Marcus Vn d'Oliveira, C. Dresel, Terry L. Erwin, Evdokimenko, Jan Falck, Ted R. Feldpausch, Ernest G. Foli, Robin B. Foster, Steffen Fritz, Antonio García-Abril, A. V. Gornov, M. Gornova, E. Gothard-Bassébé, Sylvie Gourlet-Fleury, Marcelino Carneiro Guedes, Keith C. Hamer, Farida Herry Susanty, Niro Higuchi, E.N. Honorio Coronado, Wannes Hubau, Stephen P. Hubbell, Ulrik Ilstedt, V. Ivanov, Milton Kanashiro, Anders Karlsson, V.N. Karminov, Timothy J. Killeen, J.K. Konan, M. E. Konovalova, Florian Kraxner, J. Krejza, Haruni Krisnawati, Leonid Krivobokov, Kuznetsov, Ivan Lakyda, Petro Lakyda, Juan Carlos Licona, Richard Lucas, N. V. Lukina, D. Lussetti, Yadvinder Malhi, José Antonio Manzanera, Ben Hur Marimon, B.H. Marimon Junior, Rodolfo Vásquez Martínez, Olga Martynenko, Matsala, R.K. Matyashuk, Lucas Mazzei, Hervé Memiaghe, Casimiro Mendoza, Abel Monteagudo-Mendoza, O.V. Morozyuk, Liudmila Mukhortova, S. Musa, D. I. Nazimova, Toshinori Okuda, Luís Cláudio de Oliveira, P. V. Ontikov, Andrey Osipov, Alexander Parada Gutierrez, Stephan A. Pietsch, Maureen Playfair, John R. Poulsen, Vladimir G. Radchenko, Ken Rodney, Andes Hamuraby Rozak, Ademir Roberto Ruschel, Ervan Rutishauser, Linda See, Maria Shchepashchenko, N. E. Shevchenko, Anatoly Shvidenko, Javier E. Silva-Espejo, Marcos Silveira, James Singh, Bonaventure Sonké, Cintia Rodrigues de Souza, Krzysztof Stereńczak, Martin J. P. Sullivan, J. Szatniewska, Hermann Taedoumg, H. ter Steege, Elena B. Tikhonova, Marisol Toledo, Olga V. Trefilova, Ruben Valbuena, L.V. Valenzuela Gamarra, E.F. Vedrova, S.V. Verhovets, Edson Vidal, N.A. Vladimirova, Jason Vleminckx, Vincent A. Vos, F.K. Vozmitel, Wolfgang Wanek, Thales A.P. West, Hannsjorg Woell, John T. Woods, Verginia Wortel, Toshihiro Yamada, N.H. Zamah Shari, Irie Casimir Zo-Bi 
13 Mar 2019
TL;DR: The Forest Observation System (FOS) initiative as discussed by the authors is an international cooperation to establish and maintain a global in situ forest biomass database for monitoring the Earth's ecosystems and climate.
Abstract: Forest biomass is an essential indicator for monitoring the Earth’s ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25ha scale from field measurements made in permanent research plots across the world's forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities. Live, most up-to-date dataset is available at https://forest-observation-system.net/