Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests

The seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter productivity measurements), their associated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonality in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positively to precipitation when rainfall is < 2000ĝ€-mmĝ€-yrĝ'1 (water-limited forests) and to radiation otherwise (light-limited forests). On the other hand, independent of climate limitations, wood productivity and litterfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosynthetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest productivity in a drier climate in water-limited forest, and in current light-limited forest with future rainfall < 2000ĝ€-mmĝ€-yrĝ'1. Author(s) 2016.Fil: Wagner, Fabien H.. Instituto Nacional de Pesquisas Espaciais; BrasilFil: Hérault, Bruno. Ecologie Des Forets de Guyane; BrasilFil: Bonal, Damien. Institut National de la Recherche Agronomique; FranciaFil: Stahl, Clment. Universiteit Antwerp; BélgicaFil: Anderson, Liana O.. National Center For Monitoring And Early Warning Of Natural Disasters; BrasilFil: Baker, Timothy R.. University Of Leeds; Reino UnidoFil: Sebastian Becker, Gabriel. Universidad de Hohenheim; AlemaniaFil: Beeckman, Hans. Royal Museum For Central Africa; BélgicaFil: Boanerges Souza, Danilo. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Cesar Botosso, Paulo. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Bowman, David M. J. S.. University of Tasmania; AustraliaFil: Bräuning, Achim. Universitat Erlangen-Nuremberg; AlemaniaFil: Brede, Benjamin. Wageningen University And Research Centre; Países BajosFil: Irving Brown, Foster. Universidade Federal Do Acre; BrasilFil: Julio Camarero, Jesus. Instituto Boliviano de Investigacion Forestal Bolivia; BoliviaFil: Camargo, Plnio Barbosa. Universidade de Sao Paulo; BrasilFil: Cardoso, Fernanda C.G.. Universidade Federal do Paraná; BrasilFil: Carvalho, Fabrcio Alvim. Universidade Federal de Juiz de Fora; BrasilFil: Castro, Wendeson. Universidade Federal Do Acre; BrasilFil: Koloski Chagas, Rubens. Universidade de Sao Paulo; BrasilFil: Chave, Jrome. Centre National de la Recherche Scientifique; FranciaFil: Chidumayo, Emmanuel N.. University Of Zambia; ZambiaFil: Clark, Deborah A.. University Of Missouri-st. Louis; Estados UnidosFil: Regina Capellotto Costa, Flavia. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Couralet, Camille. Royal Museum For Central Africa; BélgicaFil: Henrique Da Silva Mauricio, Paulo. Universidade Federal Do Acre; BrasilFil: Dalitz, Helmut. Universidad de Hohenheim; AlemaniaFil: Resende De Castro, Vinicius. Universidade Federal de Vicosa; BrasilFil: Milani, Jaanan Eloisa De Freitas. Universidade Federal do Paraná; BrasilFil: Roig Junent, Fidel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla | Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla | Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla; Argentin

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests

The seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter productivity measurements), their associated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonality in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positively to precipitation when rainfall is  < 2000 mm yr⁻¹ (water-limited forests) and to radiation otherwise (light-limited forests). On the other hand, independent of climate limitations, wood productivity and litterfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosynthetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest productivity in a drier climate in water-limited forest, and in current light-limited forest with future rainfall  < 2000 mm yr⁻¹

COPA : uma arquitetura para monitoramento de redes sem fio cabeadas e gerenciamento de containers

New applications are demanding several improvements in the quality of computer network technologies and infrastructure. These new applications that are expected to operate in dynamic environments and computing solutions must provide similar performance even in different network and resources qualities scenarios. Convergent wireless and wireline networks provides mobile and high data rate transfer and solves some of this new requirements such as mobility and low-latency network communication. Also, remote computing such as Cloud and Fog computing has arisen as an approach to deal with latency issues in this context. This group of computing technologies enables several new studies areas. Therefore, to support the research and development solutions for future computer networks, we design COPA, a wireless/wireline network convergent monitoring and container manager architecture for testbeds. This architecture enable the testbeds experimental research by providing real-time wireless and wireline network monitoring and experimenter-level orchestration. The monitoring of the network provides network quality data for decision-making algorithms. Also, we provide a friendly user interface for following the experiment network scenario. While, the experimenter-level orchestration enables the emulation of Cloud and Fog interplay by providing tools for management of virtualized environments such as containers. We also present a use case of COPA. In this use case we developed a smart lighting IoT system that allows control of light bulbs (turn on/off, color and brightness change). This IoT device could be controlled by voice commands which are processed and translated to light bulb language in a remote server. This server could be at a Cloud computing, Fog computing datacenter or close to the device at some kind of smart gateway. We show in this use case, how COPA can provide the necessary data for convergent wireless/wireline networks related research. Finally, we conclude that COPA provide a excellent environment for researches of wireless/wireline convergent network and orchestration algorithms for containerized services.Novas aplicações estão demandando muitas melhoras na qualidade das tecnologias e infraestrutura de redes de computadores. É esperado que essas novas aplicações operem em ambientes dinâmicos, e soluções de computação precisam prover uma performance similar mesmo em diferentes qualidades de recursos computacionais ou de rede. Redes convergentes sem-fio e cabeado provem conectividade móvel e alta taxa de transferência de dados solucionando alguns requisitos dessas novas tecnologias tais como mobilidade e baixa latência de comunicação de rede. Soluções de computação remota tais como Cloud e Fog computing vem sendo utilizadas para melhorar problemas de atraso de rede nesse contexto. Esse grupo de tecnologias de informática habilitam o estudo em diversas novas áreas. Então, para apoiar a pesquisa e desenvolvimento de soluções para redes de computadores do futuro, COPA foi arquitetado, uma arquitetura para monitoramento de redes convergentes sem-fio/cabeadas e gerenciamento de containers para laboratórios experimentais. Essa arquitetura habilita a pesquisa em ambientes experimentais provendo monitoramento de rede sem-fio e cabeada em tempo real e orquestração em nível de experimentador. Também é apresentado um uso de caso do COPA. Nesse uso de caso, é desenvolvido um sistema de luzes inteligentes que permite o controle de uma lampada (liga/desliga, cor e intensidade do brilho). Esse dispositivo pode ser controlado por comandos de voz os quais são processados em um servidor remoto e traduzidos para a linguagem da luz inteligente. Esse servidor poderia estar localizado em um centro de dados de Cloud computing, Fog computing or perto do usuário final em algum tipo de smart gateway. Nesse uso de caso, é mostrado como o COPA pode prover os dados necessários para pesquisas relacionadas a convergência de redes sem-fio/cabeadas. Por fim, é concluído que o COPA fornece um excelente ambiente para pesquisas de redes convergentes sem-fio/cabeadas e algorítmos de orquestração de serviços virtualizados