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

Trajetórias da Educomunicação nas Políticas Públicas e a Formação de seus Profissionais

Esta obra é composta com os trabalhos apresentados no primeiro subtema, TRAJETÓRIA – Educação para a Comunicação como Política pública, nas perspectivas da Educomunicação e da Mídia-Educação, do II Congresso Internacional de Comunicação e Educação. Os artigos pretendem propiciar trocas de informações e produzir reflexões com os leitores sobre os caminhos percorridos, e ainda a percorrer, tendo como meta a expansão e a legitimação das práticas educomunicativas e/ou mídia-educativas como política pública para o atendimento à formação de crianças, adolescentes, jovens e adultos, no Brasil e no mundo

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

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

TREE SPECIES SUSCEPTIBILITY TO LEAF-CUTTING ANTS ATTACK IN CARBON NEUTRALIZATION PLANTATIONS

Greenhouse gas (GHG) emissions neutralize planting are one of the options for climate changes mitigating. Leaf-cutter ants attack is a threat to the plantations proper development. Ants have selective foraging, which makes it important to know this selectivity and, thus, choose more suitable species to neutralization planting compose. Thus, the goal of the present study was to evaluate the tree species susceptibility to be attacked by leaf-cutter ants in carbon neutralization plantations. The study was carried out in mixed plantations in Viçosa-MG and a classification was created for the present species. The Damage Index (DI) was created by multiplying the Mean of Severity (MS) and the Frequency of Attacks (FA). The species were classified according to the indication for neutralization plantations (indicated, moderately indicated, and not indicated) and potential species for the extraction of natural insecticides. From the 59 species evaluated, 22 were classified as suitable for neutralization plantations, 6 as moderately indicated, 24 as not indicated, and 7 as potential species for extracting natural insecticides. Keywords: forest carbon; pest control; carbon offset; mixed plantings.ABSTRACT: Greenhouse gas (GHG) emissions neutralize planting are one of the options for climate changes mitigating. Leaf-cutter ants attack is a threat to the plantations proper development. Ants have selective foraging, which makes it important to know this selectivity and, thus, choose more suitable species to neutralization planting compose. Thus, the goal of the present study was to evaluate the tree species susceptibility to be attacked by leaf-cutter ants in carbon neutralization plantations. The study was carried out in mixed plantations in Viçosa-MG and a classification was created for the present species. The Damage Index (DI) was created by multiplying the Mean of Severity (MS) and the Frequency of Attacks (FA). The species were classified according to the indication for neutralization plantations (indicated, moderately indicated, and not indicated) and potential species for the extraction of natural insecticides. From the 59 species evaluated, 22 were classified as suitable for neutralization plantations, 6 as moderately indicated, 24 as not indicated, and 7 as potential species for extracting natural insecticides. Keywords: forest carbon; pest control; carbon offset; mixed plantings.   Susceptibilidade de espécies arbóreas ao ataque de formigas cortadeiras em plantações de neutralização de carbono   RESUMO: O plantio para neutralizar as emissões de gases de efeito estufa (GEE) é uma das opções para mitigar as mudanças climáticas. O ataque de formigas cortadeiras é uma ameaça ao bom desenvolvimento dessas plantações. As formigas possuem forrageamento seletivo, o que torna importante conhecer essa seletividade e, assim, escolher espécies mais adequadas para compor o plantio de neutralização. Assim, o objetivo do presente estudo foi avaliar a susceptibilidade de espécies arbóreas ao ataque de formigas cortadeiras em plantios de neutralização de carbono. O estudo foi realizado em plantios mistos do programa Carbono Zero em Viçosa-MG e, com base nessa avaliação, foi criada uma classificação para as espécies presentes. O Índice de Danos (DI) foi criado pela multiplicação da Média de Severidade (MS) e da Frequência de Ataques (FA). As espécies foram classificadas quanto à indicação para plantios de neutralização (indicada, moderadamente indicada e não indicada) e espécies potenciais para extração de inseticidas naturais. Das 59 espécies avaliadas, 22 foram classificadas como aptas para plantios de neutralização, 6 como moderadamente indicadas, 24 como não indicadas e 7 como espécies potenciais para extração de inseticidas naturais. Palavras-chave: carbono florestal; controle de pragas; compensação de carbono; plantios mistos

Brazilian carbon footprint calculators: comparative approaches and implications of using these tools

The increasing emergence of carbon calculators and the absence of specific standards to regulate these tools, may lead to inconsistencies in the results. This article evaluates individual carbon calculators that are publicly available in Brazil. Qualitative and quantitative analyses of 15 calculators were performed. Input and output data, conversion factors, as well as the costs associated with the possibilities of mitigation were evaluated. The analyses showed that there is a great discrepancy between the Emission Factors (EF), which have been highlighted by the coefficient of variation of EF adopted for the emission of liquefied petroleum gas (146.5%). The values of carbon stock also showed a large amplitude (139.45 to 359.84 kgCO2 tree−1). Furthermore, it was observed that tips on the possibility of reducing emissions are poorly provided by the calculators (27%). However, most tools (67%) make it possible to offset quantified emissions, and carbon offset plantations are widely suggested. The discrepancies found may affect calculators reliability, their potential for raising environmental awareness and their influence on decision-making. Thus, the diffusion of the calculators should be accompanied by more specific guidelines in order to minimize the uncertainties associated with the estimates

Machine Learning: Volume and Biomass Estimates of Commercial Trees in the Amazon Forest

Accurate estimation of the volume and above-ground biomass of exploitable trees by the practice of selective logging is essential for the elaboration of a sustainable management plan. The objective of this study is to develop machine learning models capable of estimating the volume and biomass of commercial trees in the Southwestern Amazon, based on dendrometric, climatic and topographic characteristics. The study was carried out in the municipality of Porto Acre, Acre state, Brazil. The volume and biomass of sample trees were determined using dendrometric, climatic and topographic variables. The Boruta algorithm was applied to select the best set of variables. Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forests (RF) and the Generalized Linear Model (GLM) were the machine learning methods evaluated. In general, the evaluated methods showed a satisfactory generalization power. The results showed that the volume and biomass predictions of commercial trees in the Amazon rainforest differed between the techniques (p ryŷ and the lowest RSME and MAE. Thus, machine learning methods such as SVM, ANN, RF and GLM are shown to be useful and efficient tools for estimating the volume and biomass of commercial trees in the Amazon rainforest. These methods can be useful tools to improve the accuracy of estimates in forest management plans

Stocks of Carbon in Logs and Timber Products from Forest Management in the Southwestern Amazon

Amazon forest management plans have a variety of effects on carbon emissions, both positive and negative. All of these effects need to be quantified to assess the role of this land use in climate change. Here, we contribute to this effort by evaluating the carbon stocks in logs and timber products from an area under forest management in the southeastern portion of Acre State, Brazil. One hundred and thirty-six trees of 12 species had DBH ranging from 50.9 cm to 149.9 cm. Basic wood density ranged from 0.3 cm−3 to 0.8 g cm−3 with an average of 0.6 g cm−3. The logs had a total volume of 925.2 m3, biomass of 564 Mg, and carbon stock of 484.2 MgC. The average volumetric yield coefficient (VYC) was 52.3% and the carbon yield coefficient (CYC) was 53.2% for logs of the 12 species. The sawn-wood products had a total volume of 484.2 m3, biomass of 302.6 Mg, and carbon stock of 149.9 MgC. Contributions of the different species to the total carbon stored in sawn-wood products ranged from 2.2% to 21.0%. Means and standard deviations for carbon transferred to sawn-wood products per-species from the 1252.8-ha harvested area ranged from 0.4 ± 1.1 MgC to 2.9 ± 0.4 MgC, with the largest percentages of the total carbon stored in wood products being from Dipteryx odorata (21.0%), Apuleia leiocarpa (18.7%), and Eschweilera grandiflora (11.7%). A total of 44,783 pieces of sawn lumber (such as rafters, planks, boards, battens, beams, and small beams) was obtained from logs derived from these trees. Lumber production was highest for boards (54.6% of volume, 47.4% of carbon) and lowest for small beams (1.9% of volume, 2.3% of carbon). The conversion factor for transforming log volume into carbon stored in sawn-wood products was 16.2%. Our results also show that species that retain low amounts of carbon should be allowed to remain in the forest, thereby avoiding low sawmill yield (and consequent generation of waste) and allowing these trees to continue fulfilling environmental functions