Serviços ambientais associados ao manejo da castanha-da-Amazônia (Bertholletia excelsa Bonpl.) no sul de Roraima, Brasil

Resumo: Considerando o papel histórico, econômico e social dos castanhais nativos, este trabalho se propôs a quantificar a importância do castanhal e da atividade extrativista de coleta de castanhas-da-amazônia para a manutenção dos estoques de biomassa/carbono e da diversidade de espécies de árvores. O estudo foi realizado em uma área de floresta localizada no município de Caracaraí/RR. Todos os indivíduos arbóreos (incluindo palmeiras) com diâmetro maior ou igual a 10 cm foram marcados, mapeados, medidos e identificados em 8 parcelas de 1 hectare distribuídas em uma área de 4 km2 para determinação do estoque de biomassa e da composição de espécies. Os dados obtidos podem contribuir para o desenvolvimento de critérios de valoração, bem como para o desenvolvimento de políticas públicas para o pagamento de serviços ambientais em castanhais nativos, elucidando a importância dos castanhais e da castanheira para a conservação da sociodiversidade e para o sequestro e estoque de carbono

LRRK2 Biology from structure to dysfunction: research progresses, but the themes remain the same

Since the discovery of leucine-rich repeat kinase 2 (LRRK2) as a protein that is likely central to the aetiology of Parkinson's disease, a considerable amount of work has gone into uncovering its basic cellular function. This effort has led to the implication of LRRK2 in a bewildering range of cell biological processes and pathways, and probable roles in a number of seemingly unrelated medical conditions. In this review we summarise current knowledge of the basic biochemistry and cellular function of LRRK2. Topics covered include the identification of phosphorylation substrates of LRRK2 kinase activity, in particular Rab proteins, and advances in understanding the activation of LRRK2 kinase activity via dimerisation and association with membranes, especially via interaction with Rab29. We also discuss biochemical studies that shed light on the complex LRRK2 GTPase activity, evidence of roles for LRRK2 in a range of cell signalling pathways that are likely cell type specific, and studies linking LRRK2 to the cell biology of organelles. The latter includes the involvement of LRRK2 in autophagy, endocytosis, and processes at the trans-Golgi network, the endoplasmic reticulum and also key microtubule-based cellular structures. We further propose a mechanism linking LRRK2 dimerisation, GTPase function and membrane recruitment with LRRK2 kinase activation by Rab29. Together these data paint a picture of a research field that in many ways is moving forward with great momentum, but in other ways has not changed fundamentally. Many key advances have been made, but very often they seem to lead back to the same places

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

Amazon tree dominance across forest strata

The forests of Amazonia are among the most biodiverse plant communities on Earth. Given the immediate threats posed by climate and land-use change, an improved understanding of how this extraordinary biodiversity is spatially organized is urgently required to develop effective conservation strategies. Most Amazonian tree species are extremely rare but a few are common across the region. Indeed, just 227 ‘hyperdominant’ species account for >50% of all individuals >10 cm diameter at 1.3 m in height. Yet, the degree to which the phenomenon of hyperdominance is sensitive to tree size, the extent to which the composition of dominant species changes with size class and how evolutionary history constrains tree hyperdominance, all remain unknown. Here, we use a large floristic dataset to show that, while hyperdominance is a universal phenomenon across forest strata, different species dominate the forest understory, midstory and canopy. We further find that, although species belonging to a range of phylogenetically dispersed lineages have become hyperdominant in small size classes, hyperdominants in large size classes are restricted to a few lineages. Our results demonstrate that it is essential to consider all forest strata to understand regional patterns of dominance and composition in Amazonia. More generally, through the lens of 654 hyperdominant species, we outline a tractable pathway for understanding the functioning of half of Amazonian forests across vertical strata and geographical locations

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

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

Protium Burm.f.

<i>2.4. Comparison of Protium monoterpene ‘fingerprints’ between individuals</i> <p> Of the 77 <i>Protium</i> individuals, we observed a consistency of <i>Protium</i> monoterpene ‘fingerprints’ that could be separated in two cases: a) similar monoterpene ‘fingerprints’ patterns between individuals of different species, b) similar monoterpene ‘fingerprints’ patterns between individuals of the same species. Graphical representations of example monoterpene ‘fingerprints’ showing consistency between individuals of different species (Figs. S2–S 4) and the same species (Figs. S5–S 6) is provided in the supporting information.</p> <p> As an example of similar patterns between individuals from different species, the comparison of the monoterpene ‘fingerprint’ from <i>P. calendulinum</i> species (Tree 1408) and that of <i>P. hebetatum</i> var. 1 (Tree 277C) showed a strong consistency of monoterpene patterns (Figs. S2a and S2d). Both ‘fingerprints’ presented the same dominant monoterpene (α-pinene) and similar relative abundance for other monoterpenes, including camphene, β-pinene, and <i>d</i> -limonene (MTPs 8, 12 and 18, respectively).</p> <p> In a second example, the monoterpene ‘fingerprint’ of a <i>P. paniculatum</i> var. <i>paniculatum</i> individual (Tree 1301) showed similarities with the ‘fingerprint’ of a second individual, from <i>P. paniculatum</i> var. <i>paniculatum</i> species (Tree 157), with α-phellandrene as the dominant monoterpene in both cases (compare Fig. S2b with S2e). There was also a good similarity between the relative abundances of the monoterpenes α-terpinene, β-phellandrene, γ-terpinene, α-terpinolene and isoterpinolene (MTPs 16, 19, 22, 24 and 25, respectively). Another interesting example showed that the ‘fingerprint’ of a <i>P. apiculatum</i> (Tree 464) had a good similarity with the ‘fingerprint’ of a <i>P. nitidifolium</i> (Tree 334D) individual, with <i>d</i> -limonene as the dominant monoterpene (compare Fig. S2c with S2f). More examples of monoterpene ‘fingerprints’ similarities for individuals from different species can be observed in Figs. S3 and S 4.</p> <p> A comparison analysis of monoterpene ‘fingerprints’ with similar patterns between individuals from the same species was also conducted. Monoterpene ‘fingerprints’ from two individuals of <i>P. hebetatum</i> var. 2 (Trees 661 and 724) showed a consistency of monoterpene patterns (compare Fig. S5a with S5d), with α-pinene as the dominant monoterpene. Furthermore, for both ‘fingerprints’, the monoterpenes β-pinene, α-phellandrene and α-terpinene were detected. For another two monoterpene ‘fingerprints’ (Trees 261 and 931B), also from <i>P. hebetatum</i> var. 2 species and with α-pinene as the dominant monoterpene, we can observe a consistency of monoterpenes occurrence and monoterpenes relative abundance (compare Fig. S5b with S5e). Other examples of similarities for ‘fingerprints’ from the same species are highlighted in Fig. S6 (a-f). In this figure, it is interesting to observe that four individuals from <i>P. paniculatum</i> var. <i>modestum</i> species presented strong similarities in their monoterpene ‘fingerprints’. For example, both Trees 1289 and 1295 (Figs. S6a and S6b) presented the occurrence of monoterpenes 3-carene, α-terpinene, β-phellandrene, cis-β-ocimene and γ-terpinene, with strong relative abundance similarities of these monoterpenes. For these four ‘fingerprints’ from the same species, the same dominant monoterpene (α-phellandrene) was observed, as well.</p>Published as part of <i>Piva, Luani R. de O., Jardine, Kolby J., Gimenez, Bruno O., Perdiz, Ricardo de Oliveira, Menezes, Valdiek S., Durgante, Flávia M., Cobello, Leticia O., Higuchi, Niro & Chambers, Jeffrey Q., 2019, Volatile monoterpene ' fingerprints' of resinous Protium tree species in the Amazon rainforest, pp. 61-70 in Phytochemistry 160</i> on pages 63-64, DOI: 10.1016/j.phytochem.2019.01.014, <a href="http://zenodo.org/record/10481082">http://zenodo.org/record/10481082</a&gt