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

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

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

Flora da Usina São José, Igarassu, Pernambuco: Lecythidaceae, Marcgraviaceae e Primulaceae

Resumo A ordem Ericales nos remanescentes de Floresta Atlântica da Usina São José foi previamente tratada com a monografia da família Sapotaceae. Dando continuidade aos estudos dessa flora, neste trabalho o tratamento da ordem Ericales é atualizado com a monografia das famílias Lecythidaceae, Marcgraviaceae e Primulaceae. Nestas três famílias foram registradas oito espécies classificadas em seis gêneros. Em Lecythidaceae foram identificados três gêneros e quatro espécies, em Marcgraviaceae dois gêneros e duas espécies e Primulaceae um gênero e duas espécies. Eschweilera e Myrsine foram registradas duas espécies cada, enquanto os demais gêneros foram representados por apenas uma espécie. Este trabalho é composto por uma chave de identificação e descrições morfológicas das espécies, além de comentários ecológicos e taxonômicos sobre os gêneros e espécies registrados

The RET p.G533C Mutation Confers Predisposition to Multiple Endocrine Neoplasia Type 2A in a Brazilian Kindred and Is Able to Induce a Malignant Phenotype in Vitro and in Vivo

Background: We have previously described a p.G533C substitution in the rearranged during transfection (RET) oncogene in a large family with medullary thyroid carcinoma. Here, we explore the functional transforming potential of RET p.G533C mutation.Methods: Plasmids expressing RET mutants (p.G533C and p.C634Y) and RET wild type were stable transfected into a rat thyroid cell line (PCCL3). Biological and biochemical effects of RET p.G533C were investigated both in vitro and in vivo. Moreover, we report the first case of pheochromocytoma among the RET p.G533C-carriers in this Brazilian family and explore the RET mutational status in DNA isolated from pheochromocytoma.Results: Ectopic expression of RET p.G533C and p.C634Y activates RET/MAPK/ERK pathway at similar levels and significantly increased cell proliferation, compared with RET wild type. We additionally show that p.G533C increased cell viability, anchorage-independent growth, and micronuclei formation while reducing apoptosis, hallmarks of the malignant phenotype. RET p.G533C down-regulates the expression of thyroid specific genes in PCCL3. Moreover, RET p.G533C-expressing cells were able to induce liver metastasis in nude mice. Finally, we described two novel RET variants (G548V and S556T) in the DNA isolated from pheochromocytoma while they were absent in the DNA isolated from blood.Conclusions: Our in vitro and in vivo analysis indicates that this mutation confers a malignant phenotype to PCCL3 cells. These findings, in association with the report of first case of pheochromocytoma in the Brazilian kindred, suggest that this noncysteine mutation may be more aggressive than was initially considered.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Universidade Federal de São Paulo, Div Genet, Genet Bases Thyroid Tumors Lab, BR-04039032 São Paulo, BrazilUniversidade Federal de São Paulo, Div Endocrinol, Mol Endocrinol Lab, BR-04039032 São Paulo, BrazilUniv Fed Minas Gerais, Div Endocrinol, Belo Horizonte, MG, BrazilUniversidade Federal de São Paulo, Div Genet, Genet Bases Thyroid Tumors Lab, BR-04039032 São Paulo, BrazilUniversidade Federal de São Paulo, Div Endocrinol, Mol Endocrinol Lab, BR-04039032 São Paulo, BrazilFAPESP: 05/60330-8FAPESP: 06/60402-1FAPESP: 09/11257-7Web of Scienc