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

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

ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest

Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ

ASSOCIATION OF PESTICIDES WITH SOYBEAN LEAF MICRONUTRIENTS CONTENTS AND SEEDS YIELD AND PHYSIOLOGIC QUALITY ASSOCIAÇÃO DE AGROTÓXICOS AOS TEORES FOLIARES DE MICRONUTRIENTES E À PRODUTIVIDADE E QUALIDADE FISIOLÓGICA DE SEMENTES DE SOJA

&lt;p style="margin-bottom: 0cm; font-style: normal; font-weight: normal; line-height: 120%; text-decoration: none;" lang="pt-BR"&gt;&lt;span style="color: #000000;"&gt;&lt;span style="font-family: Times New Roman,serif;"&gt;&lt;span style="font-size: small;"&gt;Glyphosate effects on leaf micronutrients contents of transgenic soybean have been widely reported, however, little is known about these effects associated with other pesticides. The objective of this study was to evaluate the physiologic quality and yield of soybean seeds, as well as leaf micronutrients contents, according to weed control methods. Ten RBD treatments were arranged in a split-plot scheme with four replications. The application or non-application of endosulphan + tebuconazole was evaluated in the plots, while the weeds control methods were assessed in the subplots (weeded control; non-weeded control; single application of glyphosate (1,080 g ha&lt;sup&gt;-1&lt;/sup&gt;) and fomesafen + fluazifop-?-butil (180 + 225 g ha&lt;sup&gt;-1&lt;/sup&gt;), both at 15 DAE; and sequential application of glyphosate (1,080 g ha&lt;sup&gt;-1&lt;/sup&gt;) at 15, 30, and 45 DAE). After harvesting, the soybean seeds were sampled, in order to evaluate their germination, vigor, and yield. Copper and manganese contents were only influenced by the sequential application of glyphosate, associated with endosulphan + tebuconazole. The cold test germination was reduced in seeds of plants treated with fomesafen + fluazifop-p-butil associated with endosulphan + tebuconazole. Among the treatments without endosulphan + tebuconazole, the sequential application of glyphosate promoted the highest 100-seeds weight, as well as, when associated with endosulphan + tebuconazole, reduced the leaf concentrations of Cu and Mn, however, it improved seeds germination and showed no effects on seeds vigor, when compared with the weeded soybean.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;<br>&lt;p style="margin-bottom: 0cm; font-style: normal; font-weight: normal; line-height: 120%; text-decoration: none;" lang="pt-BR"&gt;&lt;span style="color: #000000;"&gt;&lt;span style="font-family: Times New Roman,serif;"&gt;&lt;span style="font-size: small;"&gt;&lt;span style="color: #000000;"&gt;&lt;span style="text-decoration: none;"&gt;&lt;span style="font-family: Times New Roman,serif;"&gt;&lt;span lang="pt-BR"&gt;&lt;span style="font-style: normal;"&gt;&lt;span style="font-weight: normal;"&gt;Efeitos do glyphosate nos teores foliares de micro-nutrientes de soja transgênica têm sido amplamente relatados, porém, pouco se sabe sobre seus efeitos associados a outros agrotóxicos. Objetivou-se, neste trabalho, avaliar a produção e a qualidade fisiológica de sementes de soja, bem como os teores de micronutrientes nas folhas, em função de métodos de controle de plantas daninhas. Foram avaliados dez tratamentos DBC, em esquema de parcelas subdivididas, com quatro repetições. Nas parcelas, avaliou-se o efeito da aplicação ou não de endossulfan + tebuconazole e, nas subparcelas, o efeito dos métodos de controle de plantas daninhas (testemunha não capinada; testemunha capinada; aplicação única de glyphosate (1.080 g ha&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style="color: #000000;"&gt;&lt;span style="text-decoration: none;"&gt;&lt;sup&gt;&lt;span style="font-family: Times New Roman,serif;"&gt;&lt;span lang="pt-BR"&gt;&lt;span style="font-style: normal;"&gt;&lt;span style="font-weight: normal;"&gt;-1&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/sup&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style="color: #000000;"&gt;&lt;span style="text-decoration: none;"&gt;&lt;span style="font-family: Times New Roman,serif;"&gt;&lt;span lang="pt-BR"&gt;&lt;span style="font-style: normal;"&gt;&lt;span style="font-weight: normal;"&gt;) e fomesafen + fluazifop-?-butil (180 + 225 g ha&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style="color: #000000;"&gt;&lt;span style="text-decoration: none;"&gt;&lt;sup&gt;&lt;span style="font-family: Times New Roman,serif;"&gt;&lt;span lang="pt-BR"&gt;&lt;span style="font-style: normal;"&gt;&lt;span style="font-weight: normal;"&gt;-1&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/sup&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style="color: #000000;"&gt;&lt;span style="text-decoration: none;"&gt;&lt;span style="font-family: Times New Roman,serif;"&gt;&lt;span lang="pt-BR"&gt;&lt;span style="font-style: normal;"&gt;&lt;span style="font-weight: normal;"&gt;), ambas aos 15 DAE; e aplicação sequencial de glyphosate (1.080 g ha&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style="color: #000000;"&gt;&lt;span style="text-decoration: none;"&gt;&lt;sup&gt;&lt;span style="font-family: Times New Roman,serif;"&gt;&lt;span lang="pt-BR"&gt;&lt;span style="font-style: normal;"&gt;&lt;span style="font-weight: normal;"&gt;-1&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/sup&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style="color: #000000;"&gt;&lt;span style="text-decoration: none;"&gt;&lt;span style="font-family: Times New Roman,serif;"&gt;&lt;span lang="pt-BR"&gt;&lt;span style="font-style: normal;"&gt;&lt;span style="font-weight: normal;"&gt;), aos 15, 30 e 45 DAE). Após a colheita da soja, as sementes foram amostradas, para avaliar a sua germinação, vigor e produtividade. Os teores de manganês foram influenciados pela aplicação sequencial de glyphosate, associada ao endossulfan + tebuconazole. A germinação do teste de frio foi reduzida em sementes de plantas tratadas com fomesafen + fluazifop-p-butil, em associação com endossulfan + tebuconazole. Dentre os tratamentos sem endossulfan + tebuconazole, a aplicação sequencial de glyphosate promoveu a maior massa de cem sementes, bem como, associada à mistura endossulfan + tebuconazole, diminuiu os teores foliares de Cu e Mn, porém, melhorou a germinação e não influenciou no vigor das sementes, em relação à soja capinada.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt

Automated classification of tribological faults of alternative systems with the use of unsupervised artificial neural networks

Preventing, anticipating, avoiding failures in electromechanical systems are demands that have challenged researchers and engineering professionals for decades. Electromechanical systems present tribological processes that result in fatigue of materials and consequent loss of efficiency or even usefulness of machines and equipment. Several techniques are used in an attempt to minimize the inherent losses of these systems through the analysis of signals from the equipment studied and the consequences of these wastes at unexpected moments, such as an aircraft in flight or a drilling rig in an oil well. Among them we can mention vibration analysis, acoustic pressure measurement, temperature monitoring, particle analysis of lubricating oil etc. However, electromechanical systems are complex and may exhibit unexpected behavior. Reliability-centric maintenance requires ever faster, more efficient and robust technological resources to ensure its efficiency and effectiveness. Artificial neural networks (ANN) are computational tools that find applicability in several segments of the research and signal analysis, where it is necessary to handle large amounts of data, associating statistics and computation in the optimization of dynamic processes and a high degree of reliability. They are artificial intelligence systems that have the ability to learn, are robust to failures, and can deliver real-time results. This work aims at the use of artificial neural networks to treat signals from the monitoring of tribological parameters using a test bench to simulate contact failures in an air compressor in order to create an automated fault detection and classification system, unsupervised, with the use of self-organized maps, or SOM, applied to the preventive and predictive maintenance of electromechanical processes.This research was supported by UFRN – Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil, which is highly appreciated by the authors

MOONS: The New Multi-Object Spectrograph for the VLT

International audienceMOONS is the new Multi-Object Optical and Near-infrared Spectrograph currently under construction for the Very Large Telescope (VLT) at ESO. This remarkable instrument combines, for the first time, the collecting power of an 8-m telescope, 1000 fibres with individual robotic positioners, and both low- and high-resolution simultaneous spectral coverage across the 0.64–1.8 μm wavelength range. This facility will provide the astronomical community with a powerful, world-leading instrument able to serve a wide range of Galactic, extragalactic and cosmological studies. Construction is now proceeding full steam ahead and this overview article presents some of the science goals and the technical description of the MOONS instrument. More detailed information on the MOONS surveys is provided in the other dedicated articles in this Messenger issue