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

Rapid assessment of chemical compounds from Phyllogorgia dilatata using Raman spectroscopy

Abstract The gorgonian Phyllogorgia dilatata is endemic to the Brazilian coast which is listed as threatened with extinction. This species is known to produce sterols, mono- to tetra-terpenes, conjugated polyenals and peptides. The main objective of this study is to present an alternative method for identification of different classes of compounds based upon a Raman mapping technique using FT-Raman spectroscopy. The Raman analysis performed directly on the tissues (in situ) revealed the occurrence of peridinin, diadinoxanthin, conjugated polyenal and linoleic acid, that were also confirmed by Raman analysis of partitioned crude extracts. We have demonstrated that the technique has potential for use in guiding chromatographic separations and in providing information with respect to the early stages of a tissue necrosis through “purpling”. It may become a valuable non-destructive technique for monitoring the accumulation or production of metabolites during a biological interaction

Raman Spectroscopic Study of Antioxidant Pigments from Cup Corals Tubastraea spp.

Chemical investigation of nonindigenous Tubastraea coccinea and T. tagusensis by Raman spectroscopy resulted in the identification of carotenoids and indolic alkaloids. Comparison of Raman data obtained for the in situ and crude extracts has shown the potential of the technique for characterizing samples which are metabolic fingerprints, by means of band analysis. Raman bands at ca. 1520, 1160, and 1005 cm^–1 assigned to ν₁(CC), ν₂(CC), and ρ₃(CCH₃) modes were attributed to astaxanthin, and the band at 1665 cm^–1 could be assigned to the ν­(CN), ν­(CO), and ν­(CC) coupled mode of the iminoimidazolinone from aplysinopsin. The antioxidant activity of the crude extracts has also been demonstrated, suggesting a possible role of these classes of compounds in the studied corals

Processos de recuperação em áreas afetadas por mineração: uma revisão cienciométrica

A major challenge in using recovery techniques, for the different natural ecosystems affected by mining, is a mutual relationship between the habitat and its biota response. This study aimed to do a review to identify the number of publications, which countries are publishing more and which recovery techniques and taxonomic group are used in mining areas globally have contributed to the maintenance or recovery of the environment. We reviewed the literature on recovery in mining areas worldwide, between 1994 and 2016, using the Web of Science online database. We identified 9,000 publications, after the selection procedures, we analyzed the 467 remaining manuscripts. Of these, 34.26% were published between 1994 and 2004, and 65.74% between 2006 and 2016. The countries that contributed the most were the USA with 16.45%, Australia with 13.56% and China with 8.66%. Brazil contributed 6.9% of the publications. The recovery techniques using vegetation were the most reported in the literature and most used. The taxonomic group of terrestrial plants was the most cited and most used in the recovery of degraded areas. We found various techniques for recovering degraded areas can be established, but most of them did not show proper monitoring and without this the recovery processes may not achieve their objectives and studies that test the effect size of these recovery methods are still necessary.Um grande desafio no uso de técnicas de recuperação, para os diferentes ecossistemas naturais afetados pela mineração, é uma relação conjunta entre a resposta do habitat e a biota. O objetivo deste estudo foi realizar uma revisão para identificar o número de publicações; quais países estão publicando mais; quais técnicas de recuperação; e grupo taxonômico são mais usados nas áreas de mineração e que contribuíram para a manutenção ou recuperação do meio ambiente. Revisamos a literatura sobre recuperação em áreas de mineração em todo o mundo, entre 1994 e 2016, usando o banco de dados on-line da Web of Science. Foram identificadas 9.000 publicações; após os procedimentos de seleção, foram analisados 467 manuscritos restantes. Desses, 34,26% foram publicados entre 1994 e 2004 e 65,74% entre 2006 e 2016. Os países que mais contribuíram foram os EUA com 16,45%, a Austrália com 13,56% e a China com 8,66%. O Brasil contribuiu com 6,9% das publicações. As técnicas de recuperação com vegetação foram as mais relatadas na literatura e as mais utilizadas. O grupo taxonômico de plantas terrestres foi o mais citado e mais utilizado na recuperação de áreas degradadas. Descobrimos que várias técnicas para recuperar áreas degradadas podem ser estabelecidas, mas a maioria não mostrou monitoramento adequado e, sem isso, os processos de recuperação podem não atingir seus objetivos e estudos que testam os tamanhos de efeitos dessas técnicas ainda são necessários