O gênero Hypnea (Cystocloniaceae, Rhodophyta) no litoral do estado da Bahia, Brasil

A detailed morpho-anatomical study of the genus Hypnea from the state of Bahia is presented. Eight species have been recognized: H. cenomyce, H. cervicornis, H. cornuta, H. musciformis, H. nigrescens, H. platyclada, H. spinella and H. valentiae. An identification key, as well as descriptions, illustrations, comparisons with related taxa and maps of distribution in Bahia for each species, are presented.É apresentado um estudo morfoanatômico detalhado do gênero Hypnea no estado da Bahia. Foram reconhecidas oito espécies: H. cenomyce, H. cervicornis, H. cornuta, H. musciformis, H. nigrescens, H. platyclada, H. spinella e H. valentiae. É apresentada uma chave de identificação, além de descrições, ilustrações, comparações com táxons relacionados e mapas de distribuição na Bahia para cada espécie

Evaluation of the stocks of Hypnea musciformis (rhodophyta: gigartinales) on two beaches in Bahia, Brazil

Hypnea musciformis occurs widely in the northeast coast of Brazil, and it is one of the most important natural sources of k-carrageenan, which is used in food and cosmetic industries. Despite its potential for exploitation little is known about its ecology. The aim of this study was to investigate the biology and ecology of H. musciformis in Brazil through analyses of biomass stock and accompanying flora. Two populations of H. musciformis were analyzed: those on the beaches of Stella Maris and Itacimirim (Bahia, Brazil). Eight samplings were conducted between 2007 and 2009 during the dry and rainy seasons. The algae were sampled along transects (20 m) using quadrats (0.04 m²) in three different hydrodynamic regions of the reefs (TP, PRR and FRR). The t-Test, Dunn's Test and parametric and nonparametric ANOVA were used for statistical analyses. 17 host species and 41 associated species were identified. The highest biomass stock was observed during the dry season. On Stella Maris, the region with the highest biomass stock was FRR; on Itacimirim, TP had the highest biomass value. This study permits the assumption that seasonality, microhabitat, hydrodynamic and micro scale factors contribute to variation in biomass stock in H. musciformis populations.Hypnea musciformis ocorre amplamente na costa nordeste do Brasil, sendo uma das mais importantes fontes naturais de k-carrageenana que é utilizado nas indústrias alimentícia e cosmética. Apesar do potencial de explotação, pouco se sabe sobre sua ecologia. O objetivo deste estudo foi conhecer a biologia e ecologia de H. musciformis no Brasil através da análise do estoque de biomassa e da flora acompanhante. Duas populações de H. musciformis foram analisadas nas praias de Stella Maris e Itacimirim (Bahia, Brasil). Oito amostragens foram realizadas entre 2007 e 2009, durante as estações seca e chuvosa. As algas foram coletadas ao longo de transectos (20 m) utilizando quadrados (0,04 m²) em três diferentes regiões hidrodinâmicas dos recifes (TP, PRR e FRR). t-Test, Teste de Dunn e ANOVA paramétrica e não-paramétrica foram utilizados para a análise estatística. Foram identificadas 17 espécies hospedeiras e 41 espécies associadas. O maior estoque de biomassa foi observado durante a estação seca. Em Stella Maris, a região com maior estoque de biomassa foi FRR, e em Itacimirim, TP apresentou biomassa maior. Este estudo permite supor que sazonalidade, microhabitats, hidrodinamismo e fatores em microescala contribuem para a variação no estoque de biomassa em populações de H. musciformis

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

Acta Botanica Brasilica

p. 973-978Durante estudo sobre as espécies de Hypnea do litoral do estado da Bahia foram encontrados exemplares de Hypnea cornuta (Kützing) J. Agardh em coletas realizadas no extremo sul do estado. Este é o primeiro relato de H. cornuta para o estado da Bahia, Brasil. É fornecida uma caracterização detalhada dos aspectos morfológicos, anatômicos e reprodutivos desta espécie

Vulnerable areas determination based on seismic response at Chile center region

Chile is one of most seismic countries in the world. In the past 100 years, more than 8 major seismic events occurred in Chile, with the coastal area of central Chile being one of the most affected Since 2007, nearly 2,306 earthquakes were recorded in this area with magnitudes varying between 1.9 and 6.9 in the Ritcher and Mercalli scales. The earthquakes’ impact on the engineering structures depends on several factors, such as: the depth of the wave, wave type, geomorphological aspects of the rock, and soil properties. Chile central region of is a very important economics area, but at the same time, very vulnerable. The cities existing on this area, are installed in coastal plains, limited by Coastal Mountains and Pacific Ocean. Most of the seismic activity episodes recorded by Nacional Seismological center (CSN) is on the ocean. The present research aims to identify the regions vulnerable to significant seismic activities based on the average shear wave velocity on the upper 30m (Vs30) and using Geographic Information Systems. The Vs30 values were measured by the United Stated Geological Service (USGS). This analysis is based on the topography, geological units classified by Chilean norms NCh433, NCh2369, and law decree 61/ 2011. The analysis indicated that last epicenter city, Cobquecura, is the low vulnerable area, considering high Vs30 values and low number of habitants. Cities as Concepción, Talcachuano, San Pedro de la Paz, Coronel y Hualpén are more vulnerable because low Vs30 values, and high population. This factor can be associated to river mouths and floodplains presents in this area