Leaf anatomy of Crambe abyssinica Hochst. during in vitro shoot induction

This study aimed to characterize and evaluate possible modifications in the leaf anatomy of crambe during the process of shoot induction based on micropropagation protocol. The anatomic characteristics of the leaves, and also the morphological characteristics of crambe plantlets, were evaluated during the shoot induction phase of the micropropagation. The shoots were induced by the cytokinins, 6-benzylaminopurine (BAP), kinetin (KIN) and thidiazuron (TDZ), at distinct concentrations on Murashige and Skoog (MS) medium during 120 days of culture. Comparing the leaf anatomy, it was observed that, at day 30, only the adaxial epidermis and the palisade parenchyma presented significant differences in relation to the tested concentrations, independently of the type of cytokinin employed. At 120 days, the anatomic analysis of the mesophyll demonstrated no marked difference among the cytokinins at 5 μM. Therefore, the various sources and concentrations of cytokinins applied in this work did not promote marked changes in the sense of altering the organization and/or thickness compared to the control.Key words: Oilseeds, micropropagation, mesophyll, histology, plant morphology

Genetic diversity of bromeliaceae species from the atlantic forest

The Bromeliaceae family includes a range of species used for many purposes, including ornamental use and use as food, medicine, feed, and fiber. The state of Espírito Santo, Brazil is a center of diversity for this family in the Atlantic Forest. We evaluated the genetic diversity of five populations of the Bromeliaceae family, including specimens of the genera Aechmea, Billbergia (subfamily Bromelioideae), and Pitcairnia (subfamily Pitcairnioidea), all found in the Atlantic Forest and distributed in the state of Espírito Santo. The number of alleles per locus in populations ranged from two to six and the fixation index (F), estimated for some simple sequence repeats in bromeliad populations, was less than zero in all populations. All markers in the Pitcairnia flammea population were in Hardy-Weinberg equilibrium (P < 0.05). Moreover, significant deviations from Hardy-Weinberg equilibrium were observed at some loci in populations of the five bromeliad species. In most cases, this can be attributed to the presence of inbreeding or the Wahlund effect. The genetic diversity indices of five species showed greater allelic richness in P. flammea (3.55). Therefore, we provide useful information for the characterization of genetic diversity in natural populations of Aechmea ramosa, Aechmea nudicaulis, Billbergia horrid, Billbergia euphemia, and P. flammea in Atlantic Forest remnants in the south of Espírito Santo state. © 2017 The Authors

Quantification of the diversity among common bean accessions using Ward-MLM strategy

O objetivo deste trabalho foi avaliar a divergência de acessos de feijoeiro-comum por suas características agronômicas, morfológicas e moleculares, com base no procedimento Ward-MLM. Uma coleção de 57 acessos do banco de germoplasma da Universidade Federal do Espírito Santo foi utilizada neste estudo, dos quais: 31 acessos locais, pertencentes à comunidade Fortaleza, no Município de Muqui, ES; 20 acessos fornecidos pela Embrapa Trigo; e 6 cultivares comerciais. Foram avaliados cinco caracteres agronômicos (ciclo da planta, número de sementes por vagem, número de vagens por planta, peso de 100 grãos e produtividade de grãos), cinco caracteres morfológicos (hábito de crescimento, porte da planta, formato da semente, cor da semente e grupo comercial) e 16 iniciadores microssatélites. Detectou-se ampla variabilidade genética pelos dados morfológicos, agronômicos e moleculares nos 57 acessos de feijão. O procedimento Ward-MLM mostrou que cinco foi o número ideal de grupos, de acordo com os critérios do pseudo F e pseudo t2 . Os acessos de origem andina tiveram sementes mais pesadas do que os outros e ficaram em um mesmo grupo. O procedimento Ward-MLM é uma técnica útil para detectar divergência genética e agrupar genótipos pelo uso simultâneo de descritores morfológicos, agronômicos e moleculares.The present work aimed at evaluating the divergence among common bean accessions by their agronomic, morphological and molecular traits, based on the Ward-MLM procedure. A collection of 57 accessions from the gene bank of Universidade Federal do Espírito Santo was used in this study, from which: 31 were landraces belonging to the community Fortaleza, in the municipality of Muqui, ES, Brazil; 20 accessions were provided by Embrapa Trigo; and 6 were commercial cultivars. Five agronomic traits (plant cycle, number of seeds per pod, number of pods per plant, weight of 100 seeds, and grain yield), five morphological traits (growth habit, plant size, seed shape, seed color, and commercial group) and 16 microsatellite primers were evaluated. High genetic variability was detected considering morphological, agronomic and molecular traits in the 57 common bean accessions studied. The Ward-MLM procedure showed that the ideal number of groups was five, according to the pseudo F and pseudo t2 criteria. The accessions from Andean origin had heavier seeds than others and formed a cluster. The Ward-MLM statistical procedure is a useful technique to detect genetic divergence and to cluster genotypes by simultaneously using morphological, agronomic and molecular data

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