Nonatobase: A Database For Polychaeta (annelida) From The Southwestern Atlantic Ocean.

Networks can greatly advance data sharing attitudes by providing organized and useful data sets on marine biodiversity in a friendly and shared scientific environment. NONATObase, the interactive database on polychaetes presented herein, will provide new macroecological and taxonomic insights of the Southwestern Atlantic region. The database was developed by the NONATO network, a team of South American researchers, who integrated available information on polychaetes from between 5°N and 80°S in the Atlantic Ocean and near the Antarctic. The guiding principle of the database is to keep free and open access to data based on partnerships. Its architecture consists of a relational database integrated in the MySQL and PHP framework. Its web application allows access to the data from three different directions: species (qualitative data), abundance (quantitative data) and data set (reference data). The database has built-in functionality, such as the filter of data on user-defined taxonomic levels, characteristics of site, sample, sampler, and mesh size used. Considering that there are still many taxonomic issues related to poorly known regional fauna, a scientific committee was created to work out consistent solutions to current misidentifications and equivocal taxonomy status of some species. Expertise from this committee will be incorporated by NONATObase continually. The use of quantitative data was possible by standardization of a sample unit. All data, maps of distribution and references from a data set or a specified query can be visualized and exported to a commonly used data format in statistical analysis or reference manager software. The NONATO network has initialized with NONATObase, a valuable resource for marine ecologists and taxonomists. The database is expected to grow in functionality as it comes in useful, particularly regarding the challenges of dealing with molecular genetic data and tools to assess the effects of global environment change. Database URL: http://nonatobase.ufsc.br/.2014bau00

DIS-EMBODIED LANGUAGE: L'ACQUISIZIONE LINGUISTICA IN ASSENZA DI RIFERIMENTI ORO-ARTICOLATORI. L'ELOQUIO DEL BAMBINO DISPRASSICO ITALIANO.

La tesi fornisce una prima classificazione sistematica dei fenomeni fonetico-fonologici che caratterizzano l'eloquio del bambino, di lingua italiana, affetto da disprassia verbale in età evolutiva. Presenta, inoltre, un'ipotesi di lettura della patologia dal punto da un punto di vista "embodied" ed ipotesi di ricerca attinenti

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

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