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

Avaliação epidemiológica das vítimas de trauma abdominal submetidas ao tratamento cirúrgico

OBJETIVO: avaliar o perfil epidemiológico e o desfecho das vítimas de trauma abdominal submetidas à laparotomia em hospital de urgência. MÉTODOS: estudo observacional, descritivo, longitudinal, com abordagem prospectiva, mediante entrevista de 100 pacientes com trauma abdominal submetidos ao tratamento cirúrgico e à avaliação dos seus prontuários. Período da coleta dos dados: setembro a novembro de 2011. RESULTADOS: Os pacientes mais acometidos pelo trauma abdominal foram do sexo masculino, de cor parda, na faixa etária de 25-49 anos, com baixa escolaridade, solteiros, católicos, com rendimento de um a dois salários mínimos. Houve uma predominância do trauma no ambiente urbano, no período noturno e no final de semana. O motivo mais frequente do trauma foi a tentativa de homicídio, associado ao uso de álcool e drogas ilícitas e o mecanismo a arma branca. A dor mostrou-se o sinal de alerta mais presente. A região mais afetada foi abdome superior e o fígado o órgão mais acometido. O tempo de internação hospitalar durou em torno de quatro a dez dias. A maioria teve alta sem sequela. Ocorreram dois óbitos. CONCLUSÃO: Foi marcante a associação do trauma abdominal com homens sob efeito de álcool e/ou drogas ilícitas, refletindo o contexto da violência interpessoal na sociedade atual. A despeito da magnitude do trauma, o desfecho foi satisfatório, apesar da ocorrência de óbitos, o que denota a importância dos hospitais de urgência de manter no seu corpo clínico uma equipe cirúrgica treinada

Núcleos de Ensino da Unesp: artigos 2008

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq