Modelos de distribuição de Zygodontomys brevicauda (Allen & Chapman, 1893) (Mammalia: Muridae) nas savanas de Roraima, norte do Brasil

The present study describes the distribution of Zygodontomys brevicauda (Rodentia, Sigmodontinae) relating the presence/absence of the species to a digital database on the vegetation of savannas of the northeastern State of Roraima , Brazil. The study area is situated in the Surumu River region, between 03o58’- 04o27’N and 60o13’-61o16’W, and is composed mainly of savanna formations. In a total effort of 9479 trap days, the trap success for Z. brevicauda was 0.57%. The probability of capture of the species was calculated for each trap station through logistic regression, using structural characteristics of each habitat. The association of capture probabilities with different habitat classes using a LANDSAT-TM satellite image allowed a spatial view of the potential distribution of the species considering the habitat mosaic of the region. The species is at least partially dependent on the savanna-forest boundary. The models show a high frequency of apparently unsuitable areas, especially of open and closed savannas, which might suggest that habitat occupancy is far from saturated. Zygodontomys brevicauda appears to be a colonizing species, and was shown to be associated particularly with the edges of the gallery forests. This habitat type may act as source habitats for open savannas.O presente estudo avalia a distribuição potencial de Zygodontomys brevicauda (Rodentia, Sigmodontinae) relacionando a presença/ausência da espécie através de uma base digital de dados sobre a vegetação das savanas do nordeste do Estado de Roraima, Brasil. A área de estudo situa-se na região do Alto e Médio Rio Surumu (3o58’-4o27’N; 60o13’-61o16’W) e é composta por várias formações, sendo mais extensas as de savana. Foram empregadas 9.479 armadilhas-dia e o sucesso de captura de Z. brevicauda foi de 0.57%. As probabilidades de captura da espécie foram calculadas para cada estação de captura através de regressões logísticas utilizando variáveis estruturais dos hábitats. As associações das probabilidades de captura com as diferentes classes de hábitats, reconhecidas via imagem de satélite LANDSAT-TM, permitiram avaliar a distribuição potencial da espécie no mosaico de hábitats da região. A espécie está parcialmente associada às áreas de contato savana-floresta. O modelo evidenciou alta freqüência de áreas potencialmente vagas, especialmente nas savanas arbóreas abertas e graminosas, sugerindo forte insaturação dos hábitats. Zygodontomys brevicauda é potencialmente uma espécie colonizadora dessas classes de hábitats, com as áreas de borda das matas de galeria atuando como hábitats-fonte para as savanas abertas

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

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 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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Lowland tapir distribution and habitat loss in South America

The development of species distribution models (SDMs) can help conservation efforts by generating potential distributions and identifying areas of high environmental suitability for protection. Our study presents a distribution and habitat map for lowland tapir in South America. We also describe the potential habitat suitability of various geographical regions and habitat loss, inside and outside of protected areas network. Two different SDM approaches, MAXENT and ENFA, produced relative different Habitat Suitability Maps for the lowland tapir. While MAXENT was efficient at identifying areas as suitable or unsuitable, it was less efficient (when compared to the results by ENFA) at identifying the gradient of habitat suitability. MAXENT is a more multifaceted technique that establishes more complex relationships between dependent and independent variables. Our results demonstrate that for at least one species, the lowland tapir, the use of a simple consensual approach (average of ENFA and MAXENT models outputs) better reflected its current distribution patterns. The Brazilian ecoregions have the highest habitat loss for the tapir. Cerrado and Atlantic Forest account for nearly half (48.19%) of the total area lost. The Amazon region contains the largest area under protection, and the most extensive remaining habitat for the tapir, but also showed high levels of habitat loss outside protected areas, which increases the importance of support for proper management

Coxiella and Bartonella spp. in bats (Chiroptera) captured in the Brazilian Atlantic Forest biome

AUTHORS - Michelle Santos Ferreira1, Alexandro Guterres1, Tatiana Rozental1*, Roberto Leonan Morim Novaes2, Emmanuel Messias Vilar3, Renata Carvalho de Oliveira1, Jorlan Fernandes1, Danielle Forneas1, Adonai Alvino Junior1, Martha Lima Brandão4, José Luis Passos Cordeiro4, Martín Roberto Del Valle Alvarez5, Sergio Luiz Althoff6, Ricardo Moratelli4, Pedro Cordeiro-Estrela3, Rui Cerqueira da Silva7 and Elba Regina Sampaio de Lemos1 - AFFILIATIONS - 1Laboratório de Hantaviroses e Rickettsioses, Pavilhão Helio e Peggy Pereira, 1 Pavimento, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Avenida Brasil 4365, Manguinhos, Rio de Janeiro, RJ, Brazil. 2Universidade Federal do Rio de Janeiro, Av. Pedro Calmon, 550, Cidade Universitária, Rio de Janeiro, Rio de Janeiro, RJ, Brazil. 3Laboratório de Mamíferos, Departamento de Sistemática e Ecologia, Centro de Ciências Exatas e da Natureza, Universidade Federal da Paraíba, Campus I, Castelo Branco, João Pessoa, PB, Brazil. 4Fundação Oswaldo Cruz, Fiocruz Mata Atlântica, Estrada Rodrigues Caldas, 3400, Taquara, Rio de Janeiro, RJ, Brazil. 5Departamento de Ciências Biológicas, Universidade Estadual de Santa Cruz, Rodovia Ilhéus - Itabuna, Km. 16 Salobrinho, Ilheus, BA, Brazil. 6Departamento de Ciências Naturais, Laboratório de Biologia Animal, Fundação Universidade Regional de Blumenau, Ccen, Dcn. FURB - Fundação Universidade Regional de Blumenau Itoupava Seca, Blumenau, SC, Brazil. 7Laboratório de Vertebrados, Departamento de Ecologia, Instituto de Biologia, Universidade Federal do Rio de Janeiro, Av. Pedro Calmon, 550, Cidade Universitária, Rio de Janeiro, RJ, Brazil.Submitted by Sandra Infurna ([email protected]) on 2019-02-01T14:37:58Z No. of bitstreams: 1 jorlan_fernandes_etal_IOC_2018.pdf: 987842 bytes, checksum: ac7a643e97226c541e8143added4d88b (MD5)Approved for entry into archive by Sandra Infurna ([email protected]) on 2019-02-01T14:59:25Z (GMT) No. of bitstreams: 1 jorlan_fernandes_etal_IOC_2018.pdf: 987842 bytes, checksum: ac7a643e97226c541e8143added4d88b (MD5)Made available in DSpace on 2019-02-01T14:59:25Z (GMT). No. of bitstreams: 1 jorlan_fernandes_etal_IOC_2018.pdf: 987842 bytes, checksum: ac7a643e97226c541e8143added4d88b (MD5) Previous issue date: 2018Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Hantaviroses e Rickettsioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Hantaviroses e Rickettsioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Hantaviroses e Rickettsioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Hantaviroses e Rickettsioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Hantaviroses e Rickettsioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Hantaviroses e Rickettsioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Hantaviroses e Rickettsioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Fiocruz Mata Atlântica. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Fiocruz Mata Atlântica. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Fiocruz Mata Atlântica. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Hantaviroses e Rickettsioses. Rio de Janeiro, RJ, Brasil.Múltipl autoria - ver em NotasBackground: The role of bats as reservoirs of zoonotic agents, especially pathogenic bacteria such as Bartonella and Coxiella, has been discussed around the world. Recent studies have identified bats as potential hosts of species from the proteobacteria phylum. In Brazil, however, the role of bats in the natural cycle of these agents is poorly investigated and generally neglected. In order to analyze the participation of bats in the epidemiology of diseases caused by Bartonella, Coxiella, Rickettsia, Anaplasma and Ehrlichia, we conducted a descriptive epidemiological study in three biogeographic regions of the Brazilian Atlantic Forest. Results: Tissues of 119 bats captured in preserved areas in the states of Rio de Janeiro, Bahia and Santa Catarina from 2014 to 2015 were submitted to molecular analysis using specific primers. Bartonella spp. was detected in 22 spleen samples (18.5%, 95% CI: 11.9–26.6), whose phylogenetic analysis revealed the generation of at least two independent clusters, suggesting that these may be new unique genotypes of Bartonella species. In addition, four samples (3.4%, 95% CI: 0.9–8.3) were positive for the htpAB gene of C. burnetii [spleen (2), liver (1) and heart (1)]. Rickettsia spp., Anaplasma and Ehrlichia were not identified. This is the first study reporting C. burnetii and Bartonella spp. infections in bats from the Atlantic Forest biome. Conclusions: These findings shed light on potential host range for these bacteria, which are characterized as important zoonotic pathogens