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

Chagas disease in urban and peri urban environment in the Amazon: Sentinel hosts, vectors, and the environment

This work was supported by The Pro-Amazônia Biodiversity and Sustainability project was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES), notice 047/2012, AUXPE 3286/2013 - Process 23,038.009430 / 2013–98.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Laboratório de Geoprocessamento. Ananindeua, PA, Brasil.Universidade Federal do Pará. Instituto de Ciências Biológicas. Laboratório de Ecologia e Zoologia de Vertebrados. Belém PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Castanhal, PA, Brazil.Chagas disease is an anthropozoonosis, caused by a flagellated protozoan, Trypanosoma cruzi, in which the enzootic cycle occurs between mammals and triatomines. Two dogs with a history of sudden death were necropsied at the Federal University of Pará (UFPA). One dog had a pale area in the myocardium, which on histopathological examination showed a T. cruzi amastigote nest; immunohistochemistry (IHC) analysis characterized it as acute Chagas disease (ACD). The second dog showed no macroscopic changes. Microscopically, a few cardiomyocytes were replaced by adipocytes, and IHC result was negative for T. cruzi. However, results of polymerase chain reaction (PCR) of the cardiac tissue of both dogs was positive for T. cruzi DNA. After that, an epidemiological study was conducted in the region. For this study, we selected four areas in Castanhal. One of the four areas (Area 1) is where one of the dogs lived. The other three areas were chosen because they were recently deforested for housing. Blood samples were collected from dogs, cats, wild small mammals (marsupials and rodents), and the digestive tract of triatomines. Nested PCR was performed on all the blood samples and the triatomine digestive tracts. In Area 1, T. cruzi DNA was detected in 50% (12/24) of the tested dogs, in the only tested cat (1/1), 50% (1/2) of the tested marsupials (Didelphis marsupials), and 100% of the captured triatomines (Rhodnius pictipes) (2/2). In Area 2, T. cruzi DNA was not detected in any of the 11 (0/11) dogs and two marsupials tested (0/2), and no triatomines were found in this area. In Area 3, T. cruzi DNA was detected in 42.25% (30/71) of the dogs, in 66,6% (2/3) of the cats, the only captured marsupial (D. marsupialis) (1/1), and all three triatomines (3/3) (R. pictipes) tested. In Area 4, the two dogs tested were negative (0/2), 25% (1/4) of the captured marsupials (D. marsupialis) was positive, and no triatomine was captured in this area. The data demonstrate the importance of detecting T. cruzi in dogs, cats, small rodents, and marsupials in the Amazon metropolitan areas, where ecotopes carry reservoirs and vectors capable of participating in the Chagas disease cycle. The proximity between humans and T. cruzi vectors in these places might contribute to increased disease transmission risk and maintenance of agents. It was noted that high-standard condominiums, previously thought to reduce the risk for this disease, presented a new epidemiological risk. The presence of T. cruzi DNA in a dog who, a year earlier had tested negative, when another dog in the same house died of ACD, shows that the transmission cycle is present and active, with a high possibility of disease transmission to animals and humans

NEOTROPICAL CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data