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

Use of wild vertebrates for consumption and bushmeat trade in Brazil: a review

Abstract Background Bushmeat is a resource exploited by thousands of people around the world, especially in tropical and neotropical regions, constituting an important source of protein and income. But what is known, so far, about the consumption and trade of wild vertebrate meat (hereinafter “bushmeat”) in a megadiverse country like Brazil? This question was answered through a systematic survey of publications on the consumption and trade of wild vertebrate meat made in Brazil between 2011 and 2021. Methods We selected 63 scientific articles available on “Google Scholar,” “Science Direct,” “Scopus,” “ Web of Science” and “Portal de Periódico da CAPES.” The articles were categorized as: exclusive to (1) consumption or (2) bushmeat trade, totals of 54 and three articles, respectively; both (3) consumption and trade bushmeat, totaling six articles. We applied a nonparametric Spearman's correlation analysis to verify the association between the number of papers and the species richness of wild vertebrates cited for consumption by Brazilian state. Results The results revealed that the publications were concentrated in the Northeast (36), North (26) and Southeast (1) regions, distributed across 16 states of the federation. These data reinforce the need for more researches in states and other regions of the country. Our research hypothesis was confirmed, since the richness of species cited for meat consumption was positively associated with the amount of work carried out by the states of the federation. We identified a total of 321 species of wild vertebrates mentioned in the categories involving the consumption of bushmeat. We had a greater bird species richness mentioned for consumption (170) to the detriment of mammals (107), reptiles (40) and amphibians (4). Furthermore, in the articles involving the bushmeat trade categories we had 57 species of vertebrates mentioned, with mammals being the most representative in terms of species richness (29), to the detriment of birds (20) and reptiles (8). These data reinforce that birds and mammals have been the groups most used both for consumption and trade in bushmeat in the country's regions, and it is necessary to mitigate the hunting exploitation of these groups. We recorded that socioeconomic, biological, environmental and sociocultural factors were the most cited predictors of the consumption and trade of bushmeat in the articles. We identified that the bushmeat trade chain is dynamic and ramified, made up of several actors, including specialized and diversified hunters, intermediaries, market sellers, market vendors, restaurant owners and final customers. Public markets and open-air fairs were the most cited places for buying and selling wild meat in commerce. Conclusions In general, our results indicate that we have made significant advances in publications on the consumption and trade of bushmeat in Brazil over the last few years. However, we highlight the need to better understand the patterns of consumption and trade of bushmeat in different regions of the country, as well as the factors associated with the dynamics of the trade chain and uses of wildlife by local communities. We emphasized that a multidimensional understanding of hunting activities is important to face socio-ecological problems and improve the conservation of target species which have continually been explored for uses by populations in different regions of the world

IAPT chromosome data 31.

POACEAE Paspalum almum Chase, 2n = 12; Argentina, Corrientes, H & D 1703 (MNES), H & D 1704 (MNES).Paspalum conspersum Schrad., 2n = 60; Argentina, Misiones, H & D 1119 (MNES), H & D 1143 (MNES).Paspalum equitans Mez, 2n = 20; Argentina, Misiones, H & D 1447 (MNES).Paspalum fasciculatum Wild. ex Flüggé, 2n = 20; Argentina, Formosa, R 307 (BAA).Paspalum glaucescens Hack., 2n = 40; Argentina, Misiones, H & D 109 (MNES).Paspalum ionanthum Chase, n = 20; Paraguay, Cordillera, H & D 1177 (MNES).Paspalum maculosum Trin., 2n = 20, 40; Argentina, Misiones, H & D 1445 (MNES).Paspalum malacophyllum Trin., 2n = 40; Argentina, Salta, H & D 1448 (MNES).Paspalum notatum var. saurae Parodi, 2n = 20; Argentina, Santa Fe, H & D 1453 (MNES).Paspalum notatum Flüggé var. notatum, 2n = 40; Argentina, Misiones, H 220 (CTES, MNES); Argentina, Santa Fe, H & D 1304 (MNES); Argentina, Misiones, H & D 1603 (MNES).Paspalum pauciciliatum (Parodi) Herter, 2n = 40; Argentina, Misiones, H & D 465 (CTES, MNES).Paspalum paucifolium Swallen, 2n = 40; Paraguay, Paraguarí, H & D 1294 (MNES).Paspalum quarinii Morrone & Zuloaga, 2n = 20; Argentina, Misiones, H & D 1190 (CTES, MNES, SI).Paspalum regnellii Mez, 2n = 40; Argentina, Misiones, H & D 1118 (MNES).Fil: Marhold, Karol. Academia de Ciencias; EslovaquiaFil: Kurĕera, Jaromír. Academia de Ciencias; EslovaquiaFil: Aguiar-Melo, Camila. Universidade Federal do Rio Grande do Sul; BrasilFil: Almeida, Erton Mendonça de. Universidade Estadual da Paraiba; BrasilFil: Alves, Lânia Isis Ferreira. Universidade Estadual da Paraiba; BrasilFil: An'kova, Tatyana V.. Jardin botánico de Siberia Central; RusiaFil: Bered, Fernanda. Universidade Federal do Rio Grande do Sul; BrasilFil: Bonifácio, Kallyne. Universidade Estadual da Paraiba; BrasilFil: Carvalho, Luana. Universidade Federal do Rio Grande do Sul; BrasilFil: Chiarini, Franco Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Cordeiro, Joel M. P.. Universidade Estadual da Paraiba; BrasilFil: Costea, Mihai. Wilfrid Laurier School Of Business; CanadáFil: Daviña, Julio Rubén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas; ArgentinaFil: Ebel, Aleksandr L.. Tomsk State University; RusiaFil: Souto, Allan Falconi. Universidad Federal do Abc; BrasilFil: Felix, Cattleya M. P.. Universidade Estadual da Paraiba; BrasilFil: Felix, Leonardo P.. Universidade Estadual da Paraiba; BrasilFil: Fernandez, Aveliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Botánica del Nordeste. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias. Instituto de Botánica del Nordeste; ArgentinaFil: García, Miguel Ángel. University of Toronto; Canadá. Royal Botanic Gardens; Reino UnidoFil: García Ruiz, Ignacio. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. Departamento de Física; MéxicoFil: Gil, André dos Santos Bragança. Museu Paraense Emilio Goeldi; BrasilFil: Guerra, Marcelo. Universidade Federal de Pernambuco; BrasilFil: Hirsch, Luiza Domingues. Universidade Federal do Rio Grande do Sul; BrasilFil: Honfi, Ana Isabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas; Argentina. Universidad Nacional de Misiones; ArgentinaFil: Kaltchuk Santos, Eliane. Universidade Federal do Rio Grande do Sul; BrasilFil: Knapp, Sandra. Natural History Museum; Reino UnidoFil: Kumar, Rohit. Punjabi University; IndiaFil: Kumari, Vandna. Punjabi University; IndiaFil: Lovo, Juliana. Instituto Tecnológico Vale. Departamento de Bioinformatica y Genomica Ambiental.; BrasilFil: Lucena, Reinaldo F. P.. Universidade Estadual da Paraiba; BrasilFil: Medeiros Neto, Enoque. Universidade Estadual da Paraiba; BrasilFil: Moraes, Ana Paula. Universidad Federal do Abc; BrasilFil: Nascimento, Rodrigo Garcia Silva. Universidade Estadual da Paraiba; BrasilFil: Neves, José Achilles Lima. Universidade Estadual da Paraiba; BrasilFil: Nollet, Felipe. Universidad Federal Rural Pernambuco; BrasilFil: Oliveira, Regina Célia de. Universidade do Brasília; BrasilFil: Orejuela, Andrés. Royal Botanic Gardens; Reino UnidoFil: Pozzobon, Marisa Toniolo. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Reutemann, Anna Verena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Botánica del Nordeste. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias. Instituto de Botánica del Nordeste; ArgentinaFil: Oliveira Ribeiro, André Rodolfo de. Universidade do Brasília; Brasil. Universidade Estadual do Ceará; BrasilFil: Rua, Gabriel Hugo. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Santos, Angeline M. S.. Universidade Estadual da Paraiba; BrasilFil: Silva, Anádria Stéphanie da. Universidade do Brasília; BrasilFil: Silva, Rosemere. Universidade Estadual da Paraiba; BrasilFil: Silva, Ronimeire Torres da. Universidade Estadual da Paraiba; BrasilFil: Singhal, Vijay Kumar. Punjabi University; IndiaFil: Souza Chies, Tatiana T.. Universidade Federal do Rio Grande do Sul; BrasilFil: Stefanović, Saša. University of Toronto; CanadáFil: Valls, José Francisco Montenegro. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; Brasil. Universidade do Brasília; BrasilFil: Welker, Cassiano A. D.. Universidade Federal de Uberlandia; BrasilFil: Zykova, Elena. Jardin botánico de Siberia Central; Rusi

ATLANTIC ANTS: a data set of ants in Atlantic Forests of South America

International audienc

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