DIETA DO BAGRE ESTUARINO Cathorops arenatus (VALENCIENNES, 1840) NA ILHA DE MAIANDEUA, PARÁ, BRASIL

Cathorops arenatus (Siluriformes: Ariidae) inhabits shallow waters and is abundant in the North coast of Brazil. Despite its local use and susceptibility to overexploitation and pollution, basic biological information is lacking to shape conservation actions. Thus, information on the feeding of C. arenatus in the North coast of Brazil are provided herein. Specimens were sampled in one expedition during the rainy season and in another in the dry season, using horizontal trawling along a 10-m line. Food contents of 68 specimens were analyzed. They exhibited 13 food items and fed mainly on Haparcticoida copepods and plant fragments. A high number of specimens exhibited sediment in their digestive tract. No differences in the diet composition between hydrological seasons were observed. C. arenatus exhibit a benthivorous feeding habit in the Amazonian estuary, which agrees with other species of the genus, and no temporal variation in its diet was found, which may be related to the community dynamics of its main prey.Keywords: Amazon estuary, Ariidae, trophic ecology.Cathorops arenatus (Siluriformes: Ariidae) habita águas rasas e é abundante na costa Norte do Brasil. Apesar da sua abundância, uso local e suscetibilidade à superexploração e poluição, informações biológicas básicas para nortear ações de conservação são escassas. Portanto, informações sobre a dieta de C. arenatus na região costeira Norte do Brasil são aqui apresentadas. Indivíduos foram amostrados em uma expedição na estiagem e em outra na estação chuvosa, utilizando arrastos horizontais na praia ao longo de 10m. O conteúdo alimentar de 68 espécimes foi analisado, que apresentou 13 itens alimentares e se alimentaram predominantemente de copépodos Haparcticoida e fragmentos de plantas superiores. Um alto número de espécimes apresentou sedimento em seus tratos digestivos. Não foram observadas diferenças na composição da dieta entre estações hidrológicas. C. arenatus exibiu hábitos alimentares bentívoros no estuário Amazônico, o que está de acordo com outras espécies do gênero, e variações temporais em sua dieta não foram encontradas, o que pode estar relacionado à dinâmica de comunidade de suas presas principais.Palavras-chave: Ariidae, ecologia trófica, estuário amazônico

Environmental predictors of the life history of the flag tetra Hyphessobrycon heterorhabdus (Characiformes: Characidae) in streams of the Eastern Amazon

Abstract This study aimed to characterize the reproductive biology of Hyphessobrycon heterorhabdus, and its response to environmental variations in the Eastern Amazon streams. We sampled specimens every two months, between March 2019 and January 2020. The population was evaluated for sex ratio, reproductive activity, growth pattern, condition factor, size at the first sexual maturation, spawning type, and fecundity. We analyzed 180 specimens, which showed a sex ratio of 1.6 males for each female across the whole period, with 2.3 males for each female during the period of greatest reproductive activity. The peak of reproductive activity coincided with higher precipitation periods and was partially predicted by factors such as water temperature, stream discharge, dissolved oxygen, substrate complexity, and electrical conductivity. The length where 50% and 100% of population to reach sexual maturity was 18.0 and 22.0 mm for males and 19.7 and 27.0 mm for females. The oocyte diameters showed a bimodal frequency, with at least two batches of oocytes. The average fecundity of 197 oocytes. The results indicate that this species presents an opportunistic strategy, and the tactics that make up this strategy depend on variations in both the physical structure of the habitat and physicochemical aspects of the water

Neotropical Freshwater Fishes: A dataset of occurrence and abundance of freshwater fishes in the Neotropics

The Neotropical region hosts 4225 freshwater fish species, ranking first among the world's most diverse regions for freshwater fishes. Our NEOTROPICAL FRESHWATER FISHES data set is the first to produce a large-scale Neotropical freshwater fish inventory, covering the entire Neotropical region from Mexico and the Caribbean in the north to the southern limits in Argentina, Paraguay, Chile, and Uruguay. We compiled 185,787 distribution records, with unique georeferenced coordinates, for the 4225 species, represented by occurrence and abundance data. The number of species for the most numerous orders are as follows: Characiformes (1289), Siluriformes (1384), Cichliformes (354), Cyprinodontiformes (245), and Gymnotiformes (135). The most recorded species was the characid Astyanax fasciatus (4696 records). We registered 116,802 distribution records for native species, compared to 1802 distribution records for nonnative species. The main aim of the NEOTROPICAL FRESHWATER FISHES data set was to make these occurrence and abundance data accessible for international researchers to develop ecological and macroecological studies, from local to regional scales, with focal fish species, families, or orders. We anticipate that the NEOTROPICAL FRESHWATER FISHES data set will be valuable for studies on a wide range of ecological processes, such as trophic cascades, fishery pressure, the effects of habitat loss and fragmentation, and the impacts of species invasion and climate change. There are no copyright restrictions on the data, and please cite this data paper when using the data in publications.Fil: Tonella, Lívia Helena. Universidade Estadual de Maringá. Departamento de Engenharia Química. Laboratorio de Pesquisa.; BrasilFil: Ruaro, Renata. Universidade Estadual de Maringá. Departamento de Engenharia Química. Laboratorio de Pesquisa.; BrasilFil: Daga, Vanessa Salete. Universidade Federal do Paraná; BrasilFil: Garcia, Diego Azevedo Zoccal. Universidade Estadual de Londrina; BrasilFil: Barroso Vitorino Júnior, Oscar. Instituto Natureza do Tocantins-Naturatins; BrasilFil: Lobato de Magalhães, Tatiana. Universidad Autonoma de Queretaro.; MéxicoFil: Reis, Roberto Esser. Museu de Ciências e Tecnologia; BrasilFil: Di Dario, Fabio. Universidade Federal do Rio de Janeiro; BrasilFil: Petry, Ana Cristina. Universidade Federal do Rio de Janeiro; BrasilFil: Mincarone, Michael Maia. Universidade Federal do Rio de Janeiro; BrasilFil: Assis Montag, Luciano Fogaça. Universidade Federal do Pará; BrasilFil: Pompeu, Paulo Santos. Universidade Federal de Lavras; BrasilFil: Teixeira, Adonias Aphoena Martins. Universidade Estadual da Paraiba; BrasilFil: Carmassi, Alberto Luciano. Universidade Federal de Sao Paulo; Brasil. Universidade Federal do São Carlos; BrasilFil: Sánchez, Alberto J.. Universidad Juárez Autónoma de Tabasco; MéxicoFil: Giraldo Pérez, Alejandro. Universidade Federal de Minas Gerais; BrasilFil: Bono, Alessandra. Universidad de Vale do Rio dos Sinos; BrasilFil: Datovo, Aléssio. Universidade de Sao Paulo; BrasilFil: Flecker, Alexander S.. Cornell University; Estados UnidosFil: Sanches, Alexandra. Universidade de Sao Paulo; Brasil. Universidade Federal do São Carlos; BrasilFil: Godinho, Alexandre Lima. Universidade Federal de Minas Gerais; BrasilFil: Matthiensen, Alexandre. Embrapa Suínos e Aves; BrasilFil: Peressin, Alexandre. Universidade Federal de Lavras; BrasilFil: Silva Hilsdorf, Alexandre Wagner. Universidade de Mogi das Cruzes; BrasilFil: Barufatti, Alexéia. Universidade Federal da Grande Dourados; BrasilFil: Hirschmann, Alice. Universidade Federal do Pampa; BrasilFil: Jung, Aline. Universidade Do Estado de Mato Grosso (unemat);Fil: Cruz Ramírez, Allan K.. Universidad Juárez Autónoma de Tabasco; MéxicoFil: Braga Silva, Alline. Instituto Federal de Goiás; BrasilFil: Cunico, Almir Manoel. Universidade Federal do Paraná; BrasilFil: Tagliaferro, Marina Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; Argentin

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