New records of predation of Harpactorinae (Hemiptera: Reduviidae) over Euglossini and Xylocopini bees (Hymenoptera: Apidae) in Brazil.

Abstract. The predatory activities of Apiomerus duckei Costa Lima, Seabra & Hathaway, 1951, Apiomerus pilipes (Fabricius, 1787) and Apiomerus luctuosus Costa Lima, Seabra & Hathaway, 1951 (Hemiptera: Reduviidae: Harpactorinae: Apiomerini) on orchid bees (Hymenoptera: Apidae: Apinae: Euglossini) in odoriferous traps in the influence area of Santo Antônio Hydroelectric Power Plant, Rondônia State, Brazil, and of Cosmoclopius annulosus Stål, 1872 (Hemiptera: Reduviidae: Harpactorinae: Harpactorini) on the bee Ceratina rupestris Holmberg, 1884 (Hymenoptera: Apidae: Apinae: Xylocopini: Ceratinina), in an experimental area cultivated with canola in Passo Fundo, Rio Grande do Sul State, Brazil, are recorded by the first time. Resumen. Se registran por primera vez las actividades depredadoras de Apiomerus duckei Costa Lima, Seabra y Hathaway, 1951, Apiomerus pilipes (Fabricius, 1787) y Apiomerus luctuosus Costa Lima, Seabra y Hathaway, 1951 (Hemiptera: Reduviidae: Harpactorinae: Apiomerini) sobre abejas orquídeas (Hymenoptera: Apidae: Apinae: Euglossini) en trampas odoríferas ubicadas en el área de influencia de la Central Hidroeléctrica Santo Antônio (HEP), Estado de Rondônia, Brasil, y de Cosmoclopius annulosus Stål, 1872 (Hemiptera: Reduviidae: Harpactorinae: Harpactorini) sobre la abeja Ceratina rupestris Holmberg, 1884 (Hymenoptera: Apidae: Apinae: Xylocopini: Ceratinina), en un área experimental cultivada con canola en Passo Fundo, Rio Grande do Sul State, Brasil

HERBase: a collection of understorey herb vegetation plots from Amazonia.

In this article, we describe the database HERBase, an exhaustive compilation of published and unpublished data on herb inventories in Amazonia

Fauna of euglossina (Hymenoptera: Apidae) from southwestern Amazonia, Acre, Brazil

Male orchid bees were collected between December 2005 and September 2006 in 11 forest areas of different sizes in the region of Rio Branco, Acre, Southwestern Amazonia, Brazil. The bees were attracted by 6 aromatic compounds and collected by insect nets and scent baited traps. A total of 3,675 males of Euglossina in 4 genera and 36 species were collected. Eulaema cingulata (Fabricius) was the most common (24.6%), followed by Eulaema meriana (Olivier) (14.6%), Euglossa amazonica Dressler (10.5%), Eulaema nigrita Lepeletier (10.5%) and Eulaema pseudocingulata (Oliveira) (7.2%). Cineole was the scent that attracted the greatest number of individuals (23.8%) and methyl salicylate the greatest number of species (28) for both methods of sampling. Thirty one bees of 9 species with pollinar orchid attached to their bodies were collected. The accumulative number of species stabilized after the 48th collection. Few species were abundant; the great majority were represented by less than 50 bees. The lack of standardized sample protocols limited very much the conclusions derived from comparisons among the majority of studies on Euglossina assemblages. However, the results presented here suggest that the State of Acre is very rich in those bees compared to other regions.Machos de abelhas Euglossina foram coletados entre dezembro de 2005 e setembro de 2006 em 11 áreas florestais de diferentes tamanhos na região de Rio Branco, Acre, Amazônia Sul-Ocidental. As abelhas foram atraídas por 6 substâncias odoríferas e coletadas com rede entomológica e armadilhas. Um total de 3.675 machos de Euglossina pertencentes a 4 gêneros e 36 espécies foi coletado. Eulaema cingulata (Fabricius) foi a espécie mais comum (24,6%), seguida por Eulaema meriana (Olivier) (14,6%), Euglossa amazonica Dressler (10,5%), Eulaema nigrita Lepeletier (10,5%) e Eulaema pseudocingulata (Oliveira) (7,2%). Cineol foi a substância que atraiu maior número de indivíduos (23,8%) e metil salicilato o maior número de espécies (28) para ambos os métodos de coleta. Foram coletados 31 indivíduos pertencentes a 9 espécies portando polinários. O número acumulado de espécies coletadas na região estabilizou a partir da 48ª coleta. Poucas espécies foram abundantes, a maioria representada por menos que 50 indivíduos. A falta de um protocolo amostral padronizado tem limitado comparações entre trabalhos realizados em diferentes regiões. Contudo, os resultados aqui apresentados indicam que o Acre apresenta elevada riqueza dessas abelhas

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, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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