Seletividade de inseticidas usados na cultura da macieira a duas populações de Chrysoperia externa (Hagen, 1861) (Neuroptera: Crysopidae).

Neste trabalho, foi avaliado o efeito de inseticidas sobre larvas de duas populações de Chrysoperla externa (Hagen. 1861) (Neuroptera: Chrysopidae), oriundas de pomares de Bento Gonçalves e Vacaria, RS O trabalho FOi realizado em laboratório (25±2°C, UR de 70±10% e fotofase de 12 horas) avaliando-se inseticidas, nas concentrações indicadas pelos fabricantes e/ou que estão em Jase de pesquisa para o controle da mariposa oriental, lagarta enroladeira e mosca-das-Jrutas na cultura da macieira. Os inseticidas e dosagens (g ou mL do p. c./ I OOL de água) avaliados Joram: Josmet (Imidan 500 PM - 200), metoxifenozide (lntrepid 240 SC - 60), tebufenozide (Mimic 240 SC - 60), benz oato de emamectina (Proclaim 5 SG - 15), spinosad (Tracer 480 SC - 20), etoJenprox (Trebon 100 SC - 150), clorpirifos etil (Lorsban 480 BR - 150) e testemunha (somente água). As pulverizações Joram realizadas em larvas de primeiro instar utilizando torre de Pouer. Avaliaram-se a sobrevivência e a duração das Jases de larva e pupa e, a fecundidade e a fertilidade dos adultos sobreviventes. A toxicidade dos produtos JOi calculada em Junção do eJeito total (E) de cada produto, conJorme recomendações da 10Be Para larvas de primeiro instar do crisopide o oriundas de Bento Gonçalves, be nz oato de emame ctin a. foi classificado como inofensivo (classe I); meioxifenozide, etoJenprox, tebufenozide, spinosad e fosmet, como levemente nocivos (classe 2) e clorpirtfos, como nocivo (classe 4). Já para a população larval de C externa de Vacaria, benz oato de emame ct in a, meioxifenozi de, etoJenprox, tebufenozide e spinosad Joram inofensivos; Josmet mostrou-se moderadamente nocivo (classe 3) e clorpirifos JOi nocivo. Palavras-chave: agrotoxicos, crisopide o, efeitos tóxicos, manejo integrado de pragas, controle biológico. ABSTRACT The effect oJ some insecticides on larvae oJ two populations of Chrysoperla externa (Hagen, 1861) (Neuroptera: Chrysopidae) Jrom Bento Gonçalves and Vacaria, RS were evaluated under laboratory conditions (25±2°C, RH of 70±10% and 12 hours-photophase). The compounds were used at commercial or research concentrations used to conlrol oriental fruit moth, leaf roller and fruit jly on apple orchards. The products and rates (g ou mL of Jormulated product/l Oül. o\JF water) evaluated were: phosmet (Imidan 500 PM - 200), methoxyfenozide (Intrepid 240 SC - 60), tebufenozide (Mimic 240 SC - 60), emamectin benz oate (Pr oclain 5 SG - 15), spinosad (Tracer 480 SC - 20), etoJenprox (Trebon 100 SC - 150) and chlorpyrifos (Lorsban 480 BR - 150), using water as control. Spraying of insecticides \Vas on first-instar larvae using the Potter 10\Ver. The survival rate and duration, larval and pupal development time and, fecundity and fertility oJ survivor adults \Vere evaluated. The toxic effect of each product was estimated by the total eJJect (E) according to the 10BC re commendations, Emamectin benzoate \Vas classified as harmless (class I) 10 first-instar larvae oJ C externa Jrom Bento Gonçalves. Methoxyfenozide, etoJenprox, tebufenozide, spinosad and phosmet \Vere classified as slightly harmJul (class 2), and chlorpyrifos \Vas classified as harmful (class 4). Emamectin benzoate \Vas harmless; Josmet \Vas moderately harmJul (class 3), and chlorpyrifos \Vas harmJulto the C externa from Vacaria. KEy words: pesticides, green lacewing, toxic effects, integrated pest management. biological control

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, 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