Hábitos alimenticios de la rana bromélicola Phyllodytes edelmoi Peixoto, Caramaschi & Freire, 2003 (Anura: Hylidae) del Estado de Alagoas, Noreste de Brasil

El género de hílidos Phyllodytes está compuesto por 12 especies distribuidas en el este de Brasil y conocidas por habitar estrictamente bromeliáceas. En este estudio se exploran los hábitos alimenticios de P. edelmoi y se testea la selectividad de las presas por la especie comparando las presas encontradas en los estómagos con la disponibilidad de presas en las bromelias. Nuestros resultados muestran que P. edelmoi consume grandes cantidades de hormigas a lo largo del año, por lo que podría considerarse una especie "especializada en hormigas". Este comportamiento alimenticio especializado podría ser considerado una sinapomorfia para el género Phyllodytes.The hylid frog genus Phyllodytes comprised 12 species distributed in eastern Brazil and knownto strictly inhabit inside bromeliads. In this study, we explore the feeding habits of P. edelmoiand test the prey selectivity of the species by comparing the prey items in the stomachs contentswith the availability of preys in bromeliads. Our results show that P. edelmoi consumes highamounts of ants along the year, and therefore it could be considered an ?ant specialist? species.This specialist feeding behaviour could be considered a synapomorphy of the genus Phyllodytes.Fil: Araujo-Vieira, Katyuscia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales “Bernardino Rivadavia”; ArgentinaFil: Ubiratan G. Silva. Universidade Federal de Alagoas; BrasilFil: Jhonatan Guedes dos Santos. Universidade Federal de Alagoas; BrasilFil: Thame G. Ferreira. Departamento de Ecologia E Biomonitoramento; BrasilFil: Gabriel O. Skuk. Universidade Federal de Alagoas; Brasi

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