Revision of Tympanopleura Eigenmann (Siluriformes: Auchenipteridae) with description of two new species

The Neotropical catfish genus Tympanopleura, previously synonymized within Ageneiosus, is revalidated and included species are reviewed. Six species are recognized, two of which are described as new. Tympanopleura is distinguished from Ageneiosus by having an enlarged gas bladder not strongly encapsulated in bone; a prominent pseudotympanum consisting of an area on the side of the body devoid of epaxial musculature where the gas bladder contacts the internal coelomic wall; short, blunt head without greatly elongated jaws; and smaller adult body size. Species of Tympanopleura are distinguished from each other on the basis of unique meristic, morphometric, and pigmentation differences. Ageneiosus melanopogon and Tympanopleura nigricollis are junior synonyms of Tympanopleura atronasus. Tympanopleura alta is a junior synonym of Tympanopleura brevis. A lectotype is designated for T. brevis. Ageneiosus madeirensis is a junior synonym of Tympanopleura rondoni. Tympanopleura atronasus, T. brevis, T. longipinna, and T. rondoni are relatively widespread in the middle and upper Amazon River basin. Tympanopleura cryptica is described from relatively few specimens collected in the upper portion of the Amazon River basin in Peru and the middle portion of that basin in Brazil. Tympanopleura piperata is distributed in the upper and middle Amazon River basin, as well as in the Essequibo River drainage of Guyana

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

Relationship between evenness and body size in species rich assemblages

Evenness is a key measure of community structure. Here, we examine the relationship between evenness and size–abundance distributions for both individuals and species using data gathered from Amazonian fish assemblages. We show that evenness increases as the fraction of numerically abundant species in larger body-size classes rises. As any processes that enable larger bodied species to increase their numerical dominance will influence evenness, these results help explain why evenness is an important correlate of ecosystem function