Abnormal repetitive behaviors in zebrafish and their relevance to human brain disorders

Abnormal repetitive behaviors (ARBs) are a prominent symptom of numerous human brain disorders and are commonly seen in rodent models as well. While rodent studies of ARBs continue to dominate the field, mounting evidence suggests that zebrafish (Danio rerio) also display ARB-like phenotypes and may therefore be a novel model organism for ARB research. In addition to clear practical research advantages as a model species, zebrafish share high genetic and physiological homology to humans and rodents, including multiple ARB-related genes and robust behaviors relevant to ARB. Here, we discuss a wide spectrum of stereotypic repetitive behaviors in zebrafish, data on their genetic and pharmacological modulation, and the overall translational relevance of fish ARBs to modeling human brain disorders. Overall, the zebrafish is rapidly emerging as a new promising model to study ARBs and their underlying mechanisms. © 2019 Elsevier B.V

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