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

Babesiosis and anaplasmosis in dairy cattle in Northeastern Brazil

The goal of this study was to characterize the epidemiological situation and the factors involved in the prevalence of babesiosis and anaplasmosis in cattle in the dairy basin of Parnaíba, Piauí, Brazil. The study was conducted in 22 farms, and collected blood samples from 202 cattle to study serological, molecular and determination of the packed cell volume (PCV). On the farms were applied surveys involving epidemiological aspects. Seroprevalence rates were: Babesia bigemina 52.5%, B. bovis 68.8%, and Anaplasma marginale 89.1%. Of the samples analyzed, 73.3% were reactive for Babesia spp. and A. marginale, showing co-infection. In PCR, B. bigemina and B. bovis were positive in 52.0% and 33.2% respectively, and A. marginale in 76.2%. Of these, 51.5% amplified DNA of Babesia spp. and A. marginale. The semi-intensive management predominated in 68.0% of the farms studied. The clinical history of babesiosis and anaplasmosis, was reported from 73% of the farms. There was no significant difference (p>0.05) between age groups and for the PCV of positive compared with negative animals. The study indicates that in this region is enzootic instability for babesiosis and enzootic stability for anaplasmosis, reinforcing the fact that in Brazil there are areas of enzootic instability, even in tropical regions of the country. The PCR technique was a valuable tool for the diagnosis of these diseases and may be used to characterize a geographic region