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

The feline cutaneous and oral microbiota are influenced by breed and environment.

Previous research revealed the feline skin bacterial microbiota to be site-specific and the fungal microbiota to be individual-specific. The effect of other factors, such as genotype and environment, have not yet been studied in cats, but have been shown to be potentially important in shaping the cutaneous microbiota of other animals. Therefore, the objectives of this study were to evaluate the effect of these factors on the bacterial and fungal microbiota of feline skin and oral cavity. The influence of genotype was assessed through the analysis of different cat breeds, and the influence of environment through comparison of indoor and outdoor cats. DNA was extracted from skin and oral swabs, and bacterial and fungal next-generation sequencing were performed. Analysis of the skin microbiota of different cat breeds revealed significant differences in alpha diversity, with Sphynx and Bengal cats having the most diverse communities. Many taxa were found to be differentially abundant between cat breeds, including Veillonellaceae and Malassezia spp. Outdoor environment exposure had considerable influence on beta diversity, especially in the oral cavity, and resulted in numerous differentially abundant taxa. Our findings indicate that the oral bacterial microbiota and both fungal and bacterial microbiota of feline skin are influenced by breed, and to a lesser degree, environment

Cerebrospinal fluid analysis in 58 ruminants showing neurological disorders

ABSTRACT: Ruminants may be affected by a wide variety of central nervous system (CNS) diseases. Cerebrospinal fluid (CSF) analysis forms the basis for ante mortem diagnostic evaluation of ruminants with clinical signs involving the CNS. Despite its importance as a tool to aid diagnosis, data regarding CSF examinations in spontaneous cases of CNS diseases in ruminants from Brazil are limited, and most reports involve experimental studies. Therefore, this study aimed to report the results of CSF analysis in 58 ruminants showing signs of neurological disorders. CSF samples for analysis were obtained from 32 cattle, 20 sheep, and 6 goats by cerebello-medullary cistern (n=54) or lumbosacral space (n=4) puncture. These ruminants showed neurological signs related to viral (n=13), mycotic (n=3), or bacterial (n=15) infections, and toxic (n=21), traumatic (n=4), or congenital disorders (n=2). CSF analysis from ruminants with viral infections presented lymphocytic pleocytosis, even though CSF showed no changes in several cases of rabies. Neutrophilic pleocytosis, cloudiness, presence of fibrin clots, and abnormal coloration were evident in the CSF of most cases of CNS bacterial infection, such as meningoencephalitis, meningitis, abscesses, myelitis, and a case of conidiobolomycosis. On the other hand, CSF was unchanged in most cases of toxic disorders, as botulism and hepatic encephalopathy. Elevated CSF density was observed in 60% of ruminants diagnosed with polioencephalomalacia. Our findings show that evaluation of CSF is a valuable diagnostic tool when used in association with epidemiological, clinical and pathological findings for diagnosis of CNS diseases in ruminants

sj-xlsx-1-vet-10.1177_03009858241231557 – Supplemental material for Experimental oral administration of pollen beetle (Astylus atromaculatus) to cattle results in an acute lethal gastrointestinal disease

Supplemental material, sj-xlsx-1-vet-10.1177_03009858241231557 for Experimental oral administration of pollen beetle (Astylus atromaculatus) to cattle results in an acute lethal gastrointestinal disease by Federico Giannitti, Mizael Machado, Caroline da Silva Silveira, Ximena Cibils-Stewart, Nicolás Baráibar, Cintia R. R. Queiroz-Machado, Robert H. Poppenga, Alejo Menchaca, Francisco A. Uzal, Juan A. García, Carolina Matto, Fernando Dutra, Gretel Ruprechter, Darío Caffarena and Anderson Saravia in Veterinary Pathology</p