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

Primary endemic Cryptococcosis gattii by molecular type VGII in the state of Pará, Brazil

In order to study the infectious agents causing human disseminated cryptococcosis in the state of Pará, North Brazil, 56 isolates of Cryptococcusspp. (54 isolated from cerebral spinal fluid and two from blood cultures) from 43 cases diagnosed between 2003-2007 were analysed. The species were determined through morphological and physiological tests and genotypes were determined by URA5-RFLP and PCR-fingerprinting (wild-type phage M13). The following species and genotypes were identified: Cryptococcus neoformans VNI (28/56, 50%), Cryptococcus gattii VGII (25/56, 44.64%) and C. gattii VGI (3/56, 5.26%). The genotype VNI occurred in 12 out of 14 HIV-positive adults, whereas the genotype VGII occurred in 11 out of 21 HIV-negative adults (p < 0.02, OR = 6.6 IC95% 0.98-56.0). All patients less than 12 years old were HIV negative and six cases were caused by the VGII genotype, one by the VGI and one by VNI. Therefore, endemic primary mycosis in HIV-negative individuals, including an unexpectedly high number of children, caused by the VGII genotype deserves further study and suggests the need for surveillance on cryptococcal infection in the state of Pará, Eastern Amazon

Primary Endemic Cryptococcosis Gattii by Molecular Type VGII in the State of Pará, Brazil

In order to study the infectious agents causing human disseminated cryptococcosis in the state of Pará, North Brazil, 56 isolates of Cryptococcus spp. (54 isolated from cerebral spinal fluid and two from blood cultures) from 43 cases diagnosed between 2003-2007 were analysed. The species were determined through morphological and physiological tests and genotypes were determined by URA5-RFLP and PCR-fingerprinting (wild-type phage M13). The following species and genotypes were identified: Cryptococcus neoformans VNI (28/56, 50%), Cryptococcus gattii VGII (25/56, 44.64%) and C. gattii VGI (3/56, 5.26%). The genotype VNI occurred in 12 out of 14 HIV-positive adults, whereas the genotype VGII occurred in 11 out of 21 HIV-negative adults (p < 0.02, OR = 6.6 IC95% 0.98-56.0). All patients less than 12 years old were HIV negative and six cases were caused by the VGII genotype, one by the VGI and one by VNI. Therefore, endemic primary mycosis in HIV-negative individuals, including an unexpectedly high number of children, caused by the VGII genotype deserves further study and suggests the need for surveillance on cryptococcal infection in the state of Pará, Eastern Amazon