Epidemiological status of bovine brucellosis in the State of Goiás, Brazil

Realizou-se um estudo para caracterizar a situação epidemiológica da brucelose no Estado de Goiás. O Estado foi estratificado em três circuitos produtores. Em cada circuito foram amostradas aleatoriamente 300 propriedades e, dentro dessas, foi escolhido de forma aleatória um número pré-estabelecido de animais, dos quais foi obtida uma amostra de sangue. No total, foram amostrados 10.744 animais, provenientes de 900 propriedades. Em cada propriedade visitada aplicou-se um questionário epidemiológico para verificar o tipo de exploração e as práticas de criação e sanitárias que poderiam estar associadas ao risco de infecção pela doença. O protocolo de testes utilizado foi o da triagem com o teste do antígeno acidificado tamponado e a confirmação dos positivos com o teste do 2-mercaptoetanol. O rebanho foi considerado positivo quando pelo menos um animal foi reagente às duas provas sorológicas. No estrato 1, a prevalência foi de 7,7% [4,7-10,7%] para propriedades, e de 1,4% [0,99-1,7%] para animais. No estrato 2, foi de 19,5% [15,0-24,0%] para propriedades e de 2,6% [2,0-3,1%] para animais. No estrato 3, foi de 21,4% [16,7-26,1] para propriedades e 4,3% [3,7-5,0%] para animais. A prevalência obtida para o Estado foi de 17,5% [14,9-20,2%] para propriedades e de 3,0% [2,7-3,3%] para animais. Os fatores de risco (odds ratio, OR) associados à condição de foco, segundo a análise multivariada, foram: compra de reprodutores a comerciantes de gado (OR = 2,06 [1,12-3,52]), ocorrência de abortos nos últimos 12 meses (OR = 5,83 [3,86-8,8]) e prática de vacinação contra brucelose (OR = 2,07 [1,38-3,09]). Tanto a ocorrência de aborto quanto a vacinação são, neste caso, consequência da presença de brucelose no rebanho. __________________________________________________________________________________________________________________ ABSTRACTA study to characterize the epidemiological status of brucellosis in the State of Goiás was carried out. The State was divided in three regions. Three hundred herds were randomly sampled in each region and a pre-established number of animals was sampled in each of these herds. A total of 10,744 serum samples from 900 herds were collected. In each herd, it was applied an epidemiological questionnaire focused on herd traits as well as husbandry and sanitary practices that could be associated with the risk of infection. The serum samples were screened for antibodies against Brucella spp. by the Rose-Bengal Test (RBT), and all positive sera were re-tested by the 2-Mercaptoethanol test (2-ME). The herd was considered positive if at least one animal was positive on both RBT and 2-ME tests. For region 1, the herd prevalence was 7.7% [4.7-10.7%] and the animal prevalence was 1.4% [0.99-1.7%]. For region 2, the herd prevalence was 19.5% [15.0-24.0%] and the animal prevalence was 2.6% [2.0-3.1%]. For region 3, the herd prevalence was 21.4% [16.8-26.1%] and the animal prevalence was 4.3% [3.7-5.0%]. For the whole state, the herd prevalence was 17.5% [14.9-20.2%] and the animal prevalence was 3.0% [2.7-3.3%]. The multivariate analysis identified the following risk factors (odds ratio, OR) associated with positive herds: purchase of breeding stock from cattle traders (OR = 2.06 [1.12-3.52]), occurrence of abortions over the last 12 months (OR = 5.83 [3.86-8.8]), and vaccination against brucellosis (OR = 2.07 [1.38-3.09]). Both the abortions and the vaccination are, in this case, a consequence of the herd being infected with brucellosis

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

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

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics