18 research outputs found
Phase coexistence in a forecasting game
Individual choices are either based on personal experience or on information provided by peers. The latter case, causes individuals to conform to the majority in their neighborhood. Such herding behavior may be very efficient in aggregating disperse private information, thereby revealing the optimal choice. However if the majority relies on herding, this mechanism may dramatically fail to aggregate correctly the information, causing the majority adopting the wrong choice. We address these issues in a simple model of interacting agents who aim at giving a correct forecast of a public variable, either seeking private information or resorting to herding. As the fraction of herders increases, the model features a phase transition beyond which a state where most agents make the correct forecast coexists with one where most of them are wrong. Simple strategic considerations suggest that indeed such a system of agents self-organizes deep in the coexistence region. There, agents tend to agree much more among themselves than with what they aim at forecasting, as found in recent empirical studies
Efeito do isolamento e das variáveis ambientais na estrutura de comunidades de peixes do interflúvio madeira-purus na amazônia Brasileira
Due to the existence of terrestrial barriers to freshwater fish dispersion, it is believed that its distribution is strongly associated with historical factors related to the formation of the habitats they occupy. By the other hand, some studies reveal the influence of abiotic conditions (such as size of water bodies, pH, conductivity) on the composition of fish fauna occurring in small streams. This study aimed to investigate whether drainage basins, because catchment boundaries are potential barriers to fish dispersion, or the physical structure and physico-chemical characteristics of water have a greater influence on fish community structure in small streams. We sampled 22 streams belonging to five drainage basins in the Madeira-Purus interfluve. Fish were caught with dip nets and a small trawl, and data were simultaneously obtained on structural characteristics of the streams and physico-chemical characteristics of the water. Community composition was analyzed using Non-Metric Multidimensional Scaling (NMDS), and variables related to structural and physico-chemical characteristics were summarized by Principal Component Analysis (PCA). Two explanatory models relating faunal composition to environmental factors were constructed: the first using only continuous variables and the second including the drainage basin as a categorical variable. The Akaike Information Criterion (AIC) and AIC weight were used to select the best model. Although structural and physico-chemical variables significantly contributed to explaining faunal composition, the model including the drainage basin was clearly the better of the two models (more than 90% support in the data). The importance of drainage basins in structuring fish communities in streams may have significant consequences for conservation planning in these environments
The fish fauna of streams in the madeira-purus interfluvial region, Brazilian Amazon
Small headwaters streams of the Neotropical region usually have high species richness and diversity. This study aimed to investigate the species composition and abundance of fish fauna in the headwaters streams of the Madeira-Purus interfluvial plain in the Brazilian Amazon. A total of 22 streams of 1st to 3rd order were sampled during two expeditions at two separate locations in April-May and July of 2007. A total of 5508 fishes were captured using hand and small seine nets, belonging to 78 species, 22 families and six orders. Characiformes was the most diverse taxonomic group in the samples, followed by Gymnotiformes and Siluriformes. Our findings indicate that the fish fauna of streams in the Madeira- Purus interfluvial plain is both rich and diverse and should be considered during the implementation of strengthened environmental conservation strategies in this region. © 2011 Check List and Authors
A database of freshwater fish species of the Amazon Basin
The Amazon Basin is an unquestionable biodiversity hotspot, containing the highest freshwater biodiversity on earth and facing off a recent increase in anthropogenic threats. The current knowledge on the spatial distribution of the freshwater fish species is greatly deficient in this basin, preventing a comprehensive understanding of this hyper-diverse ecosystem as a whole. Filling this gap was the priority of a transnational collaborative project, i.e. the AmazonFish project - https://www.amazon-fish.com/. Relying on the outputs of this project, we provide the most complete fish species distribution records covering the whole Amazon drainage. The database, including 2,406 validated freshwater native fish species, 232,936 georeferenced records, results from an extensive survey of species distribution including 590 different sources (e.g. published articles, grey literature, online biodiversity databases and scientific collections from museums and universities worldwide) and field expeditions conducted during the project. This database, delivered at both georeferenced localities (21,500 localities) and sub-drainages grains (144 units), represents a highly valuable source of information for further studies on freshwater fish biodiversity, biogeography and conservation
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
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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
Adaptação de um Índice de Integridade Biótica para igarapés da Amazônia Central, com base em atributos ecológicos da comunidade de peixes
The Distrito Agropecuário da Zona Franca de Manaus (DAS) is inserted in the Central Amazon, a region that harbors high biological and ecosystems diversity, and that is suffering the environmental impacts resulting from the urban growth of Manaus. In the present study we aimed to know the fish fauna of the forest streams of the DAS and adapt an Index of Biotic Integrity (IBI) based on the characteristics of those fish assemblages. Our main objective was to gather information that could help identifying, monitoring and mitigate environmental impacts in aquatic systems. The samples were obtained in 30 streams of 1st and 2nd order, from four hydrographic basins (Cuieiras river, n = 11; Tarumã river, n = 6; Preto da Eva river, n = 6 and Urubu river, n = 7). Fish collecting and environmental data gathering were done in 50-m stream reaches. The fish were captured with hand nets, metallic sieves and small seine nets. Data on environment characteristics and some water quality parameters were also recorded in each sampling station. 2674 fish were captured, belonging to 87 species, 22 families and six orders. Characiformes showed the greatest richness (39 species), representing 67% of the total number of specimens collected, followed by Siluriformes with 17 species, Perciformes (14), Gymnotiformes (12), Synbranchiformes (3) and Cyprinodontiformes (3). Cyprinodontiformes was the second in abundance with 11.4% total number of specimens collected, followed by Perciformes (8.6%), Gymnotiformes (6.9%), Siluriformes (5.3%) and Synbranchiformes (0.7%). Environmental variables were analyzed through a Principal Components Analysis (PCA) jointly with an Environmental Assessment Protocol to verify the relation of the IBI with the environmental characteristics. Fish abundance were ordered by Non-metric Multidimensional Scaling (NMDS) and tested for the relationships with the IBI. The IBI showed significant correlations only with PCA-1 (Pearson, r = -0,62; p <0,001), with the results of the environmental assessment protocol (Pearson, r = -0,63; p <0,001), and with the fish species composition (NMDS-1, Pearson, r = 0,48; p = 0,007). The use of the IBI resulted in a wide variation of the scores for both undisturbed and disturbed streams, with low values for streams supposedly undisturbed, and high values for some streams with clear signals of environmental disturbances. Despite these weaknesses, IBI values were significantly different between the undisturbed and disturbed streams (U = 145; p = 0,012). The problems pointed out in the present study indicate that the IBI should be employed with caution for Central Amazon streams. The natural variability in environmental characteristics and the high species richness in Amazonian streams difficult a regional adaptation of IBI, and more information is necessary for choosing and refining adequate metrics.A área do Distrito Agropecuário da Superintendência da Zona Franca de Manaus está inserida em uma região da Amazônia Central que abriga grande diversidade biológica e de ecossistemas, e está sofrendo impactos ambientais decorrentes da expansão da cidade de Manaus. Dessa forma, conhecer a ictiofauna de igarapés de terra-firme da região e adaptar um Índice de Integridade Biótica (IIB), podem auxiliar na identificação de impactos em curso e na prevenção da degradação de ambientes aquáticos, além de contribuir para a elaboração de estratégias de conservação e recuperação de áreas degradadas. As amostragens foram realizadas entre os meses de abril e agosto de 2004 em 30 igarapés de 1ª e 2ª ordens (Cuieiras n = 11; Tarumã n = 6; Preto da Eva n = 6 e Urubu n = 7) em trechos de 50 m dos igarapés. Os peixes foram capturados com puçás, peneiras e redes de arrasto. Foram registradas informações sobre o ambiente e algumas variáveis físico-químicas da água para caracterizar a heterogeneidade natural do ambiente, bem como eventuais modificações produzidas por ações antrópicas. Foram capturados 2674 exemplares de peixes, pertencentes a 87 espécies, 22 famílias e seis ordens. Characiformes foi o grupo que apresentou a maior riqueza (39 espécies), representando 67% do total de exemplares coletados, seguido de Siluriformes com 17 espécies, Perciformes (14), Gymnotiformes (12), Synbranchiformes (3) e Cyprinodontiformes (3). Apesar de Cyprinodontiformes apresentar somente três espécies, foi a segunda em abundância com 11,4% dos exemplares, seguida dos Perciformes (8,6%), Gymnotiformes (6,9%), Siluriformes (5,3%) e Synbranchiformes (0,7%). As variáveis ambientais foram analisadas através de uma PCA (Análise de Componentes Principais) juntamente com um protocolo ambiental (IIA) para verificar a relação do IIB com as características ambientais. Os dados da abundância da comunidade de peixes foram sumarizados com um Escalonamento Multidimensional Não-métrico (NMDS) para verificar as relações com o IIB. O IIB apresentou correlação significativa somente com o eixo 1 da PCA (Pearson, r = -0,62; p < 0,001), com os resultados do IIA (Pearson, r = -0,63; p < 0,001) e com a composição de espécies de peixes (eixo 1 da NMDS; Pearson, r = 0,48; p = 0,007). A aplicação do Índice de Integridade Biótica produziu alguns resultados discrepantes, com valores baixos para igarapés supostamente livres de impactos, e altos para certos igarapés com indícios de alterações ambientais. A despeito dessas inconsistências, os valores do IIB foram significativamente diferentes entre os igarapés íntegros e alterados (U = 145; p = 0,012). Apesar de o IIB estar sendo adaptado com grande sucesso em diversas regiões do mundo, precisa ser avaliado com cautela para a Amazônia Central. As características do ambiente e a grande diversidade de espécies encontradas na região tornam a adaptação do índice um pouco mais complexa, e serão necessárias mais informações para estabelecer e refinar medidas mais adequadas e consistentes