13 research outputs found

    Impacts of a large hydroelectric dam on the Madeira River (Brazil) on floodplain avifauna

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    Hydroelectric dams represent an important threat to seasonally flooded environments in the Amazon basin. We aimed to evaluate how a dam in the Madeira River, one of the largest tributaries of the Amazonas River, affected floodplain avifauna. Bird occurrence was recorded through simultaneous passive acoustic monitoring in early successional vegetation and floodplain forest downstream from the dam and upstream in sites impacted by permanent flooding after dam reservoir filling. Species were identified through manual inspection and semi-automated classification of the recordings. To assess the similarity in vegetation between downstream and upstream sites, we used Landsat TM/ETM+ composite images from before (2009-2011) and after (2016-2018) reservoir filling. Downstream and upstream floodplain forest sites were similar before, but not after dam construction. Early successional vegetation sites were already different before dam construction. We recorded 195 bird species. While species richness did not differ between upstream and downstream sites, species composition differed significantly. Ten species were indicators of early successional vegetation upstream, and four downstream. Ten species were indicators of floodplain forest upstream, and 31 downstream. Seven of 24 floodplain specialist species were detected by the semi-automated classification only upstream. While we found some bird species characteristic of early successional vegetation in the upstream sites, we did not find most species characteristic of tall floodplain forest. Predominantly carnivorous, insectivorous, and nectarivorous species appear to have been replaced by generalist and widely distributed species.</p

    Environmental correlates of taxonomic and phylogenetic diversity in the Atlantic Forest

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    Aim There is little consensus on which environmental variables are best at predicting multiple dimensions of diversity. We ask whether there are common environmental correlates of diversity, despite ecological differences, across nine clades of plants and animals distributed along a single rainforest domain. For that, we compare the environmental correlates of species richness, phylogenetic diversity, and phylogenetic endemism. Location Brazilian Atlantic Forest. Taxon:Five clades of plants (Bromelioideae, Miconieae, Bertolonia, Cambessedesieae, and the Fridericia and allies) and four clades of animals (butterlies in the tribe Ithomiini, frogs in the genera Boana and Proceratophrys, and birds in the subfamily Thraupinae). Methods Using curated occurrence localities and phylogenetic data, we generated maps of (a) species richness, (b) phylogenetic diversity, (c) residuals of phylogenetic diversity regressed on species richness, and (d) phylogenetic endemism for all groups. We also compiled a set of 30 environmental descriptors, including records of current temperature and precipitation, climatic stability over time, and topography. Through a machine learning framework, we explored the environmental correlates of each of these diversity measures for each group. Results The environmental variables used here were strong predictors of diversity for all studied groups. However, models for phylogenetic endemism had lower predictive power. Although patterns of diversity are different among groups, correlates of diversity are consistent across taxa. For both species richness and phylogenetic diversity, current precipitation and precipitation stability over time were consistently ranked among the variables that strongly correlate with diversity patterns. The correlates of phylogenetic endemism were less homogeneous across groups. The results suggest that including climate stability over time is important when predicting diversity measures that reflect historical components. Main conclusions Investigating environmental correlates of diversity for multiple clades and diversity measures in a single geographic area allows for a better understanding of common patterns across taxa. This study shows that environmental conditions, particularly precipitation, are good predictors of the patterns of species richness and phylogenetic diversity‐but not phylogenetic endemism‐across different Atlantic Forest groups.Additional co-authors: Marcelo Reginato, Karina Lucas Silva‐Brandão, André Victor Lucci Freitas, Renato Goldenberg, Lúcia G. Lohmann, Fabián A. Michelangeli, Cristina Miyaki, Miguel T. Rodrigues, Ana C. Carnava

    The role of environmental filtering, geographic distance and dispersal barriers in shaping the turnover of plant and animal species in Amazonia

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    To determine the effect of rivers, environmental conditions, and isolation by distance on the distribution of species in Amazonia. Location: Brazilian Amazonia. Time period: Current. Major taxa studied: Birds, fishes, bats, ants, termites, butterflies, ferns + lycophytes, gingers and palms. We compiled a unique dataset of biotic and abiotic information from 822 plots spread over the Brazilian Amazon. We evaluated the effects of environment, geographic distance and dispersal barriers (rivers) on assemblage composition of animal and plant taxa using multivariate techniques and distance- and raw-data-based regression approaches. Environmental variables (soil/water), geographic distance, and rivers were associated with the distribution of most taxa. The wide and relatively old Amazon River tended to determine differences in community composition for most biological groups. Despite this association, environment and geographic distance were generally more important than rivers in explaining the changes in species composition. The results from multi-taxa comparisons suggest that variation in community composition in Amazonia reflects both dispersal limitation (isolation by distance or by large rivers) and the adaptation of species to local environmental conditions. Larger and older river barriers influenced the distribution of species. However, in general this effect is weaker than the effects of environmental gradients or geographical distance at broad scales in Amazonia, but the relative importance of each of these processes varies among biological groups

    Pervasive gaps in Amazonian ecological research

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    Pervasive gaps in Amazonian ecological research

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    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

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    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

    Avifauna da região do Juruti, Pará, Brasil.

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    Disponível em: http://www.ararajuba.org.br/sbo/ararajuba/artigos/Volume192/volume192.htmAvifauna da região do Juruti, Pará, Brasil. A região que compreende o interflúvio Madeira‑Tapajós é certamente uma das regiões brasileiras de maior complexidade ambiental e um dos mais importantes centros de endemismos de aves da América do Sul, denominado centro de endemismo Madeira ou Rondônia. Entretanto, essa região vem sofrendo um crescente aumento nas pressões antrópicas, principalmente pelo desmatamento, o que implica uma forte preocupação sobre a conservação de toda a biota dessa região. Nesse sentido, estivemos no período de setembro de 2002 a março de 2011 realizando um inventário da avifauna na região do município de Juruti, na divisa entre os estados do Pará e Amazonas. Nesse levantamento, registramos um total de 490 espécies de aves, distribuídas em 68 famílias. Ressalta‑se o registro de algumas espécies importantes do ponto de vista biogeográfico e de conservação (Nothocrax urumutum, Leucopternis melanops, Anodorhynchus hyacinthinus, Aratinga aurea, Neomorphus geoffroyi, Nyctibius bracteatus, Capito brunneipectus, Picumnus varzeae, Skutchia borbae, Rhegmatorhina berlepschi, Pachyramphus surinamus, Contopus virens, Cyanicterus cyanicterus), que são discutidos em detalhes

    Damming the rivers of the Amazon basin

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    More than a hundred hydropower dams have already been built in the Amazon basin and numerous proposals for further dam constructions are under consideration. The accumulated negative environmental effects of existing dams and proposed dams, if constructed, will trigger massive hydrophysical and biotic disturbances that will affect the Amazon basin's floodplains, estuary and sediment plume. We introduce a Dam Environmental Vulnerability Index to quantify the current and potential impacts of dams in the basin. The scale of foreseeable environmental degradation indicates the need for collective action among nations and states to avoid cumulative, far-reaching impacts. We suggest institutional innovations to assess and avoid the likely impoverishment of Amazon rivers.NSF [FESD-1338694, EAR-1147954, DDRI-1558446]; NASA [NAG5-6120]; National Geographic Society-Research and Exploration Grant [8855-10]; LLILAS-Mellon; Brazilian Council for Scientific and Technological Development-CNPq; CAPES Foundation6 month embargo; Published online 14 June 2017This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
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