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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Ocidental. Para informações adicionais entre em contato com cpaa.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Ocidental. |
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Data corrente: |
21/12/2012 |
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Data da última atualização: |
05/02/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MOUGA, D. M. D. da S.; NOBLE, C. F.; BUSSMANN, D. B. G.; KRUG, C. |
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Afiliação: |
DENISE MONIQUE DUBET DA SILVA MOUGA, UNIVILLE/SC; CAROLINE FURTADO NOBLE, UNIVILLE/SC; DANIELA BEATRIZ GOUDARD BUSSMANN, UNIVILLE/SC; CRISTIANE KRUG, CPAA. |
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Título: |
Bees and plants in a transition area between atlantic rain forest and araucaria forest in Southern Brazil. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Revue D'Écologie, France, v. 67, p. 313-327, 2012. |
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Idioma: |
Inglês |
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Conteúdo: |
The community of native bees from a transition area between Atlantic rain forest and Araucaria forest in Joinville, Santa Catarina state, Brazil, was studied regarding to species richness, relative abundance, floral resources and plant interactions. Observations were made monthly from 2008 to 2009, using entomological nets. 710 individuals of 88 species were sampled from the five bee subfamilies existing in Brazil. The bees were sampled on 62 plant species from 29 families. The most visited plant families were Asteraceae (48 %), Lamiaceae (10 %), Saxifragaceae (9 %) and Rosaceae (8 %). The bee subfamily with the highest species diversity was Halictinae (44 %), followed by Apinae (38 %), Andreninae (11 %), Megachilinae (8 %) and Colletinae (1 %). The subfamilies abundance sequence was: Apinae (81 %), Halictinae (12 %), Andreninae and Megachilinae (both 3 %) and Colletinae (less than 1 %). Apis mellifera L. was the most abundant species (42 %), followed by Trigona spinipes (Fabricius) (14 %) and Plebeia sp. This study depicts a system with asymmetric interactions shown by the species grouping, with a predominance of general relationships, revealing the relative importance of abundance for mutual networks nesting. The results from the network metrics evaluated reveal a robust and diverse web, in a recurrent feature of biodiversity structuring. |
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Palavras-Chave: |
Bees; Interactions. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01890naa a2200181 a 4500
001 1943410
005 2018-02-05
008 2012 bl uuuu u00u1 u #d
100 1 $aMOUGA, D. M. D. da S.
245 $aBees and plants in a transition area between atlantic rain forest and araucaria forest in Southern Brazil.
260 $c2012
520 $aThe community of native bees from a transition area between Atlantic rain forest and Araucaria forest in Joinville, Santa Catarina state, Brazil, was studied regarding to species richness, relative abundance, floral resources and plant interactions. Observations were made monthly from 2008 to 2009, using entomological nets. 710 individuals of 88 species were sampled from the five bee subfamilies existing in Brazil. The bees were sampled on 62 plant species from 29 families. The most visited plant families were Asteraceae (48 %), Lamiaceae (10 %), Saxifragaceae (9 %) and Rosaceae (8 %). The bee subfamily with the highest species diversity was Halictinae (44 %), followed by Apinae (38 %), Andreninae (11 %), Megachilinae (8 %) and Colletinae (1 %). The subfamilies abundance sequence was: Apinae (81 %), Halictinae (12 %), Andreninae and Megachilinae (both 3 %) and Colletinae (less than 1 %). Apis mellifera L. was the most abundant species (42 %), followed by Trigona spinipes (Fabricius) (14 %) and Plebeia sp. This study depicts a system with asymmetric interactions shown by the species grouping, with a predominance of general relationships, revealing the relative importance of abundance for mutual networks nesting. The results from the network metrics evaluated reveal a robust and diverse web, in a recurrent feature of biodiversity structuring.
653 $aBees
653 $aInteractions
700 1 $aNOBLE, C. F.
700 1 $aBUSSMANN, D. B. G.
700 1 $aKRUG, C.
773 $tRevue D'Écologie, France$gv. 67, p. 313-327, 2012.
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Registro original: |
Embrapa Amazônia Ocidental (CPAA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
14/12/2020 |
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Data da última atualização: |
14/12/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
BISPO, P. da C.; RODRÍGUEZ-VEIGA, P.; ZIMBRES, B.; MIRANDA, S. do C. de; CEZARE, C. H. G.; FLEMING, S.; BALDACCHINO, F.; LOUIS, V.; RAINS, D.; GARCIA, M.; ESPIRITO-SANTO, F. D. B.; ROITMAN, I.; PACHECO-PASCAGAZA, A. M.; GOU, Y.; ROBERTS, J.; BARRETT, K.; FERREIRA, L. G.; SHIMBO, J. Z.; ALENCAR, A.; BUSTAMANTE, M.; WOODHOUSE, I. H.; SANO, E. E.; OMETTO, J. P.; TANSEY, K.; BALZTER, H. |
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Afiliação: |
EDSON EYJI SANO, CPAC. |
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Título: |
Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing, v. 12, n. 17, 2020. |
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Idioma: |
Português |
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Conteúdo: |
The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1. View Full-Text |
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Thesagro: |
Biomassa; Carbono; Cerrado; Sensoriamento Remoto. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/219145/1/SANO-WOODY-ABOVEGROUND-BIOMASS-MAPPING-OF-THE-BRAZILIAN-SAVANNA.pdf
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Marc: |
LEADER 02683naa a2200457 a 4500
001 2128070
005 2020-12-14
008 2020 bl uuuu u00u1 u #d
100 1 $aBISPO, P. da C.
245 $aWoody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach.$h[electronic resource]
260 $c2020
520 $aThe tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1. View Full-Text
650 $aBiomassa
650 $aCarbono
650 $aCerrado
650 $aSensoriamento Remoto
700 1 $aRODRÍGUEZ-VEIGA, P.
700 1 $aZIMBRES, B.
700 1 $aMIRANDA, S. do C. de
700 1 $aCEZARE, C. H. G.
700 1 $aFLEMING, S.
700 1 $aBALDACCHINO, F.
700 1 $aLOUIS, V.
700 1 $aRAINS, D.
700 1 $aGARCIA, M.
700 1 $aESPIRITO-SANTO, F. D. B.
700 1 $aROITMAN, I.
700 1 $aPACHECO-PASCAGAZA, A. M.
700 1 $aGOU, Y.
700 1 $aROBERTS, J.
700 1 $aBARRETT, K.
700 1 $aFERREIRA, L. G.
700 1 $aSHIMBO, J. Z.
700 1 $aALENCAR, A.
700 1 $aBUSTAMANTE, M.
700 1 $aWOODHOUSE, I. H.
700 1 $aSANO, E. E.
700 1 $aOMETTO, J. P.
700 1 $aTANSEY, K.
700 1 $aBALZTER, H.
773 $tRemote Sensing$gv. 12, n. 17, 2020.
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