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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; Embrapa Florestas. |
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Data corrente: |
27/09/2018 |
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Data da última atualização: |
18/01/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
BASTIN, J. F.; RUTISHAUSER, E.; KELLNER, J. R.; SAATCHI, S.; PÉLISSIER, R.; HÉRAULT, B.; SLIK, F.; BOGAERT, J.; DE CANNIÈRE, C.; MARSHALL, A. R.; POULSEN, J.; ALVAREZ-LOYAYZA, P.; ANDRADE, A.; ANGBONGA-BASIA, A.; ARAUJO-MURAKAMI, A.; ARROYO, L.; AYYAPPAN, N.; AZEVEDO, C. P. de; BANKI, O.; BARBIER, N.; BARROSO, J. G.; BEECKMAN, H.; BITARIHO, R.; BOECKX, P.; BOEHNING-GAESE, K.; BRANDÃO, H.; BREARLEY, F. Q.; HOCKEMBA, M. B. N.; BRIENEN, R.; CAMARGO, J. L. C.; CAMPOS-ARCEIZ, A.; CASSART, B.; CHAVE, J.; CHAZDON, R.; CHUYONG, G.; CLARK, D. B.; CLARK, C. J.; CONDIT, R.; CORONADO, E. N. H.; DAVIDAR, P.; HAULLEVILLE, T. de; DESCROIX, L.; DOUCET, J-L.; DOURDAIN, A.; DROISSART, V.; DUNCAN, T.; ESPEJO. J. S.; ESPINOSA, S.; FARWIG, N.; FAYOLLE, A.; FELDPAUSCH, T. R.; FERRAZ, A.; FLETCHER, C.; GAJAPERSAD, K.; GILLET, J-F.; AMARAL, I. L. do; GONMADJE, C.; GROGAN, J.; HARRIS, D.; HERZOG, S. K.; HOMEIER, J.; HUBAU, W.; HUBBELL, S. P.; HUFKENS, K.; HURTADO, J.; KAMDEM, N. G.; KEARSLEY, E.; KENFACK, D.; KESSLER, M.; LABRIÈRE, N.; LAUMONIER, Y.; LAURANCE, S.; LAURANCE, W. F.; LEWIS, S. L.; LIBALAH, M. B.; LIGOT, G.; LLOYD, J.; LOVEJOY, T. E.; MALHI, Y.; MARIMON, B. S.; JUNIOR, B. H. M.; MARTIN, E. H.; MATIUS, P.; MEYER, V.; BAUTISTA, C. M.; MONTEAGUDO-MENDOZA, A.; MTUI, A.; NEILL, D.; GUTIERREZ, G. A. P.; PARDO, G.; PARREN, M.; PARTHASARATHY, N.; PHILLIPS, O. L.; PITMAN, N. C. A.; PLOTON, P.; PONETTE, Q.; RAMESH, B. R.; RAZAFIMAHAIMODISON, J-C.; RÉJOU-MÉCHAIN, M.; ROLIM, S. G.; SALTOS, H. R.; ROSSI, L. M. B.; SPIRONELLO, W. R.; ROVERO, F.; SANER, P.; SASAKI, D.; SCHULZE, M.; SILVEIRA, M.; SINGH, J.; SIST, P.; SONKE, B.; SOTO, J. D.; SOUZA, C. R. de; STROPP, J.; SULLIVAN, M. J. P.; SWANEPOEL, B.; STEEGE, H. ter.; TERBORGH, J.; TEXIER, N.; TOMA, T.; VALENCIA, R.; VALENZUELA, L.; FERREIRA, L. V.; VALVERDE, F. C.; ANDEL, T. R. van.; VASQUE, R.; VERBEECK, H.; VIVEK, P.; VLEMINCKX, J.; VOS, V. A.; WAGNER, F. H.; WARSUDI, P. P.; WORTEL, V.; ZAGT, R. J.; ZEBAZE, D. |
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Afiliação: |
Jean-François Bastin; Ervan Rutishauser; James R. Kellner; Sassan Saatchi; Raphael Pélissier; Bruno Hérault; Ferry Slik; Jan Bogaert; Charles De Cannière; Andrew R. Marshall; John Poulsen; Patricia Alvarez-Loyayza; Ana Andrade; Albert Angbonga-Basia; Alejandro Araujo-Murakami; Luzmila Arroyo; Narayanan Ayyappan; CELSO PAULO DE AZEVEDO, CPAA; Olaf Banki; Nicolas Barbier; Jorcely G. Barroso; Hans Beeckman; Robert Bitariho; Pascal Boeckx; Katrin Boehning-Gaese; Hilandia Brandão; Francis Q. Brearley; Mireille Breuer Ndoundou Hockemba; Roel Brienen; Jose Luis C. Camargo; Ahimsa Campos-Arceiz; Benoit Cassart; Jérôme Chave; Robin Chazdon; Georges Chuyong; David B. Clark; Connie J. Clark; Richard Condit; Euridice N. Honorio Coronado; Priya Davidar; Thalès de Haulleville; Laurent Descroix; Jean?Louis Doucet; Aurelie Dourdain; Vincent Droissart; Thomas Duncan; Javier Silva Espejo; Santiago Espinosa; Nina Farwig; Adeline Fayolle; Ted R. Feldpausch; Antonio Ferraz; Christine Fletcher; Krisna Gajapersad; Jean-François Gillet; Iêda Leão do Amaral; Christelle Gonmadje; James Grogan; David Harris; Sebastian K. Herzog; Jürgen Homeier; Wannes Hubau; Stephen P. Hubbell; Koen Hufkens; Johanna Hurtado; Narcisse G. Kamdem; Elizabeth Kearsley; David Kenfack; Michael Kessler; Nicolas Labrière; Yves Laumonier; Susan Laurance; William F. Laurance; Simon L. Lewis; Moses B. Libalah; Gauthier Ligot; Jon Lloyd; Thomas E. Lovejoy; Yadvinder Malhi; Beatriz S. Marimon; Ben Hur Marimon Junior; Emmanuel H. Martin; Paulus Matius; Victoria Meyer; Casimero Mendoza Bautista; Abel Monteagudo-Mendoza; Arafat Mtui; David Neill; Germaine Alexander Parada Gutierrez; Guido Pardo; Marc Parren; N. Parthasarathy; Oliver L. Phillips; Nigel C. A. Pitman; Pierre Ploton; Quentin Ponette; B. R. Ramesh; Jean-Claude Razafimahaimodison; Maxime Réjou-Méchain; Samir Gonçalves Rolim; Hugo Romero Saltos; LUIZ MARCELO BRUM ROSSI, CNPF; Wilson Roberto Spironello; Francesco Rovero; Philippe Saner; Denise Sasaki; Mark Schulze; Marcos Silveira; James Singh; Plinio Sist; Bonaventure Sonke; J. Daniel Soto; CINTIA RODRIGUES DE SOUZA, CPAA; Juliana Stropp; Martin J. P. Sullivan; Ben Swanepoel; Hans ter Steege; John Terborgh; Nicolas Texier; Takeshi Toma; Renato Valencia; Luis Valenzuela; Leandro Valle Ferreira; Fernando Cornejo Valverde; Tinde R. Van Andel; Rodolfo Vasque; Hans Verbeeck; Pandi Vivek; Jason Vleminckx; Vincent A. Vos; Fabien H. Wagner; Papi Puspa Warsudi; Verginia Wortel; Roderick J. Zagt; Donatien Zebaze. |
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Título: |
Pan-tropical prediction of forest structure from the largest trees. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Global Ecology and Biogeography, v. 27, n. 11, p. 1366-1383, Nov. 2018. |
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DOI: |
10.1111/geb.12803 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract
Aim: Large tropical trees form the interface between ground and airborne observa?
tions, offering a unique opportunity to capture forest properties remotely and to inves?
tigate their variations on broad scales. However, despite rapid development of metrics
to characterize the forest canopy from remotely sensed data, a gap remains between
aerial and field inventories. To close this gap, we propose a new pan?tropical model to
predict plot?level forest structure properties and biomass from only the largest trees.
Location: Pan?tropical.
Time period: Early 21st century.
Major taxa studied: Woody plants.
Methods: Using a dataset of 867 plots distributed among 118 sites across the tropics,
we tested the prediction of the quadratic mean diameter, basal area, Lorey’s height,
community wood density and aboveground biomass (AGB) from the ith largest trees.
Results: Measuring the largest trees in tropical forests enables unbiased predictions of
plot? and site?level forest structure. The 20 largest trees per hectare predicted quad?
ratic mean diameter, basal area, Lorey’s height, community wood density and AGB
with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error
in biomass prediction is driven by differences in the proportion of total biomass held
in medium?sized trees (50–70 cm diameter at breast height), which shows some conti?
nental dependency, with American tropical forests presenting the highest proportion
of total biomass in these intermediate?diameter classes relative to other continents.
Main conclusions: Our approach provides new information on tropical forest struc?
ture and can be used to generate accurate field estimates of tropical forest carbon
stocks to support the calibration and validation of current and forthcoming space
missions. It will reduce the cost of field inventories and contribute to scientific under?
standing of tropical forest ecosystems and response to climate change. MenosAbstract
Aim: Large tropical trees form the interface between ground and airborne observa?
tions, offering a unique opportunity to capture forest properties remotely and to inves?
tigate their variations on broad scales. However, despite rapid development of metrics
to characterize the forest canopy from remotely sensed data, a gap remains between
aerial and field inventories. To close this gap, we propose a new pan?tropical model to
predict plot?level forest structure properties and biomass from only the largest trees.
Location: Pan?tropical.
Time period: Early 21st century.
Major taxa studied: Woody plants.
Methods: Using a dataset of 867 plots distributed among 118 sites across the tropics,
we tested the prediction of the quadratic mean diameter, basal area, Lorey’s height,
community wood density and aboveground biomass (AGB) from the ith largest trees.
Results: Measuring the largest trees in tropical forests enables unbiased predictions of
plot? and site?level forest structure. The 20 largest trees per hectare predicted quad?
ratic mean diameter, basal area, Lorey’s height, community wood density and AGB
with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error
in biomass prediction is driven by differences in the proportion of total biomass held
in medium?sized trees (50–70 cm diameter at breast height), which shows some conti?
nental dependency, with American tropical forests presenting the highest proportion
of total biomass in these int... Mostrar Tudo |
|
Palavras-Chave: |
Tropical forest ecology. |
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Thesaurus Nal: |
Carbon; Climate change. |
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Categoria do assunto: |
--
K Ciência Florestal e Produtos de Origem Vegetal |
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Marc: |
LEADER 06464naa a2201777 a 4500
001 2096481
005 2019-01-18
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1111/geb.12803$2DOI
100 1 $aBASTIN, J. F.
245 $aPan-tropical prediction of forest structure from the largest trees.$h[electronic resource]
260 $c2018
520 $aAbstract Aim: Large tropical trees form the interface between ground and airborne observa? tions, offering a unique opportunity to capture forest properties remotely and to inves? tigate their variations on broad scales. However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. To close this gap, we propose a new pan?tropical model to predict plot?level forest structure properties and biomass from only the largest trees. Location: Pan?tropical. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: Using a dataset of 867 plots distributed among 118 sites across the tropics, we tested the prediction of the quadratic mean diameter, basal area, Lorey’s height, community wood density and aboveground biomass (AGB) from the ith largest trees. Results: Measuring the largest trees in tropical forests enables unbiased predictions of plot? and site?level forest structure. The 20 largest trees per hectare predicted quad? ratic mean diameter, basal area, Lorey’s height, community wood density and AGB with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error in biomass prediction is driven by differences in the proportion of total biomass held in medium?sized trees (50–70 cm diameter at breast height), which shows some conti? nental dependency, with American tropical forests presenting the highest proportion of total biomass in these intermediate?diameter classes relative to other continents. Main conclusions: Our approach provides new information on tropical forest struc? ture and can be used to generate accurate field estimates of tropical forest carbon stocks to support the calibration and validation of current and forthcoming space missions. It will reduce the cost of field inventories and contribute to scientific under? standing of tropical forest ecosystems and response to climate change.
650 $aCarbon
650 $aClimate change
653 $aTropical forest ecology
700 1 $aRUTISHAUSER, E.
700 1 $aKELLNER, J. R.
700 1 $aSAATCHI, S.
700 1 $aPÉLISSIER, R.
700 1 $aHÉRAULT, B.
700 1 $aSLIK, F.
700 1 $aBOGAERT, J.
700 1 $aDE CANNIÈRE, C.
700 1 $aMARSHALL, A. R.
700 1 $aPOULSEN, J.
700 1 $aALVAREZ-LOYAYZA, P.
700 1 $aANDRADE, A.
700 1 $aANGBONGA-BASIA, A.
700 1 $aARAUJO-MURAKAMI, A.
700 1 $aARROYO, L.
700 1 $aAYYAPPAN, N.
700 1 $aAZEVEDO, C. P. de
700 1 $aBANKI, O.
700 1 $aBARBIER, N.
700 1 $aBARROSO, J. G.
700 1 $aBEECKMAN, H.
700 1 $aBITARIHO, R.
700 1 $aBOECKX, P.
700 1 $aBOEHNING-GAESE, K.
700 1 $aBRANDÃO, H.
700 1 $aBREARLEY, F. Q.
700 1 $aHOCKEMBA, M. B. N.
700 1 $aBRIENEN, R.
700 1 $aCAMARGO, J. L. C.
700 1 $aCAMPOS-ARCEIZ, A.
700 1 $aCASSART, B.
700 1 $aCHAVE, J.
700 1 $aCHAZDON, R.
700 1 $aCHUYONG, G.
700 1 $aCLARK, D. B.
700 1 $aCLARK, C. J.
700 1 $aCONDIT, R.
700 1 $aCORONADO, E. N. H.
700 1 $aDAVIDAR, P.
700 1 $aHAULLEVILLE, T. de
700 1 $aDESCROIX, L.
700 1 $aDOUCET, J-L.
700 1 $aDOURDAIN, A.
700 1 $aDROISSART, V.
700 1 $aDUNCAN, T.
700 1 $aESPEJO. J. S.
700 1 $aESPINOSA, S.
700 1 $aFARWIG, N.
700 1 $aFAYOLLE, A.
700 1 $aFELDPAUSCH, T. R.
700 1 $aFERRAZ, A.
700 1 $aFLETCHER, C.
700 1 $aGAJAPERSAD, K.
700 1 $aGILLET, J-F.
700 1 $aAMARAL, I. L. do
700 1 $aGONMADJE, C.
700 1 $aGROGAN, J.
700 1 $aHARRIS, D.
700 1 $aHERZOG, S. K.
700 1 $aHOMEIER, J.
700 1 $aHUBAU, W.
700 1 $aHUBBELL, S. P.
700 1 $aHUFKENS, K.
700 1 $aHURTADO, J.
700 1 $aKAMDEM, N. G.
700 1 $aKEARSLEY, E.
700 1 $aKENFACK, D.
700 1 $aKESSLER, M.
700 1 $aLABRIÈRE, N.
700 1 $aLAUMONIER, Y.
700 1 $aLAURANCE, S.
700 1 $aLAURANCE, W. F.
700 1 $aLEWIS, S. L.
700 1 $aLIBALAH, M. B.
700 1 $aLIGOT, G.
700 1 $aLLOYD, J.
700 1 $aLOVEJOY, T. E.
700 1 $aMALHI, Y.
700 1 $aMARIMON, B. S.
700 1 $aJUNIOR, B. H. M.
700 1 $aMARTIN, E. H.
700 1 $aMATIUS, P.
700 1 $aMEYER, V.
700 1 $aBAUTISTA, C. M.
700 1 $aMONTEAGUDO-MENDOZA, A.
700 1 $aMTUI, A.
700 1 $aNEILL, D.
700 1 $aGUTIERREZ, G. A. P.
700 1 $aPARDO, G.
700 1 $aPARREN, M.
700 1 $aPARTHASARATHY, N.
700 1 $aPHILLIPS, O. L.
700 1 $aPITMAN, N. C. A.
700 1 $aPLOTON, P.
700 1 $aPONETTE, Q.
700 1 $aRAMESH, B. R.
700 1 $aRAZAFIMAHAIMODISON, J-C.
700 1 $aRÉJOU-MÉCHAIN, M.
700 1 $aROLIM, S. G.
700 1 $aSALTOS, H. R.
700 1 $aROSSI, L. M. B.
700 1 $aSPIRONELLO, W. R.
700 1 $aROVERO, F.
700 1 $aSANER, P.
700 1 $aSASAKI, D.
700 1 $aSCHULZE, M.
700 1 $aSILVEIRA, M.
700 1 $aSINGH, J.
700 1 $aSIST, P.
700 1 $aSONKE, B.
700 1 $aSOTO, J. D.
700 1 $aSOUZA, C. R. de
700 1 $aSTROPP, J.
700 1 $aSULLIVAN, M. J. P.
700 1 $aSWANEPOEL, B.
700 1 $aSTEEGE, H. ter.
700 1 $aTERBORGH, J.
700 1 $aTEXIER, N.
700 1 $aTOMA, T.
700 1 $aVALENCIA, R.
700 1 $aVALENZUELA, L.
700 1 $aFERREIRA, L. V.
700 1 $aVALVERDE, F. C.
700 1 $aANDEL, T. R. van.
700 1 $aVASQUE, R.
700 1 $aVERBEECK, H.
700 1 $aVIVEK, P.
700 1 $aVLEMINCKX, J.
700 1 $aVOS, V. A.
700 1 $aWAGNER, F. H.
700 1 $aWARSUDI, P. P.
700 1 $aWORTEL, V.
700 1 $aZAGT, R. J.
700 1 $aZEBAZE, D.
773 $tGlobal Ecology and Biogeography$gv. 27, n. 11, p. 1366-1383, Nov. 2018.
Download
Esconder MarcMostrar Marc Completo |
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Registro original: |
Embrapa Amazônia Ocidental (CPAA) |
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Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Recursos Genéticos e Biotecnologia; Embrapa Unidades Centrais. |
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Data corrente: |
15/08/2018 |
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Data da última atualização: |
23/06/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
PAIM, T. do P.; IANELLA, P.; PAIVA, S. R.; CAETANO, A. R.; PIMENTAL, C. M. Mc. |
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Afiliação: |
Tiago do Prado Paim, Instituto Federal Goiano/Campus Iporá; PATRICIA IANELLA, Cenargen; SAMUEL REZENDE PAIVA, Cenargen; ALEXANDRE RODRIGUES CAETANO, Cenargen; Concepta Margaret McManus Pimentel, Universidade de Brasília - Unb/Instituto de Biologia. |
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Título: |
Detection and evaluation of selection signatures in sheep. |
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Ano de publicação: |
2018 |
|
Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasília, DF, v. 53, n. 5, p.527-539, maio 2018 |
|
Idioma: |
Inglês |
|
Notas: |
Título em português: Detecção e avaliação de assinaturas de seleção em ovinos. |
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Conteúdo: |
The recent development of genome-wide single nucleotide polymorphism (SNP) arrays made it possible to carry out several studies with different species. The selection process can increase or reduce allelic (or genic) frequencies at specific loci in the genome, besides dragging neighboring alleles in the chromosome. This way, genomic regions with increased frequencies of specific alleles are formed, caracterizing selection signatures or selective sweeps. The detection of these signatures is important to characterize genetic resources, as well as to identify genes or regions involved in the control and expression of important production and economic traits. Sheep are an important species for theses studies as they are dispersed worldwide and have great phenotypic diversity. Due to the large amounts of genomic data generated, specific statistical methods and softwares are necessary for the detection of selection signatures. Therefore, the objectives of this review are to address the main statistical methods and softwares currently used for the analysis of genomic data and the identification of selection signatures; to describe the results of recent works published on selection signatures in sheep; and to discuss some challenges and opportunities in this research field. |
|
Palavras-Chave: |
Analysis software; SNP markers. |
|
Thesagro: |
Genética Molecular; Genoma; Melhoramento Genético Animal; Ovis Aries. |
|
Thesaurus NAL: |
Animal genetic resources; Genetic improvement; Genomics; Molecular genetics; Sheep. |
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Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/181463/1/Detection-and-evaluation-of-selection.pdf
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Marc: |
LEADER 02254naa a2200313 a 4500
001 2094407
005 2022-06-23
008 2018 bl uuuu u00u1 u #d
100 1 $aPAIM, T. do P.
245 $aDetection and evaluation of selection signatures in sheep.$h[electronic resource]
260 $c2018
500 $aTítulo em português: Detecção e avaliação de assinaturas de seleção em ovinos.
520 $aThe recent development of genome-wide single nucleotide polymorphism (SNP) arrays made it possible to carry out several studies with different species. The selection process can increase or reduce allelic (or genic) frequencies at specific loci in the genome, besides dragging neighboring alleles in the chromosome. This way, genomic regions with increased frequencies of specific alleles are formed, caracterizing selection signatures or selective sweeps. The detection of these signatures is important to characterize genetic resources, as well as to identify genes or regions involved in the control and expression of important production and economic traits. Sheep are an important species for theses studies as they are dispersed worldwide and have great phenotypic diversity. Due to the large amounts of genomic data generated, specific statistical methods and softwares are necessary for the detection of selection signatures. Therefore, the objectives of this review are to address the main statistical methods and softwares currently used for the analysis of genomic data and the identification of selection signatures; to describe the results of recent works published on selection signatures in sheep; and to discuss some challenges and opportunities in this research field.
650 $aAnimal genetic resources
650 $aGenetic improvement
650 $aGenomics
650 $aMolecular genetics
650 $aSheep
650 $aGenética Molecular
650 $aGenoma
650 $aMelhoramento Genético Animal
650 $aOvis Aries
653 $aAnalysis software
653 $aSNP markers
700 1 $aIANELLA, P.
700 1 $aPAIVA, S. R.
700 1 $aCAETANO, A. R.
700 1 $aPIMENTAL, C. M. Mc.
773 $tPesquisa Agropecuária Brasileira, Brasília, DF$gv. 53, n. 5, p.527-539, maio 2018
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Embrapa Recursos Genéticos e Biotecnologia (CENARGEN) |
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