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| 94. |  | INOUE-NAGATA, A. K.; GIORDANO, L. B.; FONSECA, M. E. N.; RIBEIRO, S. G.; ÁVILA, A. C. de; ALBUQUERQUE, L. C.; BOITEUX, L. S. Occurrence of begomovirus in tomato and other plant species in Central Brazil. INTERNATIONAL GERMINIVIRUS SYMPOSIUM, 4.; INTERNATIONAL SSDNA COMPARATIVE VIROLOGY WORKSHOP, 2., 2004, Cape Town. Programme & abstracts... Cape Town: University of Cape Town Graduate School of Business, 2004. p. 77| Biblioteca(s): Embrapa Hortaliças. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Acre. Para informações adicionais entre em contato com cpafac.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Acre. |
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Data corrente: |
28/02/2012 |
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Data da última atualização: |
06/07/2021 |
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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: |
GUARINO, E. de S. G.; BARBOSA, A. M.; WAECHTER, J. L. |
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Afiliação: |
ERNESTINO DE SOUZA GOMES GUARINO, CPAF-AC; ANA MÁRCIA BARBOSA, UNIVERSIDADE DE ÉVORA; JORGE LUIZ WAECHTER, IMPERIAL COLLEGE LONDON. |
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Título: |
Occurrence and abundance models of threatened plant species: applications to mitigate the impact of hydroelectric power dams. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Ecological Modelling, Maryland, v. 230, p. 22-33, Apr. 2012. |
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ISSN: |
0304-3800 |
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DOI: |
10.1016/j.ecolmodel.2012.01.007 |
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Idioma: |
Inglês |
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Conteúdo: |
Species occurrence and abundance models are important tools that can be used in biodiversity conservation, and can be applied to predict or plan actions needed to mitigate the environmental impacts of hydropower dams. In this study our objectives were: (i) to model the occurrence and abundance of threatened plant species, (ii) to verify the relationship between predicted occurrence and true abundance, and (iii) to assess whether models based on abundance are more effective in predicting species occurrence than those based on presence?absence data. Individual representatives of nine species were counted within 388 randomly georeferenced plots (10 m × 50 m) around the Barra Grande hydropower dam reservoir in southern Brazil. We modelled their relationship with 15 environmental variables using both occurrence (Generalised Linear Models) and abundance data (Hurdle and Zero-Inflated models). Overall, occurrence models were more accurate than abundance models. For all species, observed abundance was significantly, although not strongly, correlated with the probability of occurrence. This correlation lost significance when zero-abundance (absence) sites were excluded from analysis, but only when this entailed a substantial drop in sample size. The same occurred when analysing relationships between abundance and probability of occurrence from previously published studies on a range of different species, suggesting that future studies could potentially use probability of occurrence as an approximate indicator of abundance when the latter is not possible to obtain. This possibility might, however, depend on life history traits of the species in question, with some traits favouring a relationship between occurrence and abundance. Reconstructing species abundance patterns from occurrence could be an important tool for conservation planning and the management of threatened species, allowing scientists to indicate the best areas for collection and reintroduction of plant germplasm or choose conservation areas most likely to maintain viable populations. MenosSpecies occurrence and abundance models are important tools that can be used in biodiversity conservation, and can be applied to predict or plan actions needed to mitigate the environmental impacts of hydropower dams. In this study our objectives were: (i) to model the occurrence and abundance of threatened plant species, (ii) to verify the relationship between predicted occurrence and true abundance, and (iii) to assess whether models based on abundance are more effective in predicting species occurrence than those based on presence?absence data. Individual representatives of nine species were counted within 388 randomly georeferenced plots (10 m × 50 m) around the Barra Grande hydropower dam reservoir in southern Brazil. We modelled their relationship with 15 environmental variables using both occurrence (Generalised Linear Models) and abundance data (Hurdle and Zero-Inflated models). Overall, occurrence models were more accurate than abundance models. For all species, observed abundance was significantly, although not strongly, correlated with the probability of occurrence. This correlation lost significance when zero-abundance (absence) sites were excluded from analysis, but only when this entailed a substantial drop in sample size. The same occurred when analysing relationships between abundance and probability of occurrence from previously published studies on a range of different species, suggesting that future studies could potentially use probability of occurrence a... Mostrar Tudo |
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Palavras-Chave: |
Análisis estadístico; Barra Grande (RS); Biodiversidad; Energía hidroeléctrica; Madera tropical; Modelo de ocorrência e abundância; Pelotas (RS); Presence-absence model; Protección ambiental; Rio Grande do Sul. |
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Thesagro: |
Análise estatística; Biodiversidade; Essência florestal; Hidrelétrica; Impacto ambiental; Proteção ambiental. |
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Thesaurus NAL: |
Biodiversity; Environmental impact; Environmental protection; Hydroelectric power; Statistical analysis; Tropical wood. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
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008 2012 bl uuuu u00u1 u #d
022 $a0304-3800
024 7 $a10.1016/j.ecolmodel.2012.01.007$2DOI
100 1 $aGUARINO, E. de S. G.
245 $aOccurrence and abundance models of threatened plant species$bapplications to mitigate the impact of hydroelectric power dams.$h[electronic resource]
260 $c2012
520 $aSpecies occurrence and abundance models are important tools that can be used in biodiversity conservation, and can be applied to predict or plan actions needed to mitigate the environmental impacts of hydropower dams. In this study our objectives were: (i) to model the occurrence and abundance of threatened plant species, (ii) to verify the relationship between predicted occurrence and true abundance, and (iii) to assess whether models based on abundance are more effective in predicting species occurrence than those based on presence?absence data. Individual representatives of nine species were counted within 388 randomly georeferenced plots (10 m × 50 m) around the Barra Grande hydropower dam reservoir in southern Brazil. We modelled their relationship with 15 environmental variables using both occurrence (Generalised Linear Models) and abundance data (Hurdle and Zero-Inflated models). Overall, occurrence models were more accurate than abundance models. For all species, observed abundance was significantly, although not strongly, correlated with the probability of occurrence. This correlation lost significance when zero-abundance (absence) sites were excluded from analysis, but only when this entailed a substantial drop in sample size. The same occurred when analysing relationships between abundance and probability of occurrence from previously published studies on a range of different species, suggesting that future studies could potentially use probability of occurrence as an approximate indicator of abundance when the latter is not possible to obtain. This possibility might, however, depend on life history traits of the species in question, with some traits favouring a relationship between occurrence and abundance. Reconstructing species abundance patterns from occurrence could be an important tool for conservation planning and the management of threatened species, allowing scientists to indicate the best areas for collection and reintroduction of plant germplasm or choose conservation areas most likely to maintain viable populations.
650 $aBiodiversity
650 $aEnvironmental impact
650 $aEnvironmental protection
650 $aHydroelectric power
650 $aStatistical analysis
650 $aTropical wood
650 $aAnálise estatística
650 $aBiodiversidade
650 $aEssência florestal
650 $aHidrelétrica
650 $aImpacto ambiental
650 $aProteção ambiental
653 $aAnálisis estadístico
653 $aBarra Grande (RS)
653 $aBiodiversidad
653 $aEnergía hidroeléctrica
653 $aMadera tropical
653 $aModelo de ocorrência e abundância
653 $aPelotas (RS)
653 $aPresence-absence model
653 $aProtección ambiental
653 $aRio Grande do Sul
700 1 $aBARBOSA, A. M.
700 1 $aWAECHTER, J. L.
773 $tEcological Modelling, Maryland$gv. 230, p. 22-33, Apr. 2012.
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