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Registro Completo |
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Biblioteca(s): |
Embrapa Recursos Genéticos e Biotecnologia; Embrapa Roraima. |
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Data corrente: |
10/07/2023 |
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Data da última atualização: |
10/07/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
AMARO, G. C.; FIDELIS, E. G.; SILVA, R. S. da; MARCHIORO, C. A. |
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Afiliação: |
GEORGE CORREA AMARO, CPAF-RR; ELISANGELA GOMES FIDELIS, Cenargen; RICARDO SIQUEIRA DA SILVA, UNIVERSIDADE FEDERAL DOS VALES DO JEQUITINHONHA E MUCURI; CESAR AUGUSTO MARCHIORO, UNIVERSIDADE FEDERAL DE SANTA CATARINA. |
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Título: |
Effect of study area extent on the potential distribution of Species: A case study with models for Raoiella indica Hirst (Acari: Tenuipalpidae). |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Ecological Modelling, v. 483, 2023. |
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Idioma: |
Inglês |
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Conteúdo: |
Ecological niche models are used to quantify the relationships between known occurrence records of a given species and environmental variables at these locations. Maxent is among the most widely used algorithms for modeling species distribution and has demonstrated better performance compared to other methods. However, the extent of the study area is a critical issue in the development of presence-only species distribution models because it encompasses the region used to extract the background points employed to characterize the envi- ronments accessible to the species. Thus, this study evaluated the effect of the extension of the study area on the species distribution modeling with the Maxent algorithm and occurrence data from the invasive species Raoiella indica Hirst (Acari: Tenuipalpidae). The increase in the study area extent inflated most of the threshold- dependent and -independent metrics used to assess model performance. The selection of the study area also affected the predicted suitable areas for the species (its potential distribution). The analysis shows that models developed with smaller study areas resulted in model overfitting and an increase in false-negative predictions. The extent of the area used during model training has a strong influence on the model outputs, with significant consequences for predicting the potential distribution of invasive species and thus for the areas under risk of invasion. |
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Palavras-Chave: |
Ecological niche; Modeling process; Species distribution models; Study area extent. |
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Thesaurus Nal: |
invasive species. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1154855/1/1-s2.0-S0304380023001850-main.pdf
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Marc: |
LEADER 02139naa a2200217 a 4500
001 2154855
005 2023-07-10
008 2023 bl uuuu u00u1 u #d
100 1 $aAMARO, G. C.
245 $aEffect of study area extent on the potential distribution of Species$bA case study with models for Raoiella indica Hirst (Acari: Tenuipalpidae).$h[electronic resource]
260 $c2023
520 $aEcological niche models are used to quantify the relationships between known occurrence records of a given species and environmental variables at these locations. Maxent is among the most widely used algorithms for modeling species distribution and has demonstrated better performance compared to other methods. However, the extent of the study area is a critical issue in the development of presence-only species distribution models because it encompasses the region used to extract the background points employed to characterize the envi- ronments accessible to the species. Thus, this study evaluated the effect of the extension of the study area on the species distribution modeling with the Maxent algorithm and occurrence data from the invasive species Raoiella indica Hirst (Acari: Tenuipalpidae). The increase in the study area extent inflated most of the threshold- dependent and -independent metrics used to assess model performance. The selection of the study area also affected the predicted suitable areas for the species (its potential distribution). The analysis shows that models developed with smaller study areas resulted in model overfitting and an increase in false-negative predictions. The extent of the area used during model training has a strong influence on the model outputs, with significant consequences for predicting the potential distribution of invasive species and thus for the areas under risk of invasion.
650 $ainvasive species
653 $aEcological niche
653 $aModeling process
653 $aSpecies distribution models
653 $aStudy area extent
700 1 $aFIDELIS, E. G.
700 1 $aSILVA, R. S. da
700 1 $aMARCHIORO, C. A.
773 $tEcological Modelling$gv. 483, 2023.
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Registro original: |
Embrapa Roraima (CPAF-RR) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Corte. Para informações adicionais entre em contato com cnpgc.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Corte. |
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Data corrente: |
26/02/2013 |
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Data da última atualização: |
26/02/2013 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
CHIARI, L.; JANK, L. |
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Afiliação: |
LUCIMARA CHIARI, CNPGC; LIANA JANK, CNPGC. |
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Título: |
Gene discovery by functional genomics approaches in forage plants. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
In: JORNADAS LATINOAMERICANAS DE RECURSOS GENÉTICOS, MEJORAMIENTO Y BIOTECNOLOGÍA DE ESPECIES FORRAJERAS, 2012, Buenos Aires. [Anales...]. Junin : Universidad Nacional del Noroeste de la Provincia de Buenos Aires. UNNOBA, 2012. |
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Páginas: |
p.42-47 |
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Idioma: |
Inglês |
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Conteúdo: |
The knowledge about the nature and content of genetic information, as well as the available technologies for DNA high-throughput sequencing (Next Generation Sequence - NGS) evolved rapidly in the last two decades. This enabled the accumulation of a large quantity of information about the nucleotide sequences of various genomes (microorganisms, plants and animals) and much of this information is available in public data banks. However, the data obtained in the sequencing projects did not contribute as expected to the understanding of the role of the genes in the function of the organisms, since the genome is a virtually static element, whereas its products, messenger RNAs and proteins, have a dynamic character which is characterized by continuous changes in response to internal and external stimuli (Greenbaum et al., 2001). In this context, the Functional Genomics emerged with the objective of knowing the individual functions of the genes and its products, RNAs (transcriptome) and proteins (proteome). This new area of research uses analytical techniques that allow to evaluate the patterns of genic and proteic expressions in cells and tissues, a basic prerequisite to understand how these macromolecules interact dynamically to produce complex organisms which are able to adapt to environmental influences and to specific metabolic-physiological situations. The results of research in Functional Genomics, especially those involving genetically different individuals or those subjected to contrasting conditions, have permitted a faster and more accurate understanding of gene-phenotype relationships, and dissect the genotype-environment interactions that cause significant changes in the phenotype of individuals (FURLAN et al., 2007). MenosThe knowledge about the nature and content of genetic information, as well as the available technologies for DNA high-throughput sequencing (Next Generation Sequence - NGS) evolved rapidly in the last two decades. This enabled the accumulation of a large quantity of information about the nucleotide sequences of various genomes (microorganisms, plants and animals) and much of this information is available in public data banks. However, the data obtained in the sequencing projects did not contribute as expected to the understanding of the role of the genes in the function of the organisms, since the genome is a virtually static element, whereas its products, messenger RNAs and proteins, have a dynamic character which is characterized by continuous changes in response to internal and external stimuli (Greenbaum et al., 2001). In this context, the Functional Genomics emerged with the objective of knowing the individual functions of the genes and its products, RNAs (transcriptome) and proteins (proteome). This new area of research uses analytical techniques that allow to evaluate the patterns of genic and proteic expressions in cells and tissues, a basic prerequisite to understand how these macromolecules interact dynamically to produce complex organisms which are able to adapt to environmental influences and to specific metabolic-physiological situations. The results of research in Functional Genomics, especially those involving genetically different individuals or those subject... Mostrar Tudo |
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Thesagro: |
Melhoramento Genético Vegetal; Pastagem. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02391nam a2200157 a 4500
001 1951300
005 2013-02-26
008 2012 bl uuuu u00u1 u #d
100 1 $aCHIARI, L.
245 $aGene discovery by functional genomics approaches in forage plants.$h[electronic resource]
260 $aIn: JORNADAS LATINOAMERICANAS DE RECURSOS GENÉTICOS, MEJORAMIENTO Y BIOTECNOLOGÍA DE ESPECIES FORRAJERAS, 2012, Buenos Aires. [Anales...]. Junin : Universidad Nacional del Noroeste de la Provincia de Buenos Aires. UNNOBA$c2012
300 $ap.42-47
520 $aThe knowledge about the nature and content of genetic information, as well as the available technologies for DNA high-throughput sequencing (Next Generation Sequence - NGS) evolved rapidly in the last two decades. This enabled the accumulation of a large quantity of information about the nucleotide sequences of various genomes (microorganisms, plants and animals) and much of this information is available in public data banks. However, the data obtained in the sequencing projects did not contribute as expected to the understanding of the role of the genes in the function of the organisms, since the genome is a virtually static element, whereas its products, messenger RNAs and proteins, have a dynamic character which is characterized by continuous changes in response to internal and external stimuli (Greenbaum et al., 2001). In this context, the Functional Genomics emerged with the objective of knowing the individual functions of the genes and its products, RNAs (transcriptome) and proteins (proteome). This new area of research uses analytical techniques that allow to evaluate the patterns of genic and proteic expressions in cells and tissues, a basic prerequisite to understand how these macromolecules interact dynamically to produce complex organisms which are able to adapt to environmental influences and to specific metabolic-physiological situations. The results of research in Functional Genomics, especially those involving genetically different individuals or those subjected to contrasting conditions, have permitted a faster and more accurate understanding of gene-phenotype relationships, and dissect the genotype-environment interactions that cause significant changes in the phenotype of individuals (FURLAN et al., 2007).
650 $aMelhoramento Genético Vegetal
650 $aPastagem
700 1 $aJANK, L.
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Embrapa Gado de Corte (CNPGC) |
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