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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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Embrapa Roraima (CPAF-RR) |
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