Predicting the geographical distribution of Glycaspis brimblecombei (Hemiptera: Psylloidea) in Brazil
The red gum lerp psyllid, Glycaspis brimblecombei Moore, has been introduced from Australia into several countries, mostly in the Americas and Europe. It was first discovered in Brazil in 2003 and has continued to spread there. Today, the species is a major pest in eucalypt plantations and often req...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
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Blackwell Publishing Asia
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/42608 |
| _version_ | 1848756467285360640 |
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| author | de Queiroz, D. Majer, Jonathan Burckhardt, D. Zanetti, R. Fernandez, J. de Queiroz, E. Garrastazu, M. Fernandes, B. dos Anjos, N. |
| author_facet | de Queiroz, D. Majer, Jonathan Burckhardt, D. Zanetti, R. Fernandez, J. de Queiroz, E. Garrastazu, M. Fernandes, B. dos Anjos, N. |
| author_sort | de Queiroz, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The red gum lerp psyllid, Glycaspis brimblecombei Moore, has been introduced from Australia into several countries, mostly in the Americas and Europe. It was first discovered in Brazil in 2003 and has continued to spread there. Today, the species is a major pest in eucalypt plantations and often requires expensive control measures. Ecological modelling is used here to estimate the potential spread of G. brimblecombeii, worldwide and particularly in Brazil, based on environmental variables from 502 records of G. brimblecombei around the world. Distribution data from Australia were obtained from the literature and recent field surveys. For the first time, G. brimblecombei is recorded from Western Australia. Through the Openmodeller® program interface, 22 bioclimatic variables were used to test the efficacy of the following models: BIOCLIM, Climate Space Model, Envelope Score, Environmental Distance, GARP with best subsets (new Openmodeller implementation), GARP new Openmodeller implementation, GARP best subsets (Desktop GARP implementation), Niche Mosaic and Support Vector Machines. Among these models, Environmental Distance was the best predictor for the potential distribution of G. brimblecombei in new regions. Temperate areas appeared to be more favourable for G. brimblecombei. Regions with highest probability of occurrence in Brazil, in hierarchical order are: the southern part of the Atlantic Forest, Pampa, Caatinga and Cerrado. This modelling procedure provides a useful tool that should be incorporated in future strategies for pest management in eucalypt plantations. |
| first_indexed | 2025-11-14T09:12:40Z |
| format | Journal Article |
| id | curtin-20.500.11937-42608 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:12:40Z |
| publishDate | 2013 |
| publisher | Blackwell Publishing Asia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-426082017-09-13T16:01:42Z Predicting the geographical distribution of Glycaspis brimblecombei (Hemiptera: Psylloidea) in Brazil de Queiroz, D. Majer, Jonathan Burckhardt, D. Zanetti, R. Fernandez, J. de Queiroz, E. Garrastazu, M. Fernandes, B. dos Anjos, N. eucalypt distribution red gum lerp psyllid ecological modelling The red gum lerp psyllid, Glycaspis brimblecombei Moore, has been introduced from Australia into several countries, mostly in the Americas and Europe. It was first discovered in Brazil in 2003 and has continued to spread there. Today, the species is a major pest in eucalypt plantations and often requires expensive control measures. Ecological modelling is used here to estimate the potential spread of G. brimblecombeii, worldwide and particularly in Brazil, based on environmental variables from 502 records of G. brimblecombei around the world. Distribution data from Australia were obtained from the literature and recent field surveys. For the first time, G. brimblecombei is recorded from Western Australia. Through the Openmodeller® program interface, 22 bioclimatic variables were used to test the efficacy of the following models: BIOCLIM, Climate Space Model, Envelope Score, Environmental Distance, GARP with best subsets (new Openmodeller implementation), GARP new Openmodeller implementation, GARP best subsets (Desktop GARP implementation), Niche Mosaic and Support Vector Machines. Among these models, Environmental Distance was the best predictor for the potential distribution of G. brimblecombei in new regions. Temperate areas appeared to be more favourable for G. brimblecombei. Regions with highest probability of occurrence in Brazil, in hierarchical order are: the southern part of the Atlantic Forest, Pampa, Caatinga and Cerrado. This modelling procedure provides a useful tool that should be incorporated in future strategies for pest management in eucalypt plantations. 2013 Journal Article http://hdl.handle.net/20.500.11937/42608 10.1111/aen.12001 Blackwell Publishing Asia unknown |
| spellingShingle | eucalypt distribution red gum lerp psyllid ecological modelling de Queiroz, D. Majer, Jonathan Burckhardt, D. Zanetti, R. Fernandez, J. de Queiroz, E. Garrastazu, M. Fernandes, B. dos Anjos, N. Predicting the geographical distribution of Glycaspis brimblecombei (Hemiptera: Psylloidea) in Brazil |
| title | Predicting the geographical distribution of Glycaspis brimblecombei (Hemiptera: Psylloidea) in Brazil |
| title_full | Predicting the geographical distribution of Glycaspis brimblecombei (Hemiptera: Psylloidea) in Brazil |
| title_fullStr | Predicting the geographical distribution of Glycaspis brimblecombei (Hemiptera: Psylloidea) in Brazil |
| title_full_unstemmed | Predicting the geographical distribution of Glycaspis brimblecombei (Hemiptera: Psylloidea) in Brazil |
| title_short | Predicting the geographical distribution of Glycaspis brimblecombei (Hemiptera: Psylloidea) in Brazil |
| title_sort | predicting the geographical distribution of glycaspis brimblecombei (hemiptera: psylloidea) in brazil |
| topic | eucalypt distribution red gum lerp psyllid ecological modelling |
| url | http://hdl.handle.net/20.500.11937/42608 |