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dc.contributor.author
Lantschner, María Victoria  
dc.contributor.author
de la Vega, Gerardo José  
dc.contributor.author
Corley, Juan Carlos  
dc.date.available
2020-02-10T21:44:38Z  
dc.date.issued
2018-11  
dc.identifier.citation
Lantschner, María Victoria; de la Vega, Gerardo José; Corley, Juan Carlos; Predicting the distribution of harmful species and their natural enemies in agricultural, livestock and forestry systems: An overview; Taylor & Francis; International Journal of Pest Management; 65; 3; 11-2018; 190-206  
dc.identifier.issn
0967-0874  
dc.identifier.uri
http://hdl.handle.net/11336/97140  
dc.description.abstract
Predicting the potential distribution of harmful species to agriculture, livestock and forestry is decisive to prevent their impacts, especially when these are expanding their range due to global change. Recent advances in species distribution modelling (SDM) have made these tools widely used for biosecurity studies. We reviewed the available literature of SDM for pest, weeds, pathogen species and biological-control agents, with the aims of synthesizing and quantifying the available information, and identifying gaps in the knowledge and future perspectives. SDMs for 420 species were collected from 220 publications. Insect pests were the most frequently studied organisms. CLIMEX and MaxEnt were the most commonly used modelling tools, while pure mechanistic approaches were rarely applied. Most studies covered broad scales, and focused on predicting the distribution of invasive species and/or the effects of climate change. The challenge remains for models to include disturbance, resource availability, and biotic factors, as well as to better quantify uncertainty. This future directions will be fundamental to improve the predictive power of SDMs for productive systems in the context of a rapidly changing World.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Taylor & Francis  
dc.relation
http://hdl.handle.net/11336/184935  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
PATHOGENS  
dc.subject
PESTS  
dc.subject
SPECIES DISTRIBUTION MODELS (SDMS)  
dc.subject
WEEDS  
dc.subject.classification
Ecología  
dc.subject.classification
Ciencias Biológicas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Predicting the distribution of harmful species and their natural enemies in agricultural, livestock and forestry systems: An overview  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2019-10-15T17:56:31Z  
dc.identifier.eissn
1366-5863  
dc.journal.volume
65  
dc.journal.number
3  
dc.journal.pagination
190-206  
dc.journal.pais
Reino Unido  
dc.journal.ciudad
Londres  
dc.description.fil
Fil: Lantschner, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina  
dc.description.fil
Fil: de la Vega, Gerardo José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina  
dc.description.fil
Fil: Corley, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina. Universidad Nacional del Comahue; Argentina  
dc.journal.title
International Journal of Pest Management  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/09670874.2018.1533664  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1080/09670874.2018.1533664