Assessing the Potential Distribution of Insect Pests under Current and Future Climatic Conditions in European Forests Using Host Data. Barredo, J. I.; Strona, G.; de Rigo, D.; Caudullo, G.; Stancanelli, G.; and San-Miguel-Ayanz, J. In EFSA-EPPO Joint Workshop on Data Collection and Information Sharing in Plant Health, pages 16+.
Assessing the Potential Distribution of Insect Pests under Current and Future Climatic Conditions in European Forests Using Host Data [link]Paper  doi  abstract   bibtex   
In this study we propose a methodology for assessing forest vulnerability to insect pests at pan-European level. Two insect pests are used for testing and validating a methodology that could be extended to other forest insect pests. Our results highlight the strengths of the approach, facilitate information sharing with decision makers and discuss the limitations, including data availability of forests insect pests. Forest insect pests represent a serious threat to European forests and their effects could be exacerbated by warmer climatic conditions. The methodology is illustrated in a pilot study case assessing two European forest pests: Large Pine Weevil ( Hylobius abietis ) and Horse Chestnut Leaf Miner ( Cameraria ohridella ). The proposed approach integrates information from different sources. Data of observed insect pests were collected from the Global Biodiversity Information Facility (GBIF), climatic datasets (current climate and A1B scenario) were sourced from WorldClim and the Research Program on Climate Change, Agriculture and Food Security (CCAFS), and data of European host tree species under current and future climates were provided by EFDAC. The approach is implemented in two steps. First, the potential habitat of the pests is computed using the machine learning algorithm of MAXENT model. Data of observed presence of insect pests and a set of 19 bioclimatic covariates representing current and future climate conditions are input in MAXENT for producing maps of current and future habitat distribution of pests. In a second step the distribution of the corresponding host tree species, computed using the Constrained Spatial Multi-Frequency analysis (C-SMFA), for each pest is integrated with the pest habitat distribution maps to estimate forest vulnerability under current and future climates. Future habitat suitability of host tree species is used instead of current distribution for assessing the potential distribution of pests under future climatic conditions.
@inproceedings{barredoAssessingPotentialDistribution2014,
  title = {Assessing the Potential Distribution of Insect Pests under Current and Future Climatic Conditions in {{European}} Forests Using Host Data},
  booktitle = {{{EFSA}}-{{EPPO Joint Workshop}} on {{Data}} Collection and Information Sharing in Plant Health},
  author = {Barredo, José I. and Strona, Giovanni and de Rigo, Daneile and Caudullo, Giovanni and Stancanelli, Giuseppe and San-Miguel-Ayanz, Jesús},
  date = {2014-04},
  pages = {16+},
  doi = {10.13140/RG.2.2.30537.34407},
  url = {https://doi.org/10.13140/RG.2.2.30537.34407},
  abstract = {In this study we propose a methodology for assessing forest vulnerability to insect pests at pan-European level. Two insect pests are used for testing and validating a methodology that could be extended to other forest insect pests. Our results highlight the strengths of the approach, facilitate information sharing with decision makers and discuss the limitations, including data availability of forests insect pests. Forest insect pests represent a serious threat to European forests and their effects could be exacerbated by warmer climatic conditions. The methodology is illustrated in a pilot study case assessing two European forest pests: Large Pine Weevil ( Hylobius abietis ) and Horse Chestnut Leaf Miner ( Cameraria ohridella ). The proposed approach integrates information from different sources. Data of observed insect pests were collected from the Global Biodiversity Information Facility (GBIF), climatic datasets (current climate and A1B scenario) were sourced from WorldClim and the Research Program on Climate Change, Agriculture and Food Security (CCAFS), and data of European host tree species under current and future climates were provided by EFDAC. The approach is implemented in two steps. First, the potential habitat of the pests is computed using the machine learning algorithm of MAXENT model. Data of observed presence of insect pests and a set of 19 bioclimatic covariates representing current and future climate conditions are input in MAXENT for producing maps of current and future habitat distribution of pests. In a second step the distribution of the corresponding host tree species, computed using the Constrained Spatial Multi-Frequency analysis (C-SMFA), for each pest is integrated with the pest habitat distribution maps to estimate forest vulnerability under current and future climates. Future habitat suitability of host tree species is used instead of current distribution for assessing the potential distribution of pests under future climatic conditions.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13224772,~to-add-doi-URL,climate-change,entropy,europe,forest-pests,forest-resources,habitat-suitability,niche-modelling,plant-pests,random-walk,relative-distance-similarity,spatial-spread},
  options = {useprefix=true}
}
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