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Current and future distribution of the invasive oak processionary moth

  • M. GodefroidEmail author
  • N. Meurisse
  • F. Groenen
  • C. Kerdelhué
  • J.-P. Rossi
Original Paper

Abstract

Predicting shifts in the distribution and abundance of pest organisms relies on an accurate forecasting of their response to climate change. The oak processionary moth (OPM) Thaumetopoea processionea causes serious damages to oak trees in forest, urban and other landscapes as well as severe allergic reactions to humans and animals. In the 1990’s and 2000’s, the OPM extended its range from mainland Europe and the Middle East into northern Europe. In 2005, it was also accidentally introduced in the United Kingdom. Moreover, the intensity and the frequency of OPM outbreaks are thought to have recently increased in several countries of Europe including Belgium, the Netherlands, Germany and Austria. In the present study, we aimed at forecasting the potential distribution of the OPM in Europe under current and future climate conditions. We thoroughly compiled available records of established populations all across Europe and fitted MaxEnt and BIOCLIM models to infer bioclimatic requirements for this species. Both models showed good predictive performance under current climate conditions. In particular, the surroundings of London where the OPM recently got established were predicted as highly climatically suitable. Models also predicted that many parts of northern Europe where the OPM currently does not occur (e.g. central UK, Wales, Ireland, southern Scotland, Denmark, southern part of the Scandinavian Peninsula, etc.) might become climatically suitable by 2050. Our predictions warrant the need for proper communication and management planning around the risks associated with the potential expansion of the OPM in Europe.

Keywords

Biological invasion Species distribution models Thaumetopoea processionea Quercus Pest risk assessment Invasion risk assessment MaxEnt 

Notes

Acknowledgements

We gratefully acknowledge the contribution of the French Forest Health Department (DSF) for providing records of the OPM in France. We are thankful to Pio Frederico Roversi and Leornado Marzialli (Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria, Firenze, Italy) who kindly provided the occurrences of OPM in Italy. We thank the editor and the two anonymous reviewers who provided constructive comments on a previous version of the manuscript.

Author contribution

All authors collected the data and MG performed the statistical analyses; all authors designed the study and contributed to writing.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

10530_2019_2108_MOESM1_ESM.eps (1.3 mb)
Appendix 1a Occurrences of the oak processionary moth Thaumetopoea processionea used for model calibration after removing duplicates and occurrences collected after 2000, and environmental filtering in CLIM1 climatic space (EPS 1283 kb)
10530_2019_2108_MOESM2_ESM.eps (1.3 mb)
Appendix 1b Occurrences of the oak processionary moth Thaumetopoea processionea used for model calibration after duplicates and occurrences collected after 2000, and environmental filtering in CLIM2 climatic space. Maps were projected using a lambert azimuthal equal-area projection (EPS 1284 kb)
10530_2019_2108_MOESM3_ESM.eps (15.4 mb)
Appendix 2 Climate suitability for the oak processionary moth Thaumetopoea processionea under climate conditions relative to the period 1970-2000 in Europe as predicted by MaxEnt models fitted with the two climatic datasets CLIM1 (A) and CLIM2 (C). Maps yield the average climate suitability Index predicted by 10 model replicates. Detailed maps are also provided for the British Isles (B, D). Maps were projected using a lambert azimuthal equal-area projection (EPS 15777 kb)
10530_2019_2108_MOESM4_ESM.eps (15.4 mb)
Appendix 3 Climate suitability for the oak processionary moth Thaumetopoea processionea under climate conditions relative to the period 1970-2000 in Europe as predicted by BIOCLIM models fitted with the two climatic datasets CLIM1 (A) and CLIM2 (C). Maps yield the average climate suitability Index predicted by 10 model replicates. Detailed maps are also provided for the British Isles (B, D). Maps were projected using a lambert azimuthal equal-area projection (EPS 15777 kb)
10530_2019_2108_MOESM5_ESM.eps (15.4 mb)
Appendix 4 Potential distribution for the oak processionary moth Thaumetopoea processionea under climate conditions relative to the period 1970-2000 in Europe as predicted by MaxEnt models fitted with the two climatic datasets CLIM1 (A) and CLIM2 (C). Maps yield the number of models (out of 10 replicates) predicting a presence when implementing a threshold maximizing the sum of sensitivity and specificity. Detailed maps are also provided for the British Isles (B, D). Maps were projected using a lambert azimuthal equal-area projection (EPS 15776 kb)
10530_2019_2108_MOESM6_ESM.eps (15.4 mb)
Appendix 5 Potential distribution for the oak processionary moth Thaumetopoea processionea under climate conditions relative to the period 1970-2000 in Europe as predicted by BIOCLIM models fitted with the two climatic datasets CLIM1 (A, C) and CLIM2 (B, D). Maps yield the number of models (out of 10 replicates) predicting a presence when implementing a threshold maximizing the sum of sensitivity and specificity. Detailed maps are also provided for the British Isles (B, D). Maps were projected using a lambert azimuthal equal-area projection (EPS 15776 kb)
10530_2019_2108_MOESM7_ESM.eps (28.1 mb)
Appendix 6 Climate suitability for the oak processionary moth Thaumetopoea processionea under climate conditions in 2050 (A, C) and 2070 (B, D) in as predicted by MaxEnt models fitted with the climatic dataset CLIM1. Predictions are provided for the RCP4.5 (A, B) and RCP8.5 (C, D) greenhouse concentration pathways. Maps yield the average climate suitability Index predicted by 10 model replicates. Maps were projected using a lambert azimuthal equal-area projection (EPS 28800 kb)
10530_2019_2108_MOESM8_ESM.eps (28.1 mb)
Appendix 7 Climate suitability for the oak processionary moth Thaumetopoea processionea under climate conditions in 2050 (A, C) and 2070 (B, D) in Europe as predicted by BIOCLIM models fitted with the climatic dataset CLIM1. Predictions are provided for the RCP4.5 (A, B) and RCP8.5 (C, D) greenhouse concentration pathways. Maps yield the average climate suitability Index predicted by 10 model replicates (EPS 28800 kb)
10530_2019_2108_MOESM9_ESM.eps (28.1 mb)
Appendix 8 Climate suitability for the oak processionary moth Thaumetopoea processionea under climate conditions in 2050 (A, C) and 2070 (B, D) in Europe as predicted by MaxEnt models fitted with the climatic dataset CLIM2. Predictions are provided for the RCP4.5 (A, B) and RCP8.5 (C, D) greenhouse concentration pathways. Maps yield the average climate suitability Index predicted by 10 model replicates. Maps were projected using a lambert azimuthal equal-area projection (EPS 28800 kb)
10530_2019_2108_MOESM10_ESM.eps (28.1 mb)
Appendix 9 Climate suitability for the oak processionary moth Thaumetopoea processionea under climate conditions in 2050 (A, C) and 2070 (B, D) in Europe as predicted by BIOCLIM models fitted with the climatic dataset CLIM2. Predictions are provided for the RCP4.5 (A, B) and RCP8.5 (C, D) greenhouse concentration pathways. Maps yield the average climate suitability Index predicted by 10 model replicates. Maps were projected using a lambert azimuthal equal-area projection (EPS 28800 kb)
10530_2019_2108_MOESM11_ESM.eps (28.1 mb)
Appendix 10 Potential distribution for the oak processionary moth Thaumetopoea processionea under climate conditions in 2050 (A, C) and 2070 (B, D) in Europe and the Middle East as predicted by MaxEnt models fitted with the climatic dataset CLIM1. Predictions are provided for the RCP4.5 (A, B) and RCP8.5 (C, D) greenhouse concentration pathways. Maps yield the number of models (out of 10 replicates) predicting a presence when implementing a threshold maximizing the sum of sensitivity and specificity. Maps were projected using a lambert azimuthal equal-area projection (EPS 28799 kb)
10530_2019_2108_MOESM12_ESM.eps (28.1 mb)
Appendix 11 Potential distribution for the oak processionary moth Thaumetopoea processionea under climate conditions in 2050 (A, C) and 2070 (B, D) in Europe and the Middle East as predicted by BIOCLIM models fitted with the climatic dataset CLIM1. Predictions are provided for the RCP4.5 (A, B) and RCP8.5 (C, D) greenhouse concentration pathways. Maps yield the number of models (out of 10 replicates) predicting a presence when implementing a threshold maximizing the sum of sensitivity and specificity. Maps were projected using a lambert azimuthal equal-area projection (EPS 28799 kb)
10530_2019_2108_MOESM13_ESM.eps (28.1 mb)
Appendix 12 Potential distribution for the oak processionary moth Thaumetopoea processionea under climate conditions in 2050 (A, C) and 2070 (B, D) in Europe and the Middle East as predicted by MaxEnt models fitted with the climatic dataset CLIM2. Predictions are provided for the RCP4.5 (A, B) and RCP8.5 (C, D) greenhouse concentration pathways. Maps yield the number of models (out of 10 replicates) predicting a presence when implementing a threshold maximizing the sum of sensitivity and specificity. Maps were projected using a lambert azimuthal equal-area projection (EPS 28799 kb)
10530_2019_2108_MOESM14_ESM.eps (28.1 mb)
Appendix 13 Potential distribution for the oak processionary moth Thaumetopoea processionea under climate conditions in 2050 (A, C) and 2070 (B, D) in Europe and the Middle East as predicted by BIOCLIM models fitted with the climatic dataset CLIM2. Predictions are provided for the RCP4.5 (A, B) and RCP8.5 (C, D) greenhouse concentration pathways. Maps yield the number of models (out of 10 replicates) predicting a presence when implementing a threshold maximizing the sum of sensitivity and specificity. Maps were projected using a lambert azimuthal equal-area projection (EPS 28799 kb)

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CBGP, INRA, CIRAD, IRD, Montpellier SupAgroUniv MontpellierMontpellierFrance
  2. 2.Consejo Superior de Investigaciones Cientificas – Instituto de Ciencias Agrarias (ICA – CSIC)MadridSpain
  3. 3.Scion (New Zealand Forest Research Institute)RotoruaNew Zealand
  4. 4.BergeijkThe Netherlands

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