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Generic Model to Predict the Outbreak of Insects in European Forests

Authors :
Collot, Dorian
Robinet, Christelle
Unité de recherche Zoologie Forestière (URZF)
Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
European Project: 771271,HOMED
Source :
The 1st International Electronic Conference on Entomology, The 1st International Electronic Conference on Entomology, Jul 2021, en ligne, France
Publication Year :
2021
Publisher :
MDPI, 2021.

Abstract

International audience; Insect pests are one of the major threats to forests. Although invasive species cause more and more impacts, native species could also become real pests. The population dynamics of insects relies on several factors, going from weather to stand conditions. Due to global change, insects could face conditions they have never encountered, leading to unusual population outbreaks. Forest managers need to consider these possible emergent pests. However, the biology of these new pests is generally poorly described and predicting insect outbreaks is thus very challenging. In this context, we have developed a generic model of emergence to describe local outbreaks. This model describes the probability of occurrence of an outbreak at a given time and at a given area, based on several conditions (34 variables). It has been built and parametrized on different orders of European forest pests. This parametrization allows obtaining species profiles that can be used as a baseline to make predictions even if poor data are available on the pest, to ensure the genericity of the model. This is to our knowledge the very first generic outbreak model that has been developed so far. This model was coded in R and a user-friendly version using a shiny app was developed. In this work, we are going to present the model and its validation.

Details

Database :
OpenAIRE
Journal :
Proceedings of The 1st International Electronic Conference on Entomology
Accession number :
edsair.doi.dedup.....697973cc9f371ebc1061f7169cbf6a99
Full Text :
https://doi.org/10.3390/iece-10375