4.5 Article

Vulnerability to pine sawfly damage decreases with site fertility but the opposite is true with Scleroderris canker damage; results from Finnish ICP Forests and NFI data

Journal

ANNALS OF FOREST SCIENCE
Volume 72, Issue 7, Pages 909-917

Publisher

SPRINGER FRANCE
DOI: 10.1007/s13595-014-0435-8

Keywords

Forest damage; Pine sawflies; Neodiprion sertifer; Diprion pini; Gremmeniella abietina; Pinus sylvestris

Categories

Funding

  1. Finnish Forest Research Institute
  2. EU Life+ project Climforisk [EU/ENV/FI/00571]
  3. ICP Forests
  4. European Commission in Forest Focus project

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Key message The probability of pine sawfly damage was highest in drier sites, while Gremmeniella abietina damage showed an opposite pattern. ICP Forests and rolling National Forest Inventory (NFI) data have good potential for quantifying patterns in damage occurrence, but region-wise NFIs may produce biased results. Context Factors affecting the occurrence of important biotic damage on Pinus sylvestris were studied with data from large-scale forest monitoring networks. Aims We tested how much the probability of damage caused by pine sawflies (Neodiprion sertifer Geoffr. and Diprion pini L.) and G. abietina (Lagerb. (Morelet)) differed between different forest site types and the effects of relevant climatic factors on damage probabilities. Methods Long-term damage observations from ICP Forests Level 1 monitoring and National Forest Inventory (NFI) data were used. In addition to the traditional frequentist approach, we used a hierarchical Bayesian (HB) framework with the ICP Forests data to model the probabilities of pine sawfly outbreaks starting and continuing. Results The probability of pine sawfly damage was highest in drier sites while the probabilities for G. abietina damage showed an opposite pattern. The HB analysis revealed clear differences between forest site types in the probability of outbreak starting, but the differences in the probabilities of outbreaks continuing were not clear. Conclusion ICP Forests and rolling NFI data have good potential for quantifying patterns in damage occurrence, but annually region-wise NFIs may produce biased results.

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