Abstract
Bark beetle outbreaks are a major threat to forest productivity, and a robust forecast of early flight activity is necessary for inhibition or mitigation of large-scale infestations. We used spring phenology of common wild plants in a phenology-based forecasting approach for European spruce bark beetle Ips typographicus L. early flight activity in Bavaria, Southern Germany, and tested this novel approach against traditional thermal sum-based predictions. Our phenology-based forecast employing the 2 phenological phases of first flowering of common snowdrop Galanthus nivalis L. and leaf budburst of horse chestnut Aesculus hippocastanum L. proved to be more robust and accurate than the thermal sum-based forecast. This is explained by both bark beetle phenology and plant phenology being results of a complex control chain of environmental factors, which can be approximated by temperature sums only to a limited degree. However, our space-for-time approach demonstrates strong and unequivocal temperature sensitivity of bark beetle and plant phenology. This indicates a common pattern in bioclimatic mediation of ecophysiological processes for both plants and insects as the mechanistic foundation for forecasting. In the case of costly bark beetle activity monitoring data often characterised by gaps and irregular sampling intervals, plant phenology can thus provide an easily observable alternative or complementary predictor for early flight activity. Our results indicate that forest practitioners can benefit from simple phenological observations to improve the timing of adequate management measures to mitigate bark beetle mass infestations.
Original language | English |
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Pages (from-to) | 161-170 |
Journal | Climate Research |
Volume | 66 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2016 |
Bibliographical note
This article is published under a Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) .Keywords
- Bark beetle · Ips typographus · Phenology · Galanthus nivalis · Aesculus hippocastanum · Linear mixed effects model