Influence of clustering of wind-fallen trees on the spread of Ips typographus [L.]

 

Introduction

 

Spruce bark beetles (Ips typographus, L.) cause tree mortality and are actually one of the most important disturbances in coniferous forests (Picea abies L.) in Europe (Schelhaas, Nabuurs, & Schuck, 2003) and over boreal forest of North America (see www.nifc.gov, the National Interagency Fire Center Web site; USDA Forest Service 2005) often affecting a larger area than fire does. During endemic condition, beetles (BB) are not numerous enough to kill healthy trees, so they reproduce on felled or dying ones. These sites are too rare to allow rapid population growth (Sauvard, 2004). An beetle outbreak is triggered by i) severe climatic event causing large-scaled disturbance or by ii) decreased tree resistance over long-term altered climatic conditions(Berryman, 1982). Both of these conditions were fulfilled in Central Europe, especially in High Tatra Mts. by i) severe drought in 2003 (Martin, Warz, & Nageleisen, 2006) and by ii) large-scaled wind gale disaster in 2004. Area of almost 12 000 ha of forest was damaged, composed mostly by spruces trees (Zúbrik et al., 2004). Localized in National Park, however, the 90% of uprooted trees were salvages and only 8% was left as wind-left trees (Kunca & Zúbrik, 2006).

 

Following Slovak forest management implications, the scattered wind-felled trees should to be removed in first place to prevent subsequent bark beetle outbreak and centralized wind-felled trees should be processed as the second one (Gubka et al., 2014). Although in Finnish forestry laws, it is possible to leave fewer than 20 wind-fallen spruce trees during endemic conditions (Eriksson, Neuvonen, & Roininen, 2007). Both regulations tend to prevent subsequent bark beetle outbreak. From their different measures it is however obvious that influence of the clustering level of uprooted trees on beetle dynamics is still not recognize and is consider as knowledge gap.

Agent-based models (ABMs) have become widely accepted and used tool in social and ecological studies (Grimm et al., 2005) and allow analyses of the complex system of interactions between different entities based on bottom-up approach, where the individual’s decisions (i.e. beetle movement, host selection, interactions among entities …) lead to outgoing emergent pattern (i.e. tree infestation and mortality). ABMs can be used mostly to simulate conditions and their influence of observed phenomena which are impossible to study in field- or lab- experiments because of extended temporal, spatial scales or even moral aspects.

 

As such, ABMs allow simulation of complex interaction between spreading beetles and vulnerable trees and answer the question about influence of altering stand conditions (levels of clustering of weakened trees) on bark beetle tree infestation success. Various levels of clustered wind-fallen trees (Figure 1) are commonly created by wind or snow disturbances in coniferous forests. The information about influence of level of clustering of uprooted trees on subsequent bark beetle outbreak dynamics can be crucial for forest management application and decision making in case of more or less extensive wind gales.

 

The objective of the study is thus to determine the influence of various level of clustering of wind-fallen trees on bark beetle infestation success, tree mortality and maximum infestation distance. Equally, to fill the actual knowledge gap about triggering of bark beetle outbreak by severity of climatic event and the trade-off between spatial beetles dispersion and aggregation needed to overpass tree resistance. 

 

This objective will be achieved using advanced IPS model published by (Kautz, Schopf, & Imron, 2014) with integrated level of clustering of weakened (presenting uprooted) trees on landscape level, named Influence of Clustering model (IC). Sensitivity analysis will be used to reveal influence of the clustering level on system level responses as bark beetle successful attack, tree mortality and maximum infestation distance.