The frequent outbreaks of European spruce bark beetle Ips typographus (L.) have destroyed huge amounts of Norway spruce Picea abies (L.) forests in central and Northern Europe. Identifying the risk factors and estimating the damage level is important for strategic damage control. The risk factors of forest damage by spruce bark beetles have mostly been analyzed on the landscape scale, while the in-stand risk factors have been less investigated. This study aims at exploring the local-scale risk factors in a flat area with spruce-dominated forest in southern Sweden. The investigated factors include four abiotic factors, i.e., soil wetness, solar radiation, slope gradient, and aspect, and three biotic factors, i.e., the number of deciduous trees and trees that died from attacks in previous years that remained (TreesLeft) and removed (TreesRemoved) from the forest stand. We put up 24 pheromone bags in six stands attacked by bark beetle in the previous years, resulting in different numbers of infested trees in each plot. We explored in which microenvironment a pheromone bag resulted in more colonization, the impact radius of each factor, and the necessary factors for a risk model. The environmental factors were obtained from remote sensing-based products and images. A generalized linear model (GLM) was used with the environmental factors as the explanatory variables and the damage levels as the response variables, i.e., the number of attacked trees for the plot scale, and healthy/infested for the single-tree scale. Using 50 m and 15 m radius of the environmental factors resulted in the best fit for the model at plot and individual tree scales, respectively. At those radii, the damage risk increased both at plot and individual tree level when spruce were surrounded by more deciduous trees, surrounded by dead trees that had been removed from the forest, and spruces located at the north and east slopes (315 degrees-135 degrees of aspect, > 2 degrees slope). Soil wetness, solar radiation, and remaining standing dead trees in the surrounding did not significantly impact the damage level in the microenvironment of the study area. The GLM risk model yielded an overall accuracy of 0.69 in predicting individual trees being infested or healthy. Our efforts to investigate the risk factors provide a context for wall-to-wall mapping in-stand infestation risks, using remote sensing-based data.

PurposeThe local dimension of toxicity effect on humans and ecosystem from chemical emissions into rural environments is currently not considered in impact characterization models underlying the Life Cycle Assessment (LCA) methodology. The aim of the present study was to understand the relevance of considering a local exposure environment for the magnitude of damage related to human toxicity and ecotoxicity impacts associated with chemical emissions into a local environment.MethodUnit and realistic European emission scenarios were considered in a proposed multimedia nested box model, based on an existing framework modified with the inclusion of a local scale, and tested for ten chemicals in an illustrative case study.ResultsA substantial damage increase in terms of characterization factors in the proposed model was found for human health, mainly for local freshwater emissions (up to three orders of magnitude compared to emissions into continental freshwater for naphthalene) and soil emissions (up to two orders of magnitude, in particular for emissions into natural soil for 1,2-dichloroethane).ConclusionsBased on our results, we suggest to use the proposed framework in LCA applications, when more specific information about the local emission environment is known. With respect to considering the local-scale, however, spatialized models might be preferable over nested box models to properly capture local phenomena in rather highly densely populated areas.