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Trond Rafoss

Publications and source records attributed to Trond Rafoss.

2 recordsLinked to original sources

Can sunspot activity and ultraviolet-B radiation explain cyclic outbreaks of forest moth pest species?

Cyclic outbreaks of forest moth pest species have long remained a puzzle for foresters and ecologists. This paper presents time-series exhibiting a strong negative relationship between sunspot numbers and population indices of autumnal and winter moths, both in a mountain birch forest in central Norway and in a mixed lowland forest in southern Norway. In the latter area, also the population level of a moth species feeding entirely on lichens was negatively related to sunspot numbers. Low sunspot activity leads to a thinner ozone layer and thus higher surface ultraviolet (UV)-B radiation. As winter moth larvae prefer leaves subjected to enhanced UV-B radiation, we suggest that the causal relationship between sunspots and moths is that the metabolic costs of producing UV-B-protective pigments during periods of low sunspot activity reduce trees' and lichens' resistance to herbivores, and thus increase the survival of moth larvae. Higher peak densities of moth cycles in mountain forests could be explained by the general higher UV-B radiation at higher altitudes.

Animals↗

Spatial stochastic simulation offers potential as a quantitative method for pest risk analysis.

Pest risk analysis represents an emerging field of risk analysis that evaluates the potential risks of the introduction and establishment of plant pests into a new geographic location and then assesses the management options to reduce those potential risks. Development of new and adapted methodology is required to answer questions concerning pest risk analysis of exotic plant pests. This research describes a new method for predicting the potential establishment and spread of a plant pest into new areas using a case study, Ralstonia solanacearum, a bacterial disease of potato. This method combines current quantitative methodologies, stochastic simulation, and geographic information systems with knowledge of pest biology and environmental data to derive new information about pest establishment potential in a geographical region where a pest had not been introduced. This proposed method extends an existing methodology for matching pest characteristics with environmental conditions by modeling and simulating dissemination behavior of a pest organism. Issues related to integrating spatial variables into risk analysis models are further discussed in this article.

Ecosystem↗