The importance of efficaciously assessing the risk for introduction and establishment of pest species is an increasingly important ecological and economic issue. Evaluation of climate is fundamental to determining the potential success of an introduced or invasive insect pest. However, evaluating climatic suitability poses substantial difficulties; climate can be measured and assessed in a bewildering array of ways. Some physiological filter, in essence a lens that focuses climate through the requirements and constraints of a potential pest introduction, is required. Difficulties in assessing climate suitability are further exacerbated by the effects of climate change.
Gypsy moth (Lymantria dispar L.) is an exotic, tree-defoliating insect that is frequently introduced into the western United States. In spite of an abundance of potential host species, these introductions have yet to result in established populations. The success of eradication efforts and the unsuccessful establishment of many detected and undetected introductions may be related to an inhospitable climate. Climatic suitability for gypsy moth in the western United States, however, is potentially improving, perhaps rapidly, due to a general warming trend that began in the mid 1970s and continues today. In this work, we describe the application of a physiologically based climate suitability model for evaluating risk of gypsy moth establishment on a landscape level.
Development of this risk assessment system first required amassing databases that integrated the gypsy moth climatic assessment model, with host species distributions, and climate (historical, present, and future). This integrated system was then used to evaluate climate change scenarios for native host species in Utah, with the result that risk of establishment will dramatically increase during the remainder of the 21st century under reasonable climate change scenarios. We then applied the risk assessment system to several case histories of detected gypsy moth introductions in Utah. These applications demonstrated the general utility of the system for predicting risk of establishment and for designing improved risk detection strategies.
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