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Sirex noctiliois one of the most important invasive pests that affectPinus radiataplantations in Chile. Its management is based on a biological control complex, the most important component of which is the nematode,Deladenus siricidicola. However, in some areas,S. noctiliopopulations attain epidemic levels and no effective control methods exist to reduce large populations in a short period. In this study, we evaluated a novel method called mega-trap-plots (MTPs), which consist of an area of 1 ha with trap trees, which were debilitated in four different months (from November to February) with the purpose of reducing the Sirex wasp population through harvesting of trees attacked. The main objective was to reduce the potential population ofS. noctilio, by evaluating four periods or months of MTP installation to maximise the colonisation of trap trees by wood wasps. The results showed that the MTPs that were installed from November to January had the highest wood wasp infestation, which was coincident with the flight period of the insect. The trap trees were clearly attractive toS. noctiliofemales up to 90 d following their establishment. The MTPs that were established in November concentrated a potential population of 57 901 females ofS. noctilio, which represents a population 5.4 times greater than that in the control, with 10 701 females. The population ofS. noctilioattracted between November and January shows that the use of MTPs is an effective system for the management of wood wasps on plantations with a high level of infestation and thereby can reduce their spread and the attack of new trees within the same forest compartments. [ABSTRACT FROM AUTHOR]

Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel. • Traditional species habitat suitability models were compared to machine learning. • Extreme gradient boosting outperformed all other techniques. • In comparison, generalized linear, random forest, and MAXENT models underperformed. • Model performance improved as background point quantities increased. • Extreme gradient boosting is proposed for complex presence-only data. [ABSTRACT FROM AUTHOR]