The spruce budworm, Choristoneura fumiferana, Clem., is the most significant defoliating pest of boreal balsam fir (Abies balsamea (L.) Mill.) and spruce (Picea sp.) in North America. Historically, spruce budworm outbreaks have been managed via a reactive, foliage protection approach focused on keeping trees alive rather than stopping the outbreak. However, recent theoretical and technical advances have renewed interest in proactive population control to reduce outbreak spread and magnitude, i.e., the Early Intervention Strategy (EIS). In essence, EIS is an area-wide management program premised on detecting and controlling rising spruce budworm populations (hotspots) along the leading edge of an outbreak. In this article, we lay out the conceptual framework for EIS, including all of the core components needed for such a program to be viable. We outline the competing hypotheses of spruce budworm population dynamics and discuss their implications for how we manage outbreaks. We also discuss the practical needs for such a program to be successful (e.g., hotspot monitoring, population control, and cost–benefit analyses), as well as the importance of proactive communications with stakeholders.

Aim: Amazon‐nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon‐nut and to identify the most important predictor variables to support conservation and tree planting decisions.Localization: Amazon region, South America.Methods: We collected 3,325 unique Amazon‐nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine‐tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments.Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Amazon‐nut is found across 2.3 million km², that is, 32% of the Amazon Biome.Main conclusion: The combination of statistical techniques with expert knowledge improved the quality of our suitability model. Topographic and soil variables were the most important predictors. The combination of predictor variable selection, fine‐tuning of model parameters and spatial filtering was critical for the construction of a reliable habitat suitability model.