Spruce budworm populations in New Brunswick have been surveyed annually since 1952 by sampling egg masses (later, overwintering larvae) as part of the insecticide application program. Although not designed for an ecological investigation, we extracted as much information from the survey data as we could with respect to several ecological issues. (1) All populations across the province tended to cycle in unison, although three major regions were distinguished by dissimilarity in peak and trough levels. We found that these regional distinctions were a result of random variation in the egg recruitment rate, rather than due to factors associated with comparatively fixed ecoregional (e.g., topographic, climatic, or forest type) characteristics. (2) We found, among all regions, a significant correlation in the rate at which eggs were recruited to each generation, thus providing evidence for the Moran effect as the mechanism underlying population synchrony that caused the province-wide outbreaks. (3) We discuss, with the aid of simulations, the nature and significance of random variations in the egg recruitment rate to explain observed differences in the spatial and temporal patterns of population cycles. Finally, we remark on problems in forecasting.

The accuracy of aerial sketch-mapping estimates of spruce budworm (Choristoneurafumiferana (Clem.)) defoliation was evaluated from 1984 to 1993 in 222–325 sample plots in spruce (Picea sp.)–balsam fir (Abiesbalsamea (L.) Mill.) stands in New Brunswick. Operational aerial defoliation estimates were used, wherein all productive forest in known budworm infestation zones was surveyed each year from small aircraft with flight lines 2–5 km apart, and rated in classes of nil (0–10%), light (11–30%), moderate (31–70%), and severe (71–100%). Aerial defoliation estimates were compared with ground-based binocular estimates of current defoliation for an average of 10 trees/plot (range 5–20). Overall, 56% of plots were correctly rated by aerial sketch mapping in four classes (nil, light, moderate, and severe), with 37% of the plots underestimated and 7% overestimated. The predominant error (26% of plots) was rating defoliation as nil (0–10%) from the air when it was actually light (11–30%). This error was deemed not important in terms of predicting tree response, since data from the literature indicated that defoliation less than 30% did not cause tree mortality, although if continued, it would reduce growth. Using three defoliation classes (by combining nil and light, 0–30%), 82% of the plots were correctly classified by aerial sketch mapping. The probability of correct aerial classification of defoliation was significantly affected by defoliation class, weather conditions prior to and during observation flights, and the defoliation class × weather interaction. It was concluded that aerial sketch mapping of spruce budworm defoliation is a viable technique that can be used for both surveys and decision support systems that estimate forest response to budworm outbreaks and management activities.

Historical records of spruce budworm defoliation in Canada were analyzed to estimate variability in the spatial and temporal patterns of defoliation, and to determine 27 representative patterns that adequately describe the spatial and temporal variability in defoliation. Spatially referenced estimates of growth loss and mortality resulting from an outbreak of spruce budworm were obtained by subjecting a national forest inventory to the spatially defined representative patterns of defoliation. The use of these estimates in determining the status of Canada's forests as a carbon source or sink is discussed.

The Spruce Budworm Decision Support System (SBW DSS) quantifies the marginal timber supply (m3/ha) benefits of protecting stands against spruce budworm (Choristoneura fumiferana (Clem.)) defoliation. It allows the user to quantify the volume benefit of protecting alternative areas and determine effects on forest development and annual allowable cut. Implementing the SBW DSS on a land base involves seven steps: (i) defining the base defoliation, or an explicit forecast of the defoliation level included in yield forecasts; (ii) compiling historical defoliation; (iii) defining base volume yields; (iv) obtaining the harvest schedule from the land base management plan; (v) building the stand impact matrix, which quantifies direct impacts of defoliation; (vi) building the forest impact matrix, which quantifies indirect impacts of defoliation on harvest schedules; and (vii) building the stand-history file, which contains all stand-level and defoliation data. These tasks are usually completed every 5 years. The remaining aspects of the planning methodology are implemented annually, including (i) recording the previous year's defoliation, (ii) estimating potential defoliation in the current year from budworm survey data, (iii) calculating volume loss or protection priority, (iv) generating budworm-caused volume loss maps, (v) digitizing potential spray blocks, and (vi) evaluating the protection program. Using ARC/INFO® and ArcView® geographic information system programs, the Protection Planning System component (PROPS) generates volume loss maps that can be used to help design and analyze costs and benefits of insecticide spray programs. Implementation of PROPS for the 450 000 ha Upper Miramichi Crown License in New Brunswick is described. Under "normal" and "severe" budworm outbreak scenarios, defined based on predictions of 1999–2008 defoliation, losses of 6.6 × 106 and 16.7 × 106 m3 of spruce (Picea sp.) – balsam fir (Abies balsamea (L.) Mill.) volume were projected to occur on this land base.