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Defoliation level and site type are thought to influence tree response during spruce budworm (Choristoneura fumiferana (Clemens)) outbreaks. We determined the effects of four manual defoliation treatments (0%, 50%, 100%, and 100% + bud removal of current foliage) for 3 years on foliage production of balsam fir (Abies balsamea (L.) Mill.), black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.), and white spruce (Picea glauca (Moench) Voss) trees on four site-quality classes. After 3 years of defoliation and 2 years of recovery, foliage biomass was reduced by 34%–98%. During defoliation, the number of shoots generally increased and shoot length of spruce generally decreased, especially on rich sites. During recovery, the number of shoots increased substantially, shoot length decreased, and bud destruction reduced the number of shoots by about 50% compared with that of trees that received the 100% defoliation treatment. Defoliation did not substantially affect needle length. Trees on rich sites had two- to fourfold greater foliage production than trees on poor sites. Effects of site and defoliation differed among species, but site quality, especially nutrition, played an important role in production of shoots and needles and the tree's ability to withstand defoliation. Black spruce had more limited ability to recover foliage biomass, only producing more shoots, whereas balsam fir and white spruce had stronger ability to recover needle and shoot length, respectively. [ABSTRACT FROM AUTHOR]

Detection of spruce budworm ( Choristoneura fumiferana Clem.) defoliation is critical for forest protection strategies aimed at minimizing losses in growth and mortality. However current aerial and ground survey methods of detecting defoliation are imprecise and subjective, limiting their usefulness. We evaluated the use of hemispherical crown canopy images to quantify annual spruce budworm defoliation, comparing images taken before and after defoliation in each of two years in 75 sample plots in Québec, Canada. Gradient Boosting Machine analysis identified gap fraction change from May-October, gap fraction after defoliation, insecticide spraying, and % balsam fir ( Abies balsamea (L.) Mill.) basal area as important explanatory variables of defoliation. Logistic Generalized Linear Model (GLM) and Random Forests (RF) models were trained on a random two-thirds of sample plots combining both years, and defoliation predictions were validated on the remaining one-third of plots. RF predictions consistently resulted in slightly higher correlations and lower root mean squared errors (RMSEs) than GLM predictions. Defoliation models including insecticide spraying, gap fraction change May-October, and % balsam fir basal area had RMSEs of 14–22%, whereas models excluding insecticide spraying had higher RMSEs of 18–24%. Model goodness-of-fit using two-sample Kolmogorov-Smirnov tests indicated that predicted and measured annual defoliation had similar distributions, with the exception of GLM and RF models excluding spray compared to ocular defoliation. Use of hemispherical images to quantify gap fraction change is a feasible, non-destructive, and objective method to assess canopy foliage changes caused by spruce budworm defoliation. [ABSTRACT FROM AUTHOR]

Predictions of defoliation are an important component of planning aerial insect control programs, especially for defoliators such as balsam fir sawfly (Neodiprion abietis (Harris)) that cause severe impacts on forest growth and yield. Currently, defoliation prediction is done manually based on field observations and experience, but it is a good candidate for a Bayesian network (BN), a flexible tool for combining available expert knowledge and empirical data. We created a BN model and linked it to a geographic information system to map predicted defoliation for balsam fir sawfly in western Newfoundland over an area of 5.7 million ha from 2001 to 2008. Based on expert knowledge, probabilistic influence of egg counts, previous defoliation, and stand characteristics (species composition, stand age, and management intervention) on subsequent-year defoliation was quantified. For validation purposes, maps created using the BN model were compared with manual defoliation predictions and with measured aerial defoliation survey maps. BN model defoliation prediction maps were found to be in moderate agreement (mean Kappa value of 0.59) with conventional manual prediction maps. Overall, the BN model showed similar accuracy to manual predictions, but with benefits of automating the process and of providing more spatial detail in predictions. [ABSTRACT FROM AUTHOR]

Spruce budworm (Choristoneura fumiferana Clem.) severely defoliates balsam fir (Abies balsamea (L.) Mill.) and spruce (Picea spp.) in large periodic outbreaks and represents one of Canada’s most damaging and widespread forest insects. We present a modeling framework that integrates stand-level spruce budworm volume impacts used in the Spruce Budworm Decision Support System (SBWDSS) into an industrial-scale timber supply model for the 209 000 ha Black Brook District in northwestern New Brunswick, Canada. This approach uses linear optimization of harvest scheduling, salvage, and insecticide application to minimize volume reduction. One hundred and ninety-five scenarios were simulated, including normal and severe spruce budworm outbreaks, beginning in 2002, with combinations of varying insecticide efficacy, timing, and spatial extent of protection. After simulated severe defoliation from 2007 to 2016, maximum harvest reductions of 35% were predicted for a normal outbreak for the 2012–2016 period, and 46% for a severe outbreak for the 2017–2021 period; these impacts were reduced to 25% and 34% using re-optimized harvest scheduling and salvage. Results suggest that combined optimized salvage and harvest rescheduling could reduce future harvest reductions by up to 12%. Spatial optimization of protected areas gave similar results to those obtained using protection priority assignments based on marginal stand-level volume reduction in the SBWDSS. [ABSTRACT FROM AUTHOR]

During spruce budworm (SBW; Choristoneura fumiferana (Clem.)) outbreaks, defoliation causes severe tree damage and mortality to spruce-balsam fir (Picea spp. - Abies balsamea (L.) Mill.) forests. Annual defoliation thematic maps provide vital information for forest protection planning against outbreaks, but such data are challenging to accurately collect over large areas during a short (2–3 week) period when damaged foliage peaks. Current operational methods for estimating annual defoliation are aerial surveys, through visual observations by professionals from small aircraft, defining broad defoliation classes which are subjective and spatially can be inaccurate. Remote sensing studies estimating annual defoliation are limited due to lack of ground verification data and difficulty of capturing spectral changes specific to the short detection period. Only one study has used satellite hyperspectral imagery to detect annual SBW defoliation, classifying just two classes of defoliation and using inaccurate aerial survey results for training and validation. In this paper, we introduce a new method to estimate annual SBW defoliation in three classes (light ≤ 30%, moderate 30–70%, and severe ≥ 70%), using change detection of vegetation indices (VIs) derived from satellite (EO-1 Hyperion) hyperspectral imagery acquired before and after annual defoliation. Random Forest and Support Vector Machine classifiers were used to classify defoliation, and intensive field measurements of branch-level defoliation per plot were used for accuracy assessment. Field plots, with spatially correlated defoliation within stands, were used to derive an additional 624 plots to obtain sufficient and independent datasets for training and validating the detection results. The top four VIs for detecting annual defoliation, based on Random Forest Total Variable Importance Score and Spearman's rank correlations, were Plant Senescence Reflectance Index, Normalized Difference Water Index, Moisture Stress Index, and Enhanced Vegetation Index. The spectral regions of VIs used to classify defoliation included visible, near-infrared, red-edge, and shortwave infrared. Accuracies of the light, moderate, and severe defoliation classes using our method ranged from 55–85%, 40–76%, and 40–80%, respectively, which were significantly higher than the 32% accuracy of aerial survey defoliation data for the plots. • Spruce budworm defoliation was detected using hyperspectral (EO-1 Hyperion) data. • Classified defoliation was validated with accurate branch defoliation measurements. • Multiple vegetation indices were selected using Random Forest variable importance. • Results were similar using Random Forest and Support Vector Machine classifiers. • Defoliation detection with hyperspectral data was superior to aerial surveys. [ABSTRACT FROM AUTHOR]

This research compares the capabilities of various Sentinel-2-derived spectral vegetation indices (SVIs) in particular red-edge SVIs to detect and classify spruce budworm (Choristoneura fumiferana) (SBW) defoliation using Support Vector Machine (SVM) and Random Forest (RF) models. The results showed the superiority of RF in model building for defoliation detection and classification into three classes (nil, light, and moderate) with overall errors of 17% and 32%, respectively. The most important variables for the best model were Enhanced Vegetation Index 7 (EVI7), Modified Chlorophyll Absorption in Reflectance Index (MCARI), Inverted Red-Edge Chlorophyll Index (IRECI), Normalized Difference Infrared Index 11 (NDII11) and Modified Simple Ratio (MSR). Red-edge SVIs were more effective variables for light defoliation detection compared to traditional SVIs such as Normalized Difference Vegetation Index (NDVI) and EVI8. These findings can help improve current remote sensing-based SBW defoliation detection and monitoring. [ABSTRACT FROM AUTHOR]

Abstract: Insect outbreaks are major natural disturbance events that affect communities of forest birds, either directly by affecting the food supply or indirectly by changing the vegetation composition of forest canopies. An examination of correlations between measures of bird and insect abundance across different spatial scales and over varying time lag effects may provide insight into underlying mechanisms. We developed a hierarchical Bayesian model to assess correlations between counts of eight warbler species from the Breeding Bird Survey in eastern Canada, 1966 to 2009, with the presence of spruce budworm (Choristoneura fumiferana Clem.) at immediate local scales and time‐lagged regional scales, as measured by extent of defoliation on host tree species. Budworm‐associated species Cape May warbler (Setophaga tigrina), bay‐breasted warbler (Setophaga castanea), and Tennessee warbler (Oreothlypis peregrina) responded strongly and positively to both local and regional effects. In contrast, non‐budworm‐associated species, Blackburnian warbler (Setophaga fusca), magnolia warbler (Setophaga magnolia), Canada warbler (Cardellina canadensis), black‐throated blue warbler (Setophaga caerulescens), and black‐throated green warbler (Setophaga virens), only responded to regional effects in a manner that varied across eastern Canada. The complex responses by forest birds to insect outbreaks involve both increased numerical responses to food supply and to longer term responses to changes in forest structure and composition. These effects can vary across spatial scales and be captured in hierarchical population models, which can serve to disentangle common trends from data when examining drivers of population dynamics like forest management or climate change. [ABSTRACT FROM AUTHOR]

Defoliation reduces the growth and survival of trees, but this influence can be difficult to evaluate largely because of its interplay with various stand and site factors, especially for the highly dynamic defoliation of spruce-fir ( Picea- Abies) forests caused by spruce budworm (SBW; Choristoneura fumiferana (Clem.)), the primary tree defoliator in North America. In this study, we developed statistical models to evaluate the influence of SBW defoliation, while considering its interaction with various stand and site factors, on spruce-fir stand dynamics of annual volume net growth, mortality, and ingrowth. The data were collected at intervals of 1 to 3 years from 560 permanent sample plots during the last SBW outbreak (1970s-1980s) in Maine, USA, and New Brunswick, Canada. These data comprise a wide range of observations of cumulative defoliation, especially at relatively low levels, that have been largely overlooked in previous studies. Our results strongly demonstrated that even relatively low levels of cumulative defoliation were significantly related to stand-level mortality and ingrowth, while net growth was more competition driven. Additionally, these stand dynamics were found to be not significantly affected by any of the site factors evaluated. These findings were consistent for Maine and New Brunswick despite their differences in forest management and SBW outbreak histories. [ABSTRACT FROM AUTHOR]

Spruce budworm ( Choristoneura fumiferana Clem.) is an important and recurrent disturbance throughout spruce ( Picea sp.) and balsam fir ( Abies balsamea L.) dominated forests of North America. Forest carbon (C) dynamics in these ecosystems are affected during insect outbreaks because millions of square kilometers of forest suffer growth loss and mortality. We tested the hypothesis that a spruce budworm outbreak similar to those in the past could switch a forest from a C sink to a source in the near future. We used a model of ecosystem C to integrate past spruce budworm impact sequences with current forest management data on 106,000 km of forest in eastern Québec. Spruce budworm-caused mortality decreased stand-level merchantable C stocks by 11–90% and decreased ecosystem C stocks by 2–10% by the end of the simulation. For the first 13 years (2011–2024), adding spruce budworm significantly reduced ecosystem C stock change for the landscape from a sink (4.6 ± 2.7 g C m y in 2018) to a source (−16.8 ± 3.0 g C m y in 2018). This result was mostly due to reduced net primary production. The ecosystem stock change was reduced on average by 2 Tg C y for the entire simulated area. This study provides the first estimate that spruce budworm can significantly affect the C sink or source status of a large landscape. These results indicate that reducing spruce budworm impacts on timber may also provide an opportunity to mitigate a C source. [ABSTRACT FROM AUTHOR]