White pine decline in managed forests has led to the adoption of ecosystem-based management using natural disturbances, mainly surface fire. However, other natural disturbances, such as spruce budworm (SBW), are present in the native range of white pine but are rarely considered in the management of this species. SBW outbreaks are expected to change in frequency and severity under climate change, increasing the need to understand if and how this disturbance will affect the dynamics of white pine regeneration. In this study, we evaluated the impacts of an SBW outbreak on white pine and balsam fir regeneration and identified environmental variables that affect regeneration dynamics. We evaluated six defoliated and six control study plots in white pine stands close to the northern limit of the Québec’s temperate forest. In each plot, we counted white pine and balsam fir seedlings and measured structural (diameter, height), and abiotic (gap fraction, soil type) variables. White pine seedling density was three times greater in defoliated plots than in controls. By contrast, there was no significant difference in balsam fir seedling density between disturbance types. Seedlings of both species were taller in defoliated plots than in control plots. Overall, the SBW outbreak promoted white pine seedling density and height growth. This research contributes to our understanding of the effects of SBW outbreaks on white pine regeneration dynamics, and it will help forest managers to select harvesting methods that emulate SBW-modulated stand characteristics.

Our study explored the spatiotemporal dynamics of spruce budworm (SBW) defoliation in Quebec’s boreal forests, highlighting how climatic factors, historical defoliation, and landscape heterogeneity intersect. SBW outbreaks are a major disturbance in these ecosystems, with significant ecological and economic repercussions—underscoring the need to understand the mechanisms that drive them. Although previous research has linked warming temperatures and past defoliation patterns to more severe outbreaks, their localized effects remain poorly characterized. Our aim is to clarify these localized processes and support more targeted forest management strategies. We employed an adjacent-category autoregressive (ACAR) model specifically designed for ordinal defoliation data spanning 1992–2022. Defoliation was categorized into three severity levels: none, light, and moderate/severe. Key climate variables — most notably spring and summer temperatures, as well as precipitation — were obtained from BioSIM and assigned to each landscape unit (LU). After fitting individual ACAR models to each LU and confirming their adequacy via the Portmanteau test, we identified the best models using the Akaike Information Criterion (AIC). A clustering analysis then grouped LUs with comparable model parameters into distinct ecological response clusters. Our findings reveal that temperature exerts a non-linear influence on SBW defoliation: while warmer spring and summer conditions can initially facilitate larval survival, exceedingly high temperatures reduce defoliation by surpassing larval thermal tolerance and disrupting phenological synchrony with host trees. Additionally, strong autoregressive feedback values (β1,β2) underscore the cumulative effect of past defoliation—trees weakened by previous outbreaks become more susceptible to subsequent infestations, triggering feedback loops that endanger long-term forest health. Through clustering, we identified five distinct landscape groups. The more homogeneous clusters (Clusters 4 and 5) displayed either relatively stable precipitation patterns or pronounced temperature variability, each with high silhouette scores (0.55 and 0.24, respectively), indicating clear opportunities for targeted management. Meanwhile, heterogeneous clusters like Cluster 1 (silhouette score: −0.43) exhibited overlapping characteristics that warrant further investigation. Overall, these results emphasize the importance of localized management approaches that account for climatic thresholds and historical defoliation patterns. Pinpointing temperature extremes and incorporating the impacts of cumulative defoliation can guide both the timing and intensity of interventions. Future research may integrate additional spatial factors, such as forest composition and connectivity, to refine outbreak predictions further. Ultimately, adaptive, multi-scale management is essential for maintaining the resilience of boreal forests in a changing climate.