Spatial variability in defoliation dynamics during spruce budworm outbreaks: A landscape perspective

Abstract

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 (${\beta }_1,{\beta }_2$) 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.