Forest landscape structure influences the cyclic‐eruptive spatial dynamics of forest tent caterpillar outbreaks

Abstract

A fundamental question in forest insect ecology is the role of forest landscape structure, particularly the amount and spatial configuration of host tree species, in shaping the dynamics of recurring forest insect outbreaks. For forest tent caterpillar (FTC), independent studies do not converge on a singular conclusion, although all indicate that forest structure influences outbreak dynamics. These studies also vary in how they treat climate as a covariate. We evaluated the relative importance of host forest landscape composition and configuration, as well as climate, for their influence on FTC outbreak cycling in the twentieth century. We predicted that FTC outbreaks would exhibit greater synchrony and intensity within areas associated with higher abundance of host trees. We reconstructed FTC outbreaks from 1928 to 2006 using tree‐ring analysis within a well‐structured experimental landscape located in northwestern Ontario and northern Minnesota. Time‐series clustering and spatial nonparametric covariance were used to determine whether similarities in time series and patterns in spatial synchrony corresponded with land management history. Using constrained ordination, we compared statistical properties of outbreak time series to landscape variables representing host abundance, forest configuration, and climate. We found no evidence of climatic effects at the scale of this study, but a significant albeit small influence of landscape structure on outbreak dynamics. Outbreaks were more synchronous and more cyclic within managed zones containing a greater relative abundance of aspen and other hardwood host tree species, compared with the more conifer‐dominated Wilderness area. Yet, we also observed asynchronous outbreak dynamics across the study area, such that correlations with slower‐changing forest landscape variables varied starkly among outbreak pulses. Consequently, the strength of relationship between landscape variables and FTC outbreak patterns varied strongly through time—a result that may explain why short‐term studies yield conclusions that are at odds with one another. Our results speak to the importance of long time series, contrasting landscape structure, use of multivariate methods, and controlling for climatic variation when investigating the effects of forest landscape structure on the cyclic‐eruptive spatial dynamics for forest defoliators.

The Vision of Managing for Pest-Resistant Landscapes: Realistic or Utopic?

“…The diversity of findings in this regard demonstrates the complexities of disentangling the spatial dynamics of trophic interactions. Recent analysis of a long outbreak time series within a single heterogeneous landscape suggested that the results of shorter-term correlational studies may be misleading [ 38 ••]. This work found that outbreak dynamics are spatially and temporally heterogeneous and characterized by travelling waves.…”

“…This work found that outbreak dynamics are spatially and temporally heterogeneous and characterized by travelling waves. Portions of these waves may correlate well with some landscape attributes within a single outbreak, but the relationships are not consistent among outbreaks [ 38 ••]. These spatiotemporal outbreak patterns are influenced by host spatial structure.…”

Understanding and Modeling Forest Disturbance Interactions at the Landscape Level

“…Data quality and quantity degrade as one goes back in time; Inference of causal relationships not possible. Bebi et al, 2003;Howe and Baker, 2003;DeRose and Long, 2012;Sturtevant et al, 2014;Hart et al, 2015;Meigs et al, 2015;Robert et al, 2018Robert et al, , 2020 Prospective Empirical data used to parameterize models of disturbance mechanisms to make DI inferences.…”

“…Risk of overfitting, patterns may or may not correspond with underlying process; "black-box" data mining may limit interpretation. Fleming et al, 2002;Sturtevant et al, 2014;Seidl et al, 2016b;James et al, 2017;Mezei et al, 2017;Candau et al, 2018;Itter et al, 2019;Robert et al, 2018;Brice et al, 2020;Robert et al, 2020 trees to investigate consequent fire behavior, burn intensities, and consequent effects of those intensities related to elevated fuels on vegetation composition. Such studies can support the development of more process-based models enabling the simulation of disturbance interactions based on first principles (Figures 3, 5).…”

“…One common approach for investigating disturbance interactions is "retrogressive" (sensu Simard et al, 2011), where investigators reconstruct disturbances over a longer period of time, using a variety of data sources such as archived satellite imagery, air photos, dendrochronology, and disturbance records (e.g., Bebi et al, 2003;Howe and Baker, 2003;DeRose and Long, 2012;Sturtevant et al, 2014;Hart et al, 2015;Meigs et al, 2015;Robert et al, 2018Robert et al, , 2020. A challenge facing retrogressive approaches is that the quality of data defining underlying drivers, such as forest composition or age structure, generally degrades the further back in time one investigates (McKenzie and Perera, 2015;Robert et al, 2020) (Table 1). Conversely, empirical data defining a given disturbance legacy (e.g., fuel) are often used to drive models to infer future disturbance behavior (e.g., flame length) (DeRose and Long, 2009;Simard et al, 2011;Meigs et al, 2015;Seidl et al, 2016a).…”