Irruptive forest insects are a leading biotic disturbance across temperate and boreal forests. Outbreaks of forest insects are becoming more frequent and extensive due to anthropogenic drivers (e.g., climate and land-use), perhaps increasing the likelihood that forests will experience multiple insect-caused disturbances.

Across the fire-prone Douglas-fir forests of western North America, recent outbreaks of the western spruce budworm and Douglas-fir beetle have impacted large expanses of forests, with a higher degree of overlap than expected in some ecoregions. Outbreaks of both insects are positively related to host availability and exhibit density-dependent population dynamics that are affected by climate and weather.

Here, we leverage data from aerial detection surveys, estimates of host availability, climate and weather, and categorized fire severity to describe the spatial overlap between western spruce budworm and Douglas-fir beetle and assess: 1) how climate and host availability influence the biogeography of outbreaks; 2) how weather incites outbreaks; and finally, 3) how prior disturbances (fire and biotic) affect the subsequent outbreak likelihood of western spruce budworm and Douglas-fir beetle.

Models demonstrate that western spruce budworm and Douglas-fir beetle share similar predisposing drivers of outbreaks. Outbreaks of both insects were more likely to occur following warm weather, but only beetle outbreaks were more likely following drought. When controlling for differences in outbreak distribution and inciting factors, results indicate that both prior fire and interspecific disturbance altered the likelihood of subsequent insect-caused disturbance.

Specifically, Douglas-fir beetle outbreaks were more likely to occur for several years following low severity fire, but less likely otherwise. Prior defoliation, especially longer duration defoliation, increased the likelihood of beetle outbreak within stands and across the landscape. On the contrary, western spruce budworm outbreaks were less likely to occur following fire, while prior beetle activity dampened budworm outbreak likelihood for several years, then eventually increased outbreak likelihood.

Synthesis: Biotic-biotic disturbance interactions have the potential to amplify the incidence of insect-caused disturbance across sub-continental scales. Our findings highlight the need for future work on mechanistic linkages between biotic disturbance agents as well as the ramifications for forest trajectories and function.

Aerial surveys– To characterize the spatiotemporal patterns of Douglas-fir beetle and western spruce budworm outbreaks, we used aerial detection survey (ADS). ADS data are collected by aerial observers in fixed-wing aircrafts and include the likely damaging agent (species of insect/disease or abiotic factor), binned estimates of damage severity, and forest type. ADS data are the most accurate data describing the spatiotemporal patterns of specific forest insect outbreaks at a broad spatial extent across the western US; a recent assessment of the survey program found that 85% of randomly selected Douglas-fir beetle and 100% of western spruce budworm polygons were correctly identified based on ground-truthing in Oregon, California, and New Mexico (Coleman et al., 2018).

We summarized ADS data by calculating the presence/absence and severity of Douglas-fir beetle and western spruce budworm within a 1 km² grid. First, we listed presence when a grid cell contained any polygons or points that listed Douglas-fir beetle or western spruce budworm as the primary agent. Observed Douglas-fir beetle activity data were shifted by one year because reported tree damage generally represents bark beetle activity in the prior year (i.e., shifted from 1997-2021 to 1996-2020) (Meddens et al., 2012). Because the effects of defoliation are more immediate and readily visible, dates of observed western spruce budworm activity were unchanged (Senf et al., 2015). Second, we estimated Douglas-fir beetle and western spruce budworm outbreak severity using the ‘histogram matching’ method, which accounts for the changes in the ADS protocol over the study period (Egan et al., 2019, 2020; Hicke et al., 2020) (Supplemental Materials 1). We considered the maximum reported ADS severity for each grid cell-year combination (low, medium, or high) and simplified severity classes into low (“low”) or high (“medium” & “high”) due to the highly uneven distribution of reported severities.

Given that an important driver of spatiotemporal patterns of insect outbreaks is the population density within the surrounding landscape (Aukema et al., 2008; Howe et al., 2021; Senf et al., 2017; Simard et al., 2012), we estimated prior conspecific population pressure at 5 km (e.g., t-1). Five kilometers exceeds the dispersal distance of individual Douglas-fir beetles, which is generally less than 400 m (Dodds & Ross, 2002), but is well within the effective distance of landscape spatial correlation (i.e., nonparametric correlation function; Supplemental Materials 1). The dispersal distance of western spruce budworm is not well known (Flower et al., 2014), although dispersal distance is suggested to be roughly 1.3 km during outbreaks (Senf et al., 2017). Budworms are also capable of much longer dispersal flights as a similar species, eastern spruce budworm (Choristoneura fumiferana [Clemens, 1865]), can disperse more than 100 km within a season (Greenbank et al., 1980). In our analysis of landscape spatial correlation (Supplemental Materials 1), western spruce budworm outbreak populations were highly correlated at distances between 0-5 km with declining correlation between 5-10 km. Specifically, we used simplified inverse sigmoidal distance weighted sum of Douglas-fir beetle and western spruce budworm presence/absence, similar to other analyses of bark beetle population pressure (Hart et al., 2017; Howe, et al., 2022; Preisler et al., 2012). Estimates of prior conspecific population pressure were log(x+1) transformed for a better distribution of values.

Fire severity—We calculated estimates of burn severity by intersecting a 30 m categorized fire severity raster from the Monitoring Trends in Burn Severity (MTBS) Project with our 1 km² grid. For each 1 km² cell, we summed the number of 30 m pixels within each fire severity category (“greener”, “low”, “medium”, “high”, “unburned”, “masked” [i.e., by clouds]), multiplied the area of each by 0.1 for low, 0.25 for medium, or 0.75 for high severity, which approximately correspond to the amount of overstory mortality typically associated with each burn severity class (e.g., low: < 25% mortality; medium 25-75% mortality; high > 75% mortality). For each 1 km² cell, we then classified fire severity into “low” or “high” with a breakpoint of 0.5 (approximately less than or greater than 50% basal area loss). Fire severity breakpoints were selected to approximate the bins used in the ADS data to classify insect damage and loss of forest basal area. A comparison of our binning method relative to the 30 m categorized fire severity classes is provided (Supplemental Materials 1).

Host availability–To quantify host basal area, we aggregated 2002 and 2012 30 m rasters of Douglas-fir basal area to 1 km² by intersecting with our 1 km² grid. For each 1 km² pixel, we calculated the area-weighted mean, transformed the units into m²/ha, and performed a log transform (i.e., log[m²/ha+1]) for both the 2002 and 2012 rasters. We then used the maximum estimate of Douglas-fir basal area for each grid cell because the 2012 data did not include all basal area lost due to Douglas-fir beetle outbreaks in the early 2000s. We also estimated spruce and fir basal area based on the 2012 data to examine how incorporating alternative western spruce budworm hosts altered western spruce budworm outbreak distribution (Supplemental Materials 2).

Climate—We upscaled 800 m rasters of thirty-year climate normals (1961-1990) from ClimateNA (T. Wang et al., 2016) by intersecting with the 1 km² grid and calculated the mean across each grid cell. We used 1961-1990 as our reference time-period for climate because Douglas-fir trees are long-lived and there have been recent significant increases in temperature across the western United States. Thus, 1961-1990 represents a reference for conditions at the beginning of our study, which spans 1996-2021. In total, we considered 17 climatic normal variables (Supplemental Materials 3) for our analyses. Annual weather rasters were upscaled as above and we calculated weather as either the deviation from climatic normal over a three-year period (t-0:t-2) for temperature (˚C) or the proportional deviation from climatic normal for precipitation (proportion based on cm). Three-year weather deviations were selected for two reasons: A) visible outbreaks are the product of  multi-year population expansions; and B) it can take several years for decreases in precipitation to impact tree vigor and non-structural carbohydrate pools (Peltier et al., 2023). Proportional deviation in precipitation was used to control for the large bioclimatic differences across our study area. As an example, for a given grid cell in the year 2000, we calculated the difference from the 1961-1990 climatic normal over the 1998-2000 time-period. We considered 7 weather variables for our analyses (Table 1; Supplemental Materials 3).