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Summer 2016

Climatic Drivers of Western Spruce BudwormOutbreaks in the Okanogan HighlandsTodd Ellis

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CLIMATIC DRIVERS OF WESTERN SPRUCE BUDWORM

OUTBREAKS IN THE OKANOGAN HIGHLANDS

By

Todd M. Ellis

Accepted in Partial Completion

of the Requirements for the Degree

Master of Science

Kathleen L. Kitto, Dean of the Graduate School

ADVISORY COMMITTEE

Chair, Dr. Aquila Flower

Dr. Andy Bunn

Dr. Michael Medler

MASTER'S THESIS

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allowed without my written permission.

Todd M. Ellis

June 6, 2016

CLIMATIC DRIVERS OF WESTERN SPRUCE BUDWORM

OUTBREAKS IN THE OKANOGAN HIGHLANDS

A Thesis

Presented to

The Faculty of

Western Washington University

In Partial Fulfillment

Of the Requirements for the Degree

Master of Science

by

Todd M. Ellis

June 2016

iv

ABSTRACT

The western spruce budworm is recognized as the most ecologically- and economically-

damaging defoliator in western North America. Like other defoliating insects, the western spruce

budworm consumes the needles of host tree species like the Douglas-fir (Pseudotsuga menziesii

(Mirb.) Franco), causing limb and tree mortality, regeneration delays, and reduction in tree

growth rates. Synchronous western spruce budworm outbreaks can occur over much of a host

species' range, and we need a better understanding of the mechanisms driving the species’

population dynamics in order to predict climate change effects, mitigate ecological and resource

management impacts, and understand ecosystem dynamics. To contribute to our understanding

of this species' outbreak dynamics, we used dendrochronological methods to reconstruct

multicentury outbreak records for four sites in the Okanogan Highlands of central Washington

State. By comparing these reconstructed records to historical and reconstructed regional drought

data, we were able to test drought history as a potential driving factor of western spruce

budworm population dynamics, as well as see how human impact may be affecting these

population dynamics. Outbreak synchrony was found to increase after the late 19th century,

possibly due to anthropogenic factors. This change is more readily apparent as the intensity of

outbreaks increases. Drought records show that outbreaks tend to occur as the drought severity

decreases, and moisture availability increases. As the variability of climate conditions is

projected to increase, trending towards warm and dry conditions, the intensity and frequency of

high-intensity western spruce budworm outbreaks could likely increase as well.

v

ACKNOWLEDGMENTS

I would like to thank my advisor, Dr. Aquila Flower, for her unending enthusiasm, guidance, and

mentorship both in the field and lab. I would also like to thank my committee, Drs. Andy Bunn

and Michael Medler for their support and feedback in building this project. Paul Nash and

Connie Mehmel of the USFS both contributed essential knowledge of the Okanogan Highlands

study area. Dr. Daniel Gavin of the University of Oregon provided important feedback on our

methods. Lastly, this project would not have been possible without the field and lab assistance of

Marissa Bhatnagar, Branden Rishel, Christopher Zemp, Venice Wong, Ryan Schumacher,

Shelby Van Arnam, Demian Estrada, Derek Huling, and Dustin Greaves.

vi

TABLE OF CONTENTS

Abstract……………………………………………………………………………………….…..iv

Acknowledgments…………………………………………………………………………….…...v

List of figures and tables………………………………………………………………………..viii

1. Introduction……………………………………………………………………………………..1

2. Methods………………………………………………………………………………………....9

2.1 Study area……………………………………………………………………………..9

2.2 Sampling strategy……………………………………………………………………..9

2.3 Sample preparation and laboratory analysis…………………………………………11

2.4 Outbreak reconstructions…………………………………………………………….12

2.5 Statistical analysis……………………………………………………………………15

2.5.1 Outbreak characteristics……………………………………………………15

2.5.2 Outbreak synchrony among sites…………………………………………..15

2.5.3 Climate-outbreak associations……………………………………………..16

3. Results…………………………………………………………………………………………18

3.1 Dendrochronological characteristics and Outbreak histories……………………..…18

3.2 Inter-site outbreak synchrony………………………………………………………..19

vii

3.3 Climatic influences on outbreaks…………………………………………………….20

4. Discussion……………………………………………………………………………………..22

4.1 Outbreak histories……………………………………………………………………22

4.2 Inter-site outbreak synchrony and driving factors…………………………………...25

4.3Outbreaks and climatic variability……………………………………………………28

5. Conclusion…………………………………………………………………………………….32

6. Bibliography…………………………………………………………………………………..34

7. Figures and Tables…………………………………………………………………………….50

viii

LIST OF FIGURES AND TABLES

TABLES

Table 1 Site and chronology characters for host and non-host sites……………………...50

Table 2 Pearson cross-correlation coefficients between sample sites and climate data….51

Table 3 Outbreak statistics for host sites…………………………………………………52

Table 4 Changes to outbreak dynamics between the early and late periods……………...53

Table 5 Paired, two-sample t-tests for PDSI values over different outbreak conditions…54

FIGURES

Figure 1 Map of host and non-host sites in the Okanogan Highlands, Washington………55

Figure 2 Percent of trees recording outbreaks between 1685 and 2014…………………...56

Figure 3 Modified Ripley’s K-function for moderate initiation and cessation dates…...…58

Figure 4 Modified Ripley’s K-function for all outbreak years with moderate intensity…..59

Figure 5 Modified Ripley’s K-function for all outbreak years with high intensity……….61

Figure 6 Modified Ripley’s K-function for all outbreak years with very high intensity….63

Figure 7 Superposed epoch analysis site summary using reconstructed PDSI……………65

Figure 8 Superposed epoch analysis using reconstructed PDSI with regional records……67

Figure 9 Superposed epoch analysis site summary using historical PDSI…………….......68

Figure 10 Superposed epoch analysis using historical PDSI with regional records………..69

1. INTRODUCTION

Western spruce budworm (Choristoneura occidentalis Freeman) is recognized as the

most ecologically- and economically-damaging defoliating insect in western North America

(Fellin & Dewey 1986). Regionally synchronous, decade-long outbreaks over large regional

areas lead to widespread ecological resource impacts. However, the causal mechanisms driving

this species’ outbreak patterns and population dynamics remain under-explored, with results

often pointing to contradictory mechanisms. An understanding of the western spruce budworm’s

(WSB) population dynamics is necessary in order to understand ecosystem dynamics, predict

climate change effects, and mitigate ecological and resource management impacts (Swetnam &

Lynch 1993; Swetnam et al. 1995; Ryerson et al. 2003; Campbell et al. 2005; 2006; Maclauchlan

et al. 2006; Speer 2010; Alfaro et al. 2014; Flower et al. 2014). Multi-century records are needed

to establish accurate outbreak histories and shed light on climatic drivers of WSB’s outbreak

dynamics. Observational records of WSB activity are only available back to the mid-20th

century, but variations in the width of annual tree rings can serve as a proxy record of WSB

defoliation. Dendrochronological records have been used to reconstruct multi-century histories

of WSB outbreak dynamics in the American Southwest (Swetnam & Lynch 1989; 1993), central

Rocky Mountains (Ryerson et al. 2003), and the Pacific Northwest, including British Columbia

(BC) and the states of Montana, Idaho, and Oregon (Swetnam et al. 1995; Flower et al. 2014a;

Axelson et al. 2015). A prominent gap in the spatial coverage of these outbreak records exists in

northern Washington State. In this paper, we present a dendrochronological reconstruction of

2

WSB outbreaks in Washington State’s Okanogan Highlands region.

The WSB consumes buds and foliage of host trees, with a preference for current-year

spring growth, causing reduction in growth rates, regeneration delays, and limb and tree

mortality after several years of repeated defoliation (Alfaro et al. 1982; Van Sickle et al. 1983;

Fellin & Dewey 1986). While Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is the WSB’s

principal host species, WSB can also thrive on the needles of true fir (Abies) species, including

grand fir (Abies grandis (Dougl. ex D. Don) Lindl.), white fir (Abies concolor (Gord. & Glend.)

Lindl. ex Hildebr.), subalpine fir (Abies lasiocarpa Hook.), and corkbark fir (Abies lasiocarpa

var arizonica (Merriam) Lemm.). Additionally, species such as Engelmann spruce (Picea

engelmannii Parry ex Engelm.), western larch (Larix occidentalis Nutt.), blue spruce (Picea

pungens Engelm.), and white spruce (Picea glauca (Moench) Voss) are occasionally susceptible,

with other typically non-host coniferous species only susceptible under extreme circumstances

(Furniss & Carolyn 1977; Fellin & Dewey 1986). Repeated outbreaks ultimately modify the

composition and structure of forests, redistributing the biomass and resources of susceptible host

stands by removing photosynthetic tissue and reducing the local carbohydrate supplies necessary

for continued growth (Alfaro et al. 1982; Ferrell & Scharpf 1982; Ryerson et al. 2003; Bulaon &

Sturdevant 2006; Campbell et al. 2006).

Defoliation occurs during the species’ second instar stage between May and June (Fellin

& Dewey 1986). WSB larvae feed on host trees’ needles, staminate flowers, and developing

cones, leading to reduced seed production and regeneration capabilities (Fellin & Dewey 1986).

Young stands and seedlings, in particular, suffer increased mortality rates and stem deformities

following infestations of at least three years, with the level of damage directly related to larval

3

density and the infestation duration (Fellin & Dewey 1986; Maclauchlan et al. 2006). Mature

host stands are most at-risk for suffering topkill and stem deformities, but may also be impacted

by increased mortality rates in severe outbreaks (Fellin & Dewey 1986). All infested host stands

suffer a loss of biomass, and may have increased susceptibility to subsequent insect outbreaks

and pathogens (Alfaro et al. 1982; Ferrell & Scharpf 1982; Hadley & Veblen 1993; Woods et al.

2010).

WSB outbreaks tend to occur synchronously over much of the primary host species’

range, affecting forest stands from southern BC, Canada, to the southern Rocky Mountains in

New Mexico, USA (Fellin & Dewey 1986). Large regions of synchronous or near-synchronous

WSB outbreaks are usually attributed to one or more of the following factors: adult moth

dispersal, exogenous stochastic factors such as climate, or trophic interactions with similarly-

synchronous or mobile populations (Myers 1998; Williams & Liebhold & Kamata2000;

Liebhold et al. 2004; Cooke et al. 2007). Dispersal capabilities strongly influence synchrony of

population fluctuations at finer spatial scales (i.e., under 200 km), whereas climatic controls may

be more a more important driver of synchrony at coarse spatial scales, though the mechanisms

behind observed patterns of synchrony are still not well understood (Peltonen et al. 2002).

Local weather conditions can affect WSB populations, particularly during spring

emergence. WSB populations emerge prior to their hosts' annual bud-flushing, and rely on a

mixture of the previous years' needles, dispersal to areas with current-year bud-flush, or,

preferentially, pollen cones produced mostly by older trees early in the season, that bridge the

gap between spring emergence and bud flush (Blais 1952; McGugan 1954; Greenbank 1963;

Sanders 1991; Shepherd 1992; Nealis & Lomic 1994; Trier & Mattson 1997; Nealis & Nault

4

2005). If temperatures are too warm, they may promote an earlier emergence for WSB larvae,

which leads to local population reductions when larvae either starve or disperse to find food

(Thomson & Moncrieff 1982; Shepherd 1983; Chen et al. 2003; Thomson & Benton 2007).

Dispersal of larvae is particularly risky in relatively low density stands, where larvae must

traverse greater distances between tree crowns in order to find suitable food (Miller 1958; Nealis

& Lomic 1994; Nealis & Régnière 2009). Similarly, temperatures that are too cold can cause

high mortality rates by freezing larvae or the foliage they feed on (Fellin & Schmidt 1973; Wulf

& Cates 1987).

During outbreak conditions, earlier WSB instar emergence leads to a rapid decrease in

pollen cone production (Blais 1952; Nealis et al. 2003; Hughes et al. 2014), which Douglas-fir

compensates for by producing more vegetative buds (Shepherd 1992; Nealis & Régnière 2004).

Despite this, a higher number of WSB emerging earlier in the season still rapidly reduces needle

density (Nealis & Régnière 2009). Late-feeding WSB are thus subject to reduced rates of

survival and fecundity, as well as dangerous weather conditions like high winds or storms

(Campbell 1993).

In the Pacific Northwest, WSB infestations are frequent in many coniferous stands, with

insect populations often continuously present at low endemic levels (Swetnam et al. 1985; Fellin

& Dewey 1986; Wickman 1992). Radial growth impacts from WSB outbreak defoliation tend to

last, on average, between 11 and 15 years (Lynch 2007). Aerial survey records suggest even

shorter intervals of WSB outbreaks, lasting as few as one to two years in some regions (USDA

Forest Service 2014). Quiescent period durations also vary across impacted regions, with an

average of 32 to 40 years between outbreaks (McCune 1983; Anderson et al. 1987; Swetnam &

Lynch 1989; 1993; Swetnam et al. 1995; Weber & Schweingruber 1995; Ryerson et al. 2003;

5

Campbell et al. 2006; Lynch 2007).

Recent records suggest the Pacific Northwest has been impacted by a widespread

outbreak since the 2009 season (Man 2012), though the WSB may be undergoing population

crashes in many regions. For 2013, Oregon State reported only 129.5 hectares (ha) of defoliated

vegetation, down from 2012's 32,022.8 ha, and Washington State reported 72,794.9 ha

defoliated, down from 206,871.3 ha the previous year, and the lowest recorded since 2003

(Jenkins 2015). Isolated mid-elevation Washington forests like those of the Okanogan Highlands

remained heavily infested throughout 2013, and even continued a 3-year increasing trend across

Colville National Forest (Jenkins 2015). BC's central and southern interior regions house a

similar, connected topography and climate to Washington's mid-elevation forests. Washington's

Okanogan Highlands and nearby forested regions such as the Kamloops and Cariboo Forest

Regions have experienced similar outbreak histories according to historical records and have

sustained heavy outbreak conditions in recent years (Alfaro et al. 2014; Axelson et al. 2015).

Like much of the Western US, overall WSB defoliation damage appears to be decreasing in the

BC regions. Aerial survey records for 2014 suggest a total of 44,608 ha were defoliated by the

WSB with a minor margin for error, the lowest recorded level since 2000's 22,851 ha (Westfall

& Ebata 2014).

Over the course of the 20th century, some regions have shown increasing outbreak

synchrony, severity, and/or intensity, possibly as a result of human impact (Anderson et al. 1987;

Swetnam & Lynch 1989; 1993; Swetnam et al. 1995; Ryerson et al. 2003; Campbell et al. 2006;

Flower et al. 2014a; 2014b). These changing outbreak dynamics may be linked to expansion of

the extent and predominance of WSB’s host species due to historical human impacts. Selective

6

harvesting of competing species, fire exclusion, and livestock grazing are thought to have

favored the establishment of WSB’s host species (Anderson et al. 1987; Wickman 1992; Mutch

et al. 1993; Hessburg et al. 1994; Keane et al. 2002). Human land-use habits have led to a dense

multilayered canopy structure, promoting the growth of relatively shade-tolerant species like

Douglas-fir. The increased density of sub-canopy trees provides additional habitat for the WSB

(Maclauchlan & Brooks 2009)

Historical documents in the Pacific Northwest record WSB outbreaks as early as 1909 in

the Vancouver Forest Region of BC (Harris et al. 1985). For the western United States, annual

Insect and Disease Surveys (IDS) have been provided by the USFS since 1947, though the

accuracy for outbreak records is questionable until to more recent decades (Fellin & Dewey

1986; Williams & Birdsey 2003; Speer 2010; USDA Forest Service 2014). Historical outbreak

records report a widespread, untreated outbreak period from 1942–1948 (Brookes et al. 1987).

Aerial survey records did not start until the IDS was initiated in 1947, and recorded no outbreaks

between the widespread 1940s outbreaks and smaller, site-specific occurrences in the 1970s

(USDA Forest Service 2014). More accurate ground survey records didn't monitor WSB

outbreaks until 1976–1977, and only monitored the western border of the Okanogan Highlands

region near Mt. Hull (McComb 1979). These outbreak dates were not reflected in aerial survey

records. WSB populations were noted as present throughout the Okanogan National Forest and

neighboring Colville National Forest in 1975 and 1976, but were not closely monitored for

management (USDA Forest Service 1977). McComb’s (1979) data followed widespread DDT

treatment for a severe Douglas-fir tussock moth (Orgyia pseudotsugata McDunnough) outbreak

from 1970 to 1974, notable for being the last official use of DDT on USFS land (Mehmel,

7

personal communication 2015), as well as treatment with malathion in 1976 and zectran in 1977.

Climatic variables are thought to be a primary driving force of WSB population dynamics

(Campbell 1993; Swetnam & Lynch 1993; Swetnam et al. 1995; Murdock et al. 2013; Flower et

al. 2014a). Due to this, climate change is expected to affect the range of WSB and associated

outbreak characteristics like frequency and intensity (Swetnam & Lynch 1993; Murdock et al.

2013). A warming climate could potentially increase the area susceptible to WSB infestations by

moving WSB populations to higher elevations and latitudes as host tree ranges shift upwards and

northwards (Logan et al. 2003; Maclauchlan et al. 2006; Woods et al. 2010; Murdock et al.

2013). Fluctuations in moisture availability are seen as the most important variable in effecting

changes to WSB population dynamics (Hard et al. 1980; Thomson et al. 1984; Campbell 1993;

Swetnam & Lynch 1993; Ryerson et al. 2003; Flower et al. 2014a). WSB response to climate

variables is inconsistent across many regions, however. For instance, Swetnam & Lynch (1993)

suggested high spring precipitation as an influencing factor in outbreak timing in Colorado,

while Flower et al. (2014a) found an increase in moisture stress was necessary in initiating

outbreaks in Oregon, Idaho, and Montana. The potential range of the WSB includes a variety of

climatic zones over multiple state lines, with controlling climatic variables likely differing based

on local- and regional-level climate. Three hypotheses have been proposed to explain the

observed relationships between insect populations and moisture availability: The plant stress

hypothesis proposed that sustained abiotic factors like drought fuel insect outbreaks by

concentrating nutritional quality in foliage (White 1984; Mattson & Haack 1987). The plant

vigor hypothesis is a competing hypothesis suggesting high moisture availability can sustain

outbreaks by improving both the quality and quantity of foliage (Price 1991). The most recent

hypothesis proposes instead that a nonlinear combination of factors, like temporal variability in

8

moisture stress, would be required to initiate and subsequently sustain insect outbreaks (Huberty

& Denno 2004; Mody et al. 2009).

Multi-century outbreak reconstructions of at least three centuries are necessary to better

understand the population dynamics of the WSB. The development of additional multi-century

reconstructions contributes to the continued development of forest management strategies that

can cope with the economic and ecological impacts of defoliating insects (Shepherd 1994). The

purpose of this study is to uncover the regional history of WSB outbreaks in the Okanogan

Highlands region, connecting an important geographic gap to surrounding reconstructed

outbreak records (e.g., Flower et al. 2014a; Axelson et al. 2015). Additionally, we characterize

the frequency, periodicity, levels of synchrony, and intensity of the region's outbreak history.

Using these data with historical and reconstructed climate records, we enhance our

understanding of how climate variables like moisture availability contribute to the WSB's

population dynamics, and the importance of understanding how our changing climate will likely

lead the changes we are witnessing in WSB outbreak patterns.

9

2. METHODS

2.1 Study Area

We collected samples at seven sites in the Okanogan Highlands of Okanogan National

Forest in August 2014 (Fig. 1). The Okanogan Highlands are characterized by an arid, shrub-

steppe environment, with vegetation dominated by Douglas-fir, with lesser amounts of

ponderosa pine (Pinus ponderosa Douglas ex P. & C. Lawson), western larch, and grand fir

(McNab & Avers 1994). Elevations at our sites ranged between 1000 and 1600 m. The

geological setting of most study sites was gneiss bedrock, with the easternmost host site

identified as basalt (Lasmanis & Cheney 1994). We used ClimateWNA to find instrumental,

historical climate conditions associated with each site's coordinates. Using the 30-year climate

normals for 1981–2010, the mean center of our sites reports average temperatures between from

-6.1°C to 17.1°C in the coldest and warmest months, respectively; annual precipitation was

recorded as 433 mm, with 160 mm occurring during summer months (Daly et al. 2002; Wang et

al. 2012).

2.2 Sampling Strategy

We collected samples at paired host and non-host sites. Samples collected at non-host

sites were used to create a control chronology to compare host chronologies against. This

approach allowed us to isolate the defoliation signal contained in host tree-ring chronologies. We

chose Douglas-fir as a host species due to its wide range in the Okanogan Highlands and its

susceptibility to WSB outbreaks (Little 1971; Mason et al. 1997). We chose ponderosa pine as

our non-host species. Douglas-fir and ponderosa pine have overlapping geographic ranges and

similar responses to climate, but ponderosa pine is rarely defoliated by the WSB (Critchfield &

10

Little 1966; Fritts 1974; Swetnam et al. 1985; Swetnam 1987; Watson & Luckman 2002; Littell

& Tjoelker 2008; Chen et al. 2010; Griesbauer & Green 2010).

We selected potential study areas with a history of frequent WSB outbreaks based on

annual USFS Insect and Disease Survey data (USDA Forest Service 2014, Williams and Birdsey

2003). Within those potential study areas, we selected specific study sites using satellite imagery

and in-situ evidence. We selectively targeted sites for their distance from active forest service

roads and from one another, with significant topographic features such as mountains or valleys

ideally separating sites. We targeted stands with multi-century records, with the oldest trees

ideally dating to at least 300 years (Table 1). We avoided host and non-host stands with

extensive recent disturbances such as logging or fire damage.

We extracted two increment cores per tree. We avoided reaction-wood by coring parallel

to the slope contour at 1.3 meters above ground, except where impossible due to difficult

topography (Speer 2010). We sampled between fifteen and twenty trees from each of our four

host stands in order to adequately cover the regional history (Table 1). Within sites, we

selectively-sampled based on visual assessment, using criteria that included old-age cues such as

flattened tops, spiral-grained bark, large lower limbs, and the diameter at breast height (DBH)

being at least 40 cm. We avoided samples that included any indication of significant damage

(e.g., fire scars) that could potentially distort the growth patterns.

For non-host stands, we sampled between six and seventeen trees from three sites to

maximize the visible impacts of defoliation. To produce the longest possible record, we collected

non-host samples with the intention of creating a single regional chronology for use with each

host site. Monospecific non-host stands were preferentially targeted in order to avoid any growth

release from species affected by defoliation or competition (Swetnam et al. 1995). Old-growth

11

ponderosa pine stands are, however, aggressively-maintained by the USFS, which made locating

suitable non-host stands difficult (Nash, personal correspondence 2014). Because of this, the age

of non-host stands superseded the importance of monospecificity and we include six trees from

our Turner Lake site despite the presence of Douglas-fir.

2.3 Sample preparation & laboratory analysis

We prepared our samples using standard dendrochronological techniques (Stokes &

Smiley 1968; Fritts 1976; Speer 2010). We dried and glued core samples to wooden core mounts

before surfacing with 120-, 220-, 320-,400-, and 600-grit sandpaper. With polished samples, we

first visually crossdated each sample from the bark inwards using a microscope. We then

scanned and measured to the nearest 0.001 mm using Cybis' CooRecorder and CDendro software

(Larsson & Larsson 2014). We used CDendro to visually identify known outbreak periods

(Swetnam et al. 1985; 1995), and create master chronologies for each host and non-host site.

CDendro allowed for the creation of a master chronology to be used with any sample

measurements, minimizing the errors caused by our visual interpretation (Larsson & Larsson

2014).

The R package dplR contained functions to generate skeleton plots and cross-correlate

our time series with a master chronology, which were necessary in locating errors with our

measurements and missing rings prior to detrending (Bunn 2008; Bunn et al. 2012; R Core Team

2013; Bunn et al. 2015). We used an additional ponderosa pine chronology from the

International Tree-Ring Data Bank (ITRDb) archives during non-host crossdating for years prior

to 1976 (Brubaker 1976). This archived chronology was not used in our final analyses because

12

we needed chronologies that would cover late 20th century outbreak periods.

We used the dplR package to detrend raw measurements for host and non-host sites using

a 100-year cubic smoothing spline (Cook 1985; Bunn 2008; Bunn et al. 2012). This method of

standardization ensures decadal impacts like WSB outbreaks will be maintained, while

correcting for year-to-year age-related growth trends (Fritts 1976; Cook 1985; Cook & Holmes

1986; Swetnam & Lynch 1993). A few samples with abnormal growth trends, including overly-

complacent or parasitized specimens, were removed from the study prior to detrending. Resultant

ring-width indices were averaged together by tree, and used to create mean site chronologies for

host and non-host sites. A principal components analysis was run on the standard and residual

non-host chronologies to extract the common, presumably climatic, signal shared by the non-host

trees. We chose the first principal component of our three standard non-host master chronologies

for our outbreak reconstructions, because it explained the highest variance (81%) and correlated

most strongly with our host chronologies.

For climate records, we used both instrumental, historical climate division data for

periods after 1895 (Wang et al. 2012), and reconstructed Palmer Drought Severity Index (PDSI;

Palmer 1965) records for complete multicentury series through 2003 (Cook et al. 1999; 2004).

We conducted a Pearson’s cross-correlation analysis on the host and non-host chronologies

against regional instrumental and reconstructed records to determine whether the chronologies

expressed the similar climate responses necessary for outbreak reconstructions (Fritts 1974;

Cook 1999; Cook et al. 2004; Wang et al. 2012).

2.4 Outbreak Reconstructions

We visually compared ring-width measurements with historical records of known

outbreak periods within the Okanogan Highlands and nearby dendrochronological reconstructed

13

outbreak dates (e.g., Campbell et al. 2006; Flower et al. 2014a; Axelson et al. 2015). Like

crossdating, this visual comparison provided us with a first step to identifying periods of growth

suppression at tree-level. Outbreaks are often visible as long-term growth suppression periods in

the host tree chronologies that are not apparent in the regional non-host chronology. To

statistically-reconstruct outbreak records, we first subtracted climatic noise from each host tree

using the equation:

𝐶𝑜𝑟𝑟𝑒𝑐𝑡𝑒𝑑 𝐼𝑛𝑑𝑒𝑥 = 𝐼ℎ𝑡 −𝜎ℎ

𝜎𝑛

(𝐼𝑛𝑡 − 𝐼�̅�)

where Ih is the host trees' ring-width index for each individual year (t), σh and σn are the standard

deviations of the individual host tree series and the regional non-host series' common period, In is

the non-host control index for each year (t), and Īn is the mean for the non-host index for the

common period. The output of this equation created a new value for each year of growth across

host trees, where positive or negative values represent growth above or below the expected

growth from climatic factors alone (Nash et al. 1975; Swetnam et al. 1985; Campbell et al.

2006). Similarities between the corrected site indices during their shared common periods

(1719–2014 for three sites, 1796-2014 for all four) were checked using Pearson's correlation

coefficients.

To develop a regionally appropriate set of criteria for identifying WSB outbreaks, we

normalized the corrected tree series and identified outbreak-length periods of low growth. We

did not record non-consecutive years of positive growth as outbreak interruptions, as non-

consecutive positive growth years are common within outbreak periods (Swetnam et al. 1995;

Campbell et al. 2006). Minimum thresholds for WSB outbreak length vary by region, typically

ranging between four to eight years of sustained below-average growth, with at least one year of

growth at least 1.28 standard deviations below the long-term mean ring width (Swetnam et al.

14

1995; Flower et al. 2014a). Another defoliating insect, the Douglas-fir tussock moth (Orgyia

pseudotsugata McDunnough), can create similar outbreak patterns in Douglas-fir ring width

records, but their outbreaks last only three years, which makes four-year outbreak durations the

shortest desirable outbreak length for separating the WSB signal from similar defoliating insects

(Brubaker 1978; Brubaker & Greene 1979; Wickman et al. 1980; Wickman 1986; 1992;

Swetnam et al. 1995; Mason et al. 1997). We tested a minimum outbreak duration criterion of

between four and eight years to resolve which most accurately covered historical outbreak

records, with four years being deemed the most reflective of historical outbreaks. A minimum

growth reduction severity criterion of 1.28 standard deviations below the long-term mean was

used for all reconstructions. Outbreak periods were identified based on these criteria for each

individual tree, resulting in an annually resolved binary record of outbreak / non-outbreak

conditions.

We standardized the binary, tree-level outbreak data into the percentage of a site's sample

population reporting infestation year-to-year (e.g., Campbell et al. 2006). Since outbreak

reconstructions tend to regularly report some level of tree infestation reflecting endemic WSB

populations, small-scale population changes, or background noise, we explored multiple

outbreak intensity thresholds between 30-80%. We compared the resultant outbreak time series

with historical air and ground survey records, with which a 40% threshold best identified the

start of moderate outbreak conditions (McCune 1979; Westfall & Ebata 2014; Axelson et al.

2015; Jenkins 2015; Mehmel, personal correspondence 2015). Additionally, we used thresholds

of 60% and 80% to identify high and very high outbreak intensities, respectively, which could

gauge how intensity patterns have changed over the entire time series. The resultant corrected

chronology provides measures of synchrony (the amount of trees per site that show an ongoing

15

infestation), duration (the length of time when growth was below the corrected indices' potential

growth), and intensity (the maximum growth reduction in an outbreak) of outbreak disturbances

within and between stands (Swetnam & Lynch 1993). We defined a chronology for regional

outbreaks by periods in which at least two of our four sites recorded coincident outbreaks for a

minimum of two back-to-back years.

2.5 Statistical Analysis

2.5.1 Outbreak Characteristics

We averaged outbreak duration and intensity for site and regional outbreak records, and checked

for temporal changes by separating both duration and intensity data by century. Since the use of

centuries as a break-point feels arbitrary, we also divided the data into two similar-sized groups:

Before 1870 and after 1869. This separation would also roughly coincide with western expansion

and the introduction of controlled forestry practices. As historical records prior to 1970 either

aren't reliable or don't exist for our study area, this breakpoint provides a loose representation for

when Euro-American settlers’ influences may have begun most affecting regional outbreak

patterns (Johnson & Ross 2008). Kruskal-Wallis nonparametric ANOVA was performed on the

normalized corrected indices to test for differences between Douglas-fir’s growth response

during and outside of outbreak conditions (Burt et al. 2009).

2.5.2 Outbreak Synchrony Among Sites

We assessed the level of synchrony between site events using Pearson's correlation

coefficient. Despite the high autocorrelation inherent in synchrony records, which makes

estimation of statistical significance unreliable, the correlation coefficients can be used as an

approximate index of sites' outbreak synchrony over time (Buonaccorsi et al. 2001). This simple

analysis was conducted on the percent of each site recording outbreaks across our four sites'

16

common period (1796–2014). We also separated the common period around the 1870 breakpoint

to get a rough estimation of how site interactions may have changed before and after major

human impacts began affecting the region. We used a modified one-dimensional Ripley's K-

function (Ripley 1976; Gavin et al. 2006) to test whether discrete outbreak events were

independent of one another over increasing bidirectional temporal lags. Years of outbreak

occurrences, initiations, and cessations for our three oldest sites' common period were input into

K1D v1.2 software (Gavin 2010). The youngest site, SMD, was removed from this method in

order to retain as much of our regional record as possible without sacrificing too much of our

sample size. This method uses designated temporal step size—e.g., 1 year—to check for co-

occurrence of outbreaks between any of the sites over increasingly long temporal windows until

the bidirectional window is half the length of the total record length, with the resultant K and L

functions providing a measure of outbreak synchrony or asynchrony over increasing temporal

scales. To test for statistical significance, we ran 1000 simulations with a .95 confidence

envelope using a circular randomization, with random year data added to all site records. We

also ran this to test for differences between our early and latter periods for all three of our

intensity thresholds -- i.e., moderate (40%), high (60%), and very high (80%).

2.5.3 Climate-Outbreak Associations

To identify climatic conditions associated with WSB outbreak initiations and cessations,

we used superposed epoch analysis and t-tests at both stand- and region-levels (Lough & Fritts

1987; Grissino-Mayer 2001). PDSI records were used as a measure of summer (i.e., between

June and August) moisture stress using soil type, precipitation, and temperature (Palmer 1965).

We tested both historical (1895–2014) and reconstructed (1685–2003) PDSI (Cook et al. 2004)

records, as well as historical precipitation data for water-years (i.e., previous October to current

17

September) and growing years (April to September of the current year). Instrumental records for

the Okanogan Big Bend (Climate Division 7) area were provided by the National Oceanic and

Atmospheric Administration's (NOAA) National Centers for Environmental Information (NOAA

2015). Cook et al.'s (1999; 2004) multi-century, gridded PDSI reconstruction data was built

from tree-ring chronologies provided by the ITRDb. The North American reconstruction network

is available for 2.5° by 2.5° gridcells, with our regional data drawn from grid 43 (Cook et al.

1999; 2004).

We conducted paired, two-sample t-tests to test for differences between PDSI data

associated with outbreak conditions against non-outbreak conditions; PDSI data two years before

outbreaks against both PDSI during outbreak and non-outbreak conditions; and PDSI data two

years before outbreaks and during outbreaks against PDSI during non-outbreak conditions. We

used superposed epoch analysis to identify patterns of climatic conditions associated with

outbreak initiation and cessation dates. Superposed epoch analysis uses event years with time

series data and designated temporal lags to test for significant departures from the mean

(Grissino-Mayer 2001). Event years for our study were designated as either outbreak initiation or

cessation dates. We defined initiation dates as the first of two or more consecutive years

recording an outbreak following a gap of at least two years without recorded outbreaks, and

cessation dates as the first of at least three years of non-outbreak conditions following an

outbreak (Flower et al. 2014a). A window of five years before and after host site's event years

was used in conjunction with both historical and reconstructed climatic data to check for

potential outbreak associations. Statistical significance was assessed by performing 1000 Monte

Carlo simulations using dplR (Bunn 2008; Bunn et al. 2012).

18

3 RESULTS

3.1 Dendrochronological characteristics and Outbreak histories

A total of 69 host trees and 32 non-host trees were sampled over our six sites. Our

regional non-host chronology dated to 1685. Host sites had records from at least two trees

starting between 1685 and 1796 (Table 1). Interseries correlation (Pearson's r; p < 0.01) for our

host sites ranged between 0.650 and 0.780, while non-host sites ranged between 0.627 and 0.760.

Correlation coefficients (Pearson’s r) between our chronologies (both standard host site and

regional non-host) and climate data supported the use of our sites for outbreak reconstructions

(Table 2). All of our host site chronologies, as well as our regional non-host chronology, reported

significant (p < 0.05), positive relationships with reconstructed and historical PDSI, as well as

water-year precipitation for the current and preceding year. The relationship with temperature

returned less significance, although the current growing-year’s temperature had a significant,

negative relationship with all but one of our host sites (Table 2).

Outbreak durations ranged from two to 19 years across sites, with site mean outbreak

durations ranging between 8.6 and 10.7 years by site (Table 3). Quiescent periods lasted between

four and 52 years, with site means ranging between 11.4 and 20.0 years. The average for the

regional outbreak duration and quiescent period length was 9.2 and 14.6 years, respectively.

During the 330-years covered by our regional reconstruction (1685–2014), all reporting sites

experienced identical outbreak conditions (either outbreak or non-outbreak) during 184 years

(55.8%). There were a total of 16 regional outbreaks (i.e., periods in which at least two sites

share concurrent outbreak conditions) covering 130 years (39.4%) of the total 330 years. All four

of our sites showed changes between the early (1685–1869) and modern (1870–2014) periods,

19

with an increased number of years reporting outbreaks in the modern period.

3.2 Inter-site outbreak synchrony

Outbreak and non-outbreak conditions tended to occur synchronously or near-

synchronously (Fig. 2). Our regional outbreak record (i.e., two or more concurrent site

outbreaks) showed the strongest shift to higher synchrony and intensity in the modern period

(Table 4). With our moderate intensity threshold, the early period included 43.8% of regional

outbreaks, and the modern period 56.2%. Our very high intensity threshold contained all of its

regional outbreaks in the modern period, with only three recorded outbreaks initiating in 1938,

1992, and 2010. Based on the moderate outbreak threshold, there has been little to no change in

outbreak synchrony since the start of the reconstruction. However, analysis using higher intensity

thresholds, revealed an increase in high-intensity, synchronous outbreaks in the modern period

(Table 4). Very high outbreak intensities for our three oldest sites showed between 0.0% and

5.9% of the early period records returning outbreaks, and between 6.9% and 14.5% of the

modern period represented by outbreaks.

Pearson's correlation of outbreak histories also revealed a pattern of synchrony. The

average inter-site correlation for the three oldest sites' outbreak histories (i.e., MPD, TMD and

VLD) was 0.60, while all four sites yielded a correlation of 0.71. The Tunk Mountain Douglas-

fir (TMD) reduces both periods' correlations likely due to a unique period of asynchronous,

stand-specific outbreaks during the mid-19th century also recorded in nearby outbreak

reconstructions (Fig. 2; Harris et al. 1985; Campbell et al. 2006; Axelson et al. 2015).

The modified one-dimensional K-statistic for all outbreak years of both common periods

showed a high statistical significance (at the 0.05 confidence level) for levels of bidirectional

temporal synchrony for up to seven years for our two oldest sites, 11 years for our three sites

20

dating to 1719, and 22 years when analyzing records across all four sites (Fig. 4). Initiation and

cessation dates for the 1719 common period showed significant temporal synchrony up to six

and nine years, respectively, with a higher degree of synchrony for initiation events than

cessation dates or all outbreak years (Fig. 3). With increasing outbreak intensity, synchrony was

highly-concentrated in the late period (1870-2014). In the early period (1719-1869), no

significant synchrony was found for outbreaks using our high and very high outbreak intensities

(Figs. 5 & 6). All outbreak intensities reported between up to 9 to 24 years of synchrony for the

late period records.

3.3 Climatic influences on outbreaks

Outbreak initiation dates were preceded by between two and five warm-dry years

at all sites, with initiation dates tending to occur in cooler, wetter years (Fig. 7). While no site

reported statistically-significant cool-wet years in the five years following an initiation event, all

sites still showed a shift towards cool-wet conditions. Our regional outbreak record (1685–2014)

shows a statistically-significant warm-dry anomaly in the second year preceding initiation events

(Fig. 8). Paired, two-sample t-tests revealed significant (p = 0.01) differences between PDSI

values occurring during outbreak years and PDSI values occurring during non-outbreak years

(Table 5). The PDSI values that occurred in the two years preceding outbreak years had more

similarities with PDSI values occurring during non-outbreak years (p = 0.33) than those

occurring during outbreak years (p = 0.06). Including the PDSI values from the two years

preceding outbreak events with those occurring during outbreak conditions retained significant

(p = 0.02) differences from PDSI during non-outbreak conditions.

We identified a tendency for shifts from cool-wet to warm-dry conditions during

outbreak cessation years (Fig. 7). Between three and four sites reported cool-wet conditions in

21

each of the five years preceding outbreak cessation dates, with two of our sites reporting at least

one significant year of cool-wet conditions. Two of our four sites reported statistically-

significant cool-wet years preceding a cessation date. Our analysis of regional outbreaks shows

the same pattern, with cool-wet conditions shifting to warm-dry conditions one year prior to the

cessation date.

We saw the same pattern for historical PDSI data (1895–2014) from climate station

records, which show a transition of dry-warm conditions to cool-wet conditions (Fig. 9). All sites

reported warm-dry conditions for at least three years in a five-year window preceding outbreak

initiations, with one site reporting significance for three years. Following outbreak initiations, all

sites reported cool-wet periods for at least two years in the five-year window, with three sites

reporting significant cool-wet conditions. Three out of four sites reported warm-dry conditions

during outbreak initiation events. All sites reported warm-dry conditions at least once in the two

years following outbreak initiations, with one instance of significance. The regional outbreak

record exhibits a statistically significant pattern of warm-dry conditions two years prior to

initiation events, with significant cool-wet periods between three and five years after initiation

dates (Fig. 10). All four of our sites and the regional outbreak record showed between one and

four significant years of cool-wet conditions prior to cessation dates using the historical PDSI

data, with no sites returning significant conditions following cessation dates (Figs. 9 & 10).

22

4 Discussion

4.1 Outbreak histories

We were able to successfully reconstruct 330 years of WSB outbreak history for the

Okanogan Highlands. Our reconstructed outbreak dates closely match those recorded in

historical documents and aerial survey reports. Since regional historical records are only

available after the 1970s, nearby historical records for southern BC were used alongside regional

records to check against our outbreak reconstructions. Harris et al. (1985) records WSB

outbreaks in the Cascade Range for the 1923–1930, 1943–1958, and 1977–1983 periods, with

only the earliest outbreak period not reflected in our data. Data were not recorded for the region

between 1931 and 1942, possibly explaining the discrepancy between our reconstructions and

historical records. The three most recent outbreaks (1975–1983, 1990–2001, 2009–present)

concur with the regional insect survey data and other WSB outbreak studies from the Pacific

Northwest. USFS reports in the late 1970s indicate initiating outbreaks starting between 1974

and 1976 in and west of the Mt. Hull area of the Okanogan Highlands, which matches our

initiation year for the 1975–1983 outbreak (USDA Forest Service 1977; McComb 1979). This

outbreak period was not reflected in the aerial survey data (USDA Forest Service 2014). Aerial

survey data reported a subsequent outbreak between 1986–1993, while our regional

reconstruction reported an outbreak between 1990–2001, which is concurrent with nearby BC

records reporting outbreaks throughout the 1990s (Alfaro et al. 2014; Axelson et al. 2015).

Our ongoing regional outbreak reported an initiation date of 2009, while aerial survey

data report outbreaks starting in 2008 (USDA Forest Service 2014). Westfall & Ebata (2014) has

provided annual coverage of severe WSB outbreaks in nearby Okanagan and Merritt Timber

23

Supply Areas from 2006–2014, while Jenkins (2015) reports a stark increase in defoliated

acreage across Washington State starting in 2005–2006. Both reports show significant declines

in WSB populations starting in 2013, although Jenkins (2015) suggests a strong population was

maintained in the Okanogan Highlands region. This matches our data, as all trees sampled across

our sites in 2014 still held active WSB populations. The variable lags or missing data from the

aerial survey records in relation to our regional outbreak data and nearby sites’ historical and

reconstructed outbreak data may be in part to inconsistent quality control and accuracy in

identifying insect outbreaks via aerial survey (Johnson & Ross 2008). Our outbreak

reconstructions resulted in outbreak patterns similar to those seen in nearby regions. The regional

record's average outbreak duration of 8.3 years closely matches nearby studies in the Pacific

Northwest (Campbell et al. 2006: 12 years; Flower et al. 2014a: 12 years; Alfaro et al. 2014: 7

years; Axelson et al. 2015: 11.2 years), while the regional quiescent period of 13.3 years was the

lowest among nearby studies, which ranged between 15 to 64.2 years.

It’s possible our chosen sites are not completely representative of the area, as some

limitations are inherent in any dendroentomological study. Swetnam & Lynch (1993) reported

five shortcomings of WSB studies: Only surviving trees can be sampled, making it impossible to

include mortality in our study; site sample sizes decreased as time increased, negatively

impacting the accuracy of our oldest reconstructions; corrected indices must contain some degree

of influence from other biological factors and climate, as the nonhost chronology cannot be a

perfect control for climate (e.g., Axelson et al. 2015); minor infestations may be lost within the

corrected indices, leading us to record outbreaks of moderate to very high intensities; and

temporal lags (of one to three years) between the tree's growth response and either outbreak

initiation or cessation dates can be expected. During most years of an outbreak, a progression of

24

reduced radial growth occurs, often identified in historical records with a lag of one to three

years between the initial known infestation and visible annual ring growth suppression (Alfaro et

al. 1982; Swetnam & Lynch 1989; Swetnam & Lynch 1993; Mason et al. 1997; Speer 2010;

Flower et al. 2014a). This lag is likely a product of the host trees’ abilities to store food reserves

in previous years’ xylem and phloem tissue growth (Fritts 1976). Similarly, normal radial growth

rates may not return for one or more years following a population crash, ending the outbreak.

Visible lags between the observed outbreak cessations and the trees’ recovery are due to the time

required for replacing lost foliage (Alfaro & Maclauchlan 1992).

Additionally, our four host sites may not cover enough of the region to represent regional

variability for the entire Okanogan Highlands (e.g., Alfaro et al. 2014). Douglas-fir dwarf

mistletoe was prevalent in 11 of our sampled trees from MPD and VLD, but no associated

growth anomaly could be separated from WSB-caused growth impacts, and external signs of

severe Douglas-fir dwarf mistletoe infection also overlap with signs of long-term WSB

infestation (Hadfield et al. 2000). Filip et al. (1993) found no significant interaction or effect on

mortality when both WSB and Douglas-fir dwarf mistletoe infestations were found, although

stands with severe histories of both infestations had the most visible tree mortality. Sala et al.

(2010) determined that Douglas-fir dwarf mistletoe did not act as a significant resource sink. The

1975–1983 regional outbreak shows no lag between our reconstruction and historical records for

the initiation year, which may be the result of signal contamination by Douglas-fir tussock

moth’s regional 1970–1974 outbreak (Mehmel, personal communication 2014). The Douglas-fir

tussock moth impacts trees' growth response similarly to the WSB, which could replace the lag

between outbreak initiation and trees' growth response we expect to see with WSB outbreaks

25

(Brubaker 1978).

4.2 Inter-site outbreak synchrony and driving factors

Our reconstructions show that WSB outbreaks have been occurring synchronously in the

region back to at least 1685. The regional outbreak records showed 16 instances of synchronous

outbreaks occurring between 1685 and 2014. Similar to other studies, our region has seen an

increase in outbreak synchrony, likely as an effect of anthropogenic climate change and changing

land-use regimes (Swetnam & Lynch 1989; 1993; Swetnam et al. 1995), but not duration or

frequency after the 19th century (e.g., Ryerson et al. 2003; Alfaro et al. 2014). The drivers for the

high degree of synchrony throughout our outbreak records are not fully understood, but support

the influence of fine-scale (i.e., up to 200 km, but strongest up to 100 km) dispersal abilities and

exogenous, abiotic stochastic factors on driving WSB and similar insect outbreak synchrony

(Williams & Liebhold 2000; Peltonen et al. 2002; Jardon et al. 2003; Liebhold et al. 2004). The

latter effect, known as the Moran effect, proposes that the spatial and temporal autocorrelation of

abiotic, exogenous factors like PDSI help to synchronize biotic populations over a landscape

(Moran 1953).

WSB and similar moths have been found capable of flying hundreds of km in above-

canopy winds (Greenbank et al. 1980; Dobesburger et al. 1983; Campbell 1993; Baltensweiler &

Rubli 1999), suggesting a moderate dispersal ability limited by geography such as mountainous

terrain. Over long distances, dispersal's impact on WSB outbreak synchrony was found to

decrease, with Peltonen et al. (2002) finding WSB outbreak synchrony to significantly decrease

over distances of 100–200 km. They found that the synchrony of outbreak records decreased at a

significantly faster rate than associated weather data, breaking with the assumptions of the

Moran effect (i.e., that the degradation of outbreak synchrony and climatic correlations over

26

distance would follow an identical pattern). Since our sites were all within 50 km of one another,

it could be assumed that dispersal plays a role in our outbreak synchrony.

Dispersal abilities are likely influenced by local land-use histories, as well, which have

promoted the expansion and growth of Douglas-fir over non-host species like ponderosa pine

since the late 19th century. Coupled with fire-suppression, this has led to an increase in host

range and canopy density (Anderson et al. 1987; Swetnam & Lynch 1989; 1993; Hessburg et al.

1994; Swetnam et al. 1995; Keane et al. 2002; Maclauchlan & Brooks 2009). This

homogenization of host forests could also increase dispersal abilities over smaller areas with

limited topographic barriers like the Okanogan Highlands, potentially leading to higher

population densities during outbreak conditions (Wilhite & Stock 1983).

Regional climatic stochasticity has still been found to be the dominating influence on

insect population dynamics, particularly over larger spatial scales (Williams & Liebhold 1995;

Myers 1998; Williams & Liebhold 2000; Peltonen et al. 2002). The loss in dispersal’s influence

on population synchrony over distances of 100–200 km is also seen in similar species, and may

be more influenced by cyclicity rather than the species' physiological dispersal capabilities

(Sutcliffe et al. 1996; Ranta et al. 1998; Bjørnstad 2000; Williams & Liebhold 2000; Peltonen et

al. 2002). Ranta et al. (1999) argued that the classical Moran effect was too log-linear in its

explanation, and population synchrony of insects was influenced by nonlinear factors, like a mix

of dispersal ability seen in a spatially homogenized landscape, and spatially-autocorrelated

climatic patterns. Similarly, some studies have found a spatially heterogeneous landscape leads

to reductions in population synchrony (Engen & Sæther 2005). Another potential factor, the

influence of trophic interactions on WSB populations, has been underexplored in research, but is

assumed as similarly limited to local area due to the mobility of insectivores nearly mirroring

27

prey dispersal abilities (Peltonen et al. 2002; Jardon et al. 2003). One study of greater

Lepidoptera species found increased spatial synchrony among Lepidoptera populations in

response to generalist predators (e.g., Campbell et al. 1984), but only with pairs of similar

Lepidoptera species sharing a habitat (Raimondo et al. 2005). Studies of parasitoid and pathogen

impacts on WSB population dynamics found an increasing chance of mortality as outbreaks

move higher in elevation (over 1,000 m) than is historically expected, and increasing landscape

homogeneity and biomass allows for denser outbreak populations, which, in turn, promotes the

spread of parasitoids and pathogens (Nealis et al. 2009; Nealis et al. 2015). In both studies,

parasitoids and pathogens were found as dominating generational mortality factors, causing

roughly 33% and 20% mortality in studied populations, respectively, but their impacts during a

collapsing outbreak have not been studied.

At our sites, a highly asynchronous period of site-specific outbreaks occurred between

1820–1870, during which only one site (TMD) was impacted by outbreaks. This extended period

of asynchrony in the 19th century negatively impacted our Pearson's correlation values of

outbreak histories, particularly evident in the differences between the early and late halves of our

outbreak records, where average inter-site correlation for the three oldest sites prior to 1870 was

0.33, but increased to 0.77 after 1869. Without further research, however, it’s unclear how much

these correlation values may be impacted by other variables, like changing land-use regimes and

dispersal rates. This asynchronous period is also reported by nearby records (Harris et al. 1985;

Campbell et al. 2006; Flower et al. 2014a; Axelson et al. 2015), and may be at least partially

attributable to alternating regimes of mild warm and cool sea surface temperatures driven by the

Pacific Decadal Oscillation between 1840–1923 affecting local teleconnections like PDSI

28

(Gedalof & Smith 2001).

More significantly, higher-intensity outbreaks at both site- and region-levels reported

much higher outbreak frequencies in the last century than in previous centuries. Similarly, the

degree of synchrony tended to increase with the intensity threshold used. Our moderate outbreak

threshold returned a minor increase of up to three years of significant synchrony in the early

period (1719-1869) to up to nine years in the late period (1870-2014). High and very high

intensity thresholds returned either no significant synchrony or no synchrony in the early period.

All three intensities we investigated shared high levels of synchrony (between 9 and 26 years) in

the late period alone. This clustering of synchronous events towards the 20th century suggests the

patterns of synchrony we see in the full records (i.e., 1719–2014) for high and very high

intensities may not accurately reflect the clustered pattern, due to the method of randomization

built into the K1D software (i.e., Bigler et al.'s (2007) Poisson regression intensity weights)

being less effective at finding patterns with increasingly-clustered temporal events (Gavin,

personal communication 2016). The highest-tested intensity threshold had no regional outbreak

events prior to the 20th century, making the degree of frequency and synchrony in the late period

unprecedented. Three regional outbreaks of very high intensity occurred among our sites, and all

of them in the last century (during the 1940s, 1990s, and 2010s), strongly suggesting

anthropogenic impacts such as climate change or land-use practices play a primary role

(Swetnam & Lynch 1989; 1993; Swetnam et al. 1995). High intensity-outbreaks have been found

to lead to greater levels of dispersal, and consequently higher dispersal losses, after years of

particularly severe defoliation (Nealis & Régnière 2009).

4.3 Outbreaks and climatic variability

Climate-growth analysis showed that the annual radial growth rates of both host and non-

29

host trees were significantly limited by moisture stress (Table 2). Both our host and non-host

sites shared similar responses to climatic variables. Similar to Chen et al. (2010), we found

positive correlation between our trees’ radial growth and precipitation values, as well as negative

correlation with temperature values, which suggests warm, dry conditions inhibit radial growth

rates similarly in both ponderosa pine and Douglas-fir, while cool, wet conditions promote radial

growth rates.

Initiation dates show a distinct trend of transitioning climate from warm-dry conditions to

cool-wet conditions: All four of our sites showed strong warm-dry conditions in the two years

immediately prior to initiation events, and three of our four sites held these conditions back to

the third year before outbreak initiation events, including one site reporting significantly warm-

dry conditions (Fig. 7). During and after initiation dates, climate tended towards cool-wet

conditions, which diminished in the second year after the initiation date before returning to

synchronous cool-wet conditions among three to four of our sites. This pattern of cool-wet

climate conditions during outbreak conditions has been identified by other dendrochronological

studies (Swetnam & Lynch 1993; Swetnam et al. 1995; Ryerson et al. 2003; Flower et al. 2014a).

Since a lag of one to three years for both initiation and cessation dates usually exists between a

tree's radial growth impacts and the actual time of an infestation's initiation or cessation (Alfaro

& Machlauchlan 1992; Swetnam et al. 1995; Mason et al. 1997), this suggests that the repeating

warm-dry years prior to an outbreak initiation represent the climatic conditions driving WSB

outbreak initiations, and the subsequent transition to repeating cool-wet years are necessary to

sustain outbreak-level populations. This is consistent with previous studies that found warm-dry

conditions as necessary for preceding modern outbreak initiations (Hard et al. 1980; Thomson et

30

al. 1984; Campbell 1993; Flower et al. 2014a).

The transitioning climate having a key role in outbreak dynamics supports the nonlinear

pulsed plant stress hypothesis (Huberty & Denno 2004; Mody et al. 2009), in which temporal

variability in moisture stress was crucial in initiating and subsequently sustaining outbreaks. The

competing plant stress and plant vigor hypotheses propose linear factors like sustained drought

conditions (White 1984; Mattson & Haack 1987) or excess moisture promoting foliage growth

(Price 1991) as being the primary driver for herbivorous insect outbreaks. Moderate drought

stress has been found to favor WSB and similar herbivorous insects' growth and reproductive

rates, as well as larval survivability, by increasing foliar concentrations of nitrogenous

compounds, sugars, and other favorable compounds, particularly in younger trees (Cates et al.

1983; Kramer 1983; Mattson & Haack 1987; Campbell 1993; Parks 1993). This relationship can,

however, reverse with prolonged outbreak conditions or increasing outbreak severity (Mattson &

Haack 1987; Campbell 1993; Hubert & Denno 2004). These changes to foliage composition over

transitioning climate conditions would benefit the growth and survival of WSB during larval

stages by favoring the species' diet during moderate drought stress, and subsequently allowing

for increased food production (with decreased toughness) during sustained cool-wet conditions

(Gower et al. 1992; Krause et al. 1993). Our study follows Flower et al. (2014a) in finding the

non-linear relationships of the pulsed plant hypothesis as the strongest explanation for WSB

outbreak dynamics over multicentury records, with moisture availability as the primary driving

mechanism.

Cessation dates should, like initiation dates, show a one- to three-year lag between years

of outbreak conditions and a tree's return to normal growth conditions during the recorded

cessation date (Alfaro & Maclauchlan 1992; Swetnam et al. 1995; Mason et al. 1997), which

31

suggests the five years of sustained cool-wet conditions recorded at three to four of our sites

(including two instances of significance) should represent the conditions in which outbreaking

WSB populations crashed (Fig. 7). Cessation dates with defoliating species like WSB are

typically attributed to a loss of food from sustained overpopulation, and trophic interactions with

natural predators, parasites, or pathogens being favored by environmental conditions enough to

outperform defoliating insect populations (Cooke et al. 2007; Nealis 2015). As available needles

become sparser or difficult to mine, the WSB population density inevitably dips, while predators,

parasitoids, and pathogens that prey on WSB are able to maintain population densities and

increasingly contribute to WSB population losses (Nealis 2015). Despite also showing a

transitioning climate around cessation dates, the transition to warm-dry conditions would not

have a causal relationship with the WSB's population crashes. However, since our superposed

epoch analysis consistently reported cool-wet conditions at three to four of our sites over five

years prior to cessation dates, it’s likely that long-term maintenance of cool-wet conditions plays

a causal role in WSB population crashes by feeding significant trophic interactions, food loss,

emergence- and budburst-timing, and damaging local weather conditions.

32

5. CONCLUSION

Western spruce budworm outbreaks have been occurring synchronously in the Okanogan

Highlands since at least 1685. Outbreak synchrony across the region has increased in the late

period, with synchrony of higher-intensity outbreaks showing the biggest increase in synchrony.

Frequency of outbreaks has not changed significantly over the last three centuries, except with

high- and very high-intensity outbreaks. Although moderate-intensity outbreaks increased only

increased in synchrony, high- and very high-intensity outbreaks saw drastic increases in both

frequency and synchrony between the early and late periods, strongly affecting our record of

inter-site synchrony and defining the majority of recorded outbreaks over the last century. It’s

probable these changes were influenced by changing land-use regimes initiated by western

expansion in the 19th century, with impacts like forest homogenization and fire suppression

improving the WSB’s dispersal abilities over the spatial scale of our study area.

Our superposed epoch analyses found strong relationships between region- and site-level

outbreak histories and moisture availability. Outbreak initiation dates showed a relationship with

multiple, consecutive years of low moisture availability in the years preceding initiation events,

and consecutive years of high moisture availability during and after initiation years. Cessation

dates, on the other hand, showed a strong relationship with high moisture availability during the

five years preceding recorded cessation dates. Reconstructed moisture availability data showed a

few instances of significant departures of moisture availability preceding either initiation or

cessation events, with historical moisture availability records indicating more instances of

significant departures preceding and following event years. The temporal variability in moisture

33

availability occurring during and around outbreak events supports the pulsed plant stress

hypothesis in explaining WSB outbreak dynamics: High moisture stress is essential to feeding

increases in WSB populations and dispersal rates, and a shift to low moisture stress is necessary

to maintain the inflated outbreak populations.

As climate change causes greater climate variability, it should continue to affect WSB

population dynamics. Projected changes in regional and global temperatures and precipitation

over the next century will increase the likelihood of sustained drought conditions necessary for

initiating WSB outbreaks. It’s also possible that the increase in drought conditions could hamper

WSB outbreaks if drought conditions are sustained over too many consecutive years, or occur

too frequently, since the climatic reversal to consecutive cool-wet conditions appears necessary

in sustaining outbreaking WSB populations. Changes to land-use practices over the next century

should also impact the occurrence of WSB outbreaks. A potential increase in forest fire

occurrences with a changing climate could lead to changes in regional biomass available to WSB

populations, leading to indirect effects on WSB dynamics (Flower et al. 2014b). Additionally,

changing climate may drive shift the distribution of the WSB’s host populations over coming

centuries. The results of our study suggests a complex combination of climate change, land-use

patterns, and disturbances such as fires will continue affecting WSB outbreak dynamics in

coming centuries, and continued study is needed to better understand how this complex interplay

of exogenous factors will direct WSB populations.

34

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6. FIGURES AND TABLES

Table 1

Site and chronology characteristics for our four host sites and three non-host sites. The start of

the used record reflects either the common period shared by host and non-host trees, or the oldest

date with two or host more trees reporting.

Site Latitude Longitude

Elevation

(m)

# of

trees

Oldest

record

EPS

(Pearson)

Interseries r

(Pearson)

MPD 48.786 -119.243 1359 16 1685 0.933 0.686

SMD 48.561 -119.172 1379 18 1796 0.943 0.780

TMD 48.552 -119.225 1599 20 1685 0.936 0.650

VLD 48.825 -118.927 1286 15 1719 0.965 0.760

DCP 48.528 -119.029 1003 17 1685 0.942 0.627

TLP 48.672 -118.981 1334 6 1685 0.847 0.720

VLP 48.824 -118.927 1274 9 1685 0.936 0.760

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Table 2

Cross-correlation (Pearson’s r; p < 0.05 except where noted) values of host chronologies and the

regional non-host chronology against reconstructed (1685-2003) and historical (1895-2014)

climate data. Positive correlation values with precipitation variables and negative correlation

values with temperature variables support water stress being an important factor in radial growth.

Site

PDSI

(Cook)

PDSI

(NOAA)

WYPa

Previous

year’s

WYP

GYTb

Previous

year’s

GYT

Mean July

temperature

MPD 0.49 0.54 0.42 0.42 -0.20 -0.20 -0.16c

SMD 0.55 0.59 0.45 0.49 -0.20 -0.14c -0.09c

TMD 0.37 0.39 0.28 0.47 -0.11c -0.18 -0.13c

VLD 0.51 0.57 0.42 0.45 -0.21 -0.15c -0.13c

Regional

non-host

0.60 0.60 0.50 0.48 -0.20 -0.15c -0.14c

a Water-year (April to September) precipitation.

b Growing-year (previous October to current September) temperature.

c Value does not represent a significant (p < 0.05) relationship.

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Table 3

Outbreak statistics for our four host sites. Mean return length represents the average period of

time between outbreak cessations and subsequent outbreak initiations.

Site

# of

outbreaks

Mean outbreak

length

Mean return

length

% of record

outbreaking

Kruskal-Wallis

chi-square*

MPD 15 8.6 14.5 38.2 97.85 p < 0.01

SMD 8 9.1 20.0 32.4 67.24 p < 0.01

TMD 17 8.9 11.4 44.6 109.85 p < 0.01

VLD 11 10.7 16.0 38.2 83.67 p < 0.01

Regional 16 9.2 14.6 39.4 112.24 p < 0.01

* Kruskal-Wallis ANOVA conducted on corrected indices during outbreak conditions against non-outbreak conditions.

53

Table 4

Percent of outbreak-only years occurring in the early (1685–1869) and modern (1870–2014)

periods. Percents shown are the total number of years within each period’s length. Each of our

four sites are represented, as well as the regional outbreak history with at least two sites reporting

outbreaks concurrently. SMD (1796–2014) and VLD (1719–2014) do not represent equal

measures of time between early and modern periods.

Site

Moderate

Early Late

High

Early Late

Very high

Early Late

MPD 34.1 43.4 21.6 23.4 5.9 14.5

SMD 20.3 38.6 8.1 22.1 0.0 6.9

TMD 41.6 48.3 6.5 26.9 2.2 11.7

VLD 33.8 42.8 13.9 22.1 2.6 13.1

Regional 30.8 50.3 9.7 28.3 0.0 14.5

54

Table 5

Paired, two-sampled t-test results for tested differences between PDSI values occurring during

different outbreak conditions, including during outbreaks, between outbreaks, and in the two

years immediately preceding outbreak initiations. Regional outbreak records (i.e., two or more

sites outbreaks) were used to group PDSI data.

PDSI variables t-statistic p-value

Mean of 1st

variable

Mean of 2nd

variable

During (1st) & between (2nd)

outbreaks 2.83 0.01 0.51 -0.26

Two years prior to (1st) and during

(2nd) outbreaks -1.97 0.06 -0.67 0.51

Two years prior to (1st) and between

(2nd) outbreaks -0.99 0.33 -0.67 -0.26

During outbreaks with two years

prior to initiation dates (1st) and

between outbreaks 2.36 0.02 0.32 -0.20

55

Fig. 1

Location of host and non-host sites within the Okanogan Highlands. Note that VLD and VLP

share a site in the northeast. Significant local features include the highest peak, Mount

Bonaparte, immediately east of site MPD, and the Aeneas Valley dividing our southern sites

from our northern locations. Service layer credits: ESRI, HERE, DeLorme, Mapmyindia,

OpenStreetMap.

56

Fig. 2

Percent of trees recording an outbreak at our four host sites. The shifting black lines represents

an 8-year moving average of percent of trees recording outbreak records, and the dashed line

represents the unfiltered outbreak records. Two dotted straight lines indicate noted severity

57

levels: 40% and 80% of trees at the site recording an outbreak.

58

Fig. 3

Modified Ripley's K-function for moderate outbreak initiation dates and cessation dates. All dates

were taken from our three sites dating to 1719 to include as much of the record with as many of

our sites as we could. Y-axis represents a level of synchrony (positive) or asynchrony (negative)

calculated using the temporal window (one to 30 years) shown on the X-axis, with the shaded

region representing the confidence interval (95%). Any points outside the shaded region represent

significant years in which synchrony occurs bidirectionally from outbreak years. Initiation and

cessation dates report synchrony up to six and nine years, respectively.

59

Fig. 4

Modified Ripley's K-function calculated for all outbreak years (1719–2014) with moderate

(40%) outbreak intensity. The top image represents all years of data. The middle and bottom

images separate the outbreak years between early (1719-1869) and late (1870-2014) periods. Up

60

to 11 years of synchrony is recorded in all outbreak years. The early period shows up to three

years of synchrony, while the late period shows up to nine years of synchrony.

61

Fig. 5

Modified Ripley's K-function calculated for all outbreak years (1719–2014) with high (60%)

outbreak intensity. The complete outbreak record reports synchrony up to 19 years. No

significant synchrony is seen in the early period, while the late period outbreaks report up to nine

62

years of synchrony.

63

Fig. 6

Modified Ripley's K-function calculated for all outbreak years (1719–2014) with very high

(80%) outbreak intensity. Note the change to the y-axis due to the decreased sample sizes for

high-intensity outbreaks. The complete record shows significant synchrony of up to 26 years. No

64

synchrony existed in the early period’s very high intensity outbreaks. All synchrony was

clustered in the late period, occurring up to 24 years.

65

Fig. 7

Superposed epoch analysis summary of our four sites' initiation and cessation dates using Cook

et al.'s (1999; 2003) PDSI reconstructions. Instances of high significance are noted by dark grey

66

(90%) and black (95%) shading.

67

Fig. 8

Superposed epoch analysis using the regional outbreak initiation and cessation dates (i.e., at least

two sites with concurrent outbreak conditions between 1685 and 2014).

68

Fig. 9

Summary of superposed epoch analyses with historical (1895–2014) PDSI records and all four

sites' initiation and cessation dates.

69

Fig. 10

Superposed epoch analysis using historical (1895–2014) PDSI records with regional outbreak

initiation and cessation dates.