Fraxinus Americana) at the Allegheny National Forest

ABSTRACT

DENSITY-DEPENDENT SURVIVAL OF WHITE ASH (FRAXINUS AMERICANA) AT THE ALLEGHENY NATIONAL FOREST

by Elijah Daniel Aubihl

The study of the rapid decline of ash trees caused by the emerald ash borer beetle (EAB) has been well documented, but little is known about the decline of ash stands when a subset of trees is treated with insecticide. The treatment of a subset of ash trees in areas may provide protection to those ash trees that are untreated, resulting in the associational protection of untreated ash trees provided by treated ash trees. Two objectives of this study were to test whether such associational protection can occur, and the threshold, if any, at which associational protection occurs. The third objective was to test whether any ensuing mortality depended on the density of ash trees. This study was conducted in the Allegheny National Forest, located in Northwestern Pennsylvania and is part of a larger study by the U.S. Forest Service focused on preserving the genetic diversity of ash trees in the Allegheny National Forest. My analyses indicate no protection from insecticide and no associational protection. Further, I found that low ash density forest stands display faster rates of decline caused by EAB than high ash density forest stands. These findings are from the analyses of datasets from the first two years of a ten-year study and although they are from the early stages of a long-term study, the management implications are valuable.

DENSITY-DEPENDENT SURVIVAL OF WHITE ASH (FRAXINUS AMERICANA) AT THE ALLEGHENY NATIONAL FOREST

A Thesis

Submitted to the

Faculty of Miami University

in partial fulfillment of

the requirements for the degree of

Master of Science

Elijah Daniel Aubihl Miami University

Oxford, Ohio

2019

Advisor: Martin H. H. Stevens

Reader: Michael Vincent

Reader: Richard Moore

Reader: Reader's Name

This Thesis titled

DENSITY-DEPENDENT SURVIVAL OF WHITE ASH (FRAXINUS AMERICANA) AT THE ALLEGHENY NATIONAL FOREST

Elijah Daniel Aubihl

has been approved for publication by

The College of Arts and Science

and

Department of Biology

______Martin H. H. Stevens

______Michael Vincent

______Richard Moore

Table of Contents List of Tables…………………………………………………………………...... ………...iv List of Figures………………….…………………………………..………..…….………..v Dedication………………….….…………………………………………………………..vi Acknowledgments………………….……..………..…………………………………...…vii Introduction. ……………………………………....……………… ………………..……..1 Methods….…………………..…..…..…..…..…..…..…..…..…..…..…..…..…..…………...7 Results………………….…..…..…..…..…..…..…..…..…..…..…..…..…..…..………...….15 Discussion………………….…..…..…..…..…..…..…..…..…..…..…..…..…..………...….21 Literature Cited………………….…..…..…..…..…..…..…..……..………..…..…...... 28

iii

List of Tables

Table Page

Table 1. Canopy condition rating system used to classify ash tree canopy conditions………………....…....…..…..…..…..…...…..………………………………..12

Table 2. A shift in canopy condition from healthy to less healthy in both untreated and treated trees from 2015 to 2017……………………………….…………………………16

Table 3. Results of ANOVA test comparing the variability between log(change in canopy condition), slope of plot, and the interaction between treatment and slope…………………………………………………………………….………………..17

Table 4. Results of ANOVA test comparing the variability between the average change in canopy condition and log(n), slope of plot, and the interaction between log(n) and slope position………………………………………………………..………………………….18

List of Figures

Figure Page

Figure 1. Prediction of the resource concentration hypotheses….…..……….……….…..4

Figure 2. Prediction of the resource dilution hypotheses……...……….….….…………...5

Figure 3. Research plot locations in the Allegheny National Forest…..……...……..…….9

Figure 4. Research plot design………………..…………………………..……………...10

Figure 5. Research plot example………………..…………………………………….….11

Figure 6. Illustration of canopy condition ranking system……………….……...………12

Figure 7. Treatment and slope interaction………………...…………………………...…17

Figure 8. Density-dependent ash decline.………………………...……………………...19

Figure 9. Change in ash canopy condition (2015-2017) ……………….…………..……20

Dedication

I dedicate this thesis to my late grandfather Lowell Hosler. Unfortunately, he passed away during the writing of this thesis and although he will never see this, he has been my biggest source of motivation and I am forever grateful for the time we shared together.

Acknowledgements

I would like to thank my advisor Dr. Martin H. H. Stevens for devoting his time, knowledge, and experience to this thesis. I am especially thankful for his expertise in the use of R, data analyses, and ecology. I extend thanks to Dr. Michael Vincent who took me under his wing at the herbarium many years ago. I would also like to thank Dr. Richard Moore for expanding my knowledge on plant genetics and anatomy. I extend thanks to the U.S. Forest Service and all of the members of the Northern Research Station for the opportunity to participate in this study and use a portion for this thesis. I thank Kathleen Knight for hiring me after undergrad and sending me to the Allegheny National Forest for a summer and all her support throughout the past 4 years. She provided the foundation and funding that made this study and thesis possible. I also thank Charlie Flower for being a role model and inspiration, without his help this project would not have been possible. I would also like to thank Josh Paradise and Karlie Sherman for their help with data collection in 2015. I would like to thank Alejandro Royo for his help and resources while in the ANF. I thank Jack Keegan for his friendship, his many years of teaching and allowing me to work in the greenhouse during my undergrad years. Lastly, I would like to thank the biology department and all of the many great professors that got me to this point, without them this would not have been possible.

vii

INTRODUCTION

Worldwide, non-native invasive species are a leading cause of native tree species decline and ecosystem disruption (Mattson et al. 2007). In Eastern North America, extirpation of the genus Fraxinus by the emerald ash borer beetle (EAB) threatens temperate forests, which are under stress from a variety of other forest pests that include gypsy moth, hemlock wooly adelgid, Asian longhorned beetle, nun moth, and others becoming problematic regularly (USDA 2017). Some progress has been made in understanding what controls the rate of EAB spread and the rate of decline of infected trees through studies on ash trees in urban, metro park and localized ash stands (McCullough et al. 2009; Knight et al. 2013), but few large scale experiments have evaluated the efficacy of a basally injected insecticide (McCullough et al. 2011). Here I describe the results of a large scale experiment that evaluates the efficacy of one such approach and, in doing so, tests two contrasting hypotheses for how pest insects respond to host density. Environmental and economic costs associated with invasive species generally is estimated at $138 billion annually in the US (Pimentel et al. 2005). In addition to the financial burden imposed, non-native invasive insect pests acting as ecosystem disturbance agents cause host tree mortality, reduce forest productivity and biodiversity and alter forest succession (Flower and Gonzalez-Meler 2015). Ecosystem dynamics will be altered in the absence of the genus Fraxinus. As disturbance severity and net primary production (a metric of ecosystem function) may be non-linearly related (Flower and Gonzalez-Meler 2015), the effects of invasive insect pests may be difficult to predict. In light of these potential consequences as a result of invasive forest pests, efforts are needed to monitor ash decline and conserve the genetic diversity of species belonging to Fraxinus spp. Because of the large areas affected active management of these pests through aerial spraying or systemic injection, may hold limited promise. Nonetheless, it is important that we quantify the effects of these management strategies to uncover unexpected possible treatments or to avoid costly and ineffective approaches. It is known that anthropogenic activities and globalization are the leading causes in the decline of native plants such as those members of the Ash genus Fraxinus spp. (Pimental

1 et al. 2005). Agrilis planipennis Fairmaire, the emerald ash borer beetle (EAB), an arthropod native to Northeastern Asia was introduced to North America in the 1990’s in shipping pallets and was first detected near Detroit, Michigan and Windsor, Canada in 2002 (Siegert et al. 2014). Since its introduction into North America, EAB has spread to 35 states, including Pennsylvania (USDA APHIS 2019). The first detection of EAB in Pennsylvania was in Butler County, on the northern side of Pittsburgh and it was first discovered in the Allegheny National Forest by an arborist crew in 2013 (The Pennsylvania State University 2019). EAB now ranges from Colorado and Texas to New Hampshire (USDA APHIS 2018). While herbivory by adult A. planipennis is thought to be inconsequential, larvae feed on the cambial tissues of Fraxinus spp., specifically the phloem. This larval feeding creates serpentine galleries inside the phloem, effectively girdling the tree, cutting off the mass flow of nutrients (Flower et al. 2013). This results in >99% ash tree mortality in 2-5 years after the initial EAB infestation (Knight 2014; Flower et al. 2013). This mortality rate is visible in the 50 percent decrease in annual net growth of ash trees in Pennsylvania since 2012 (Knight et al. 2013; USDA APHIS 2018). There are an estimated 38 million ash trees growing in the 27 states that are within the range of the genus Fraxinus and it is estimated that the effects of the emerald ash borer beetle will account for more than $10.7 billion in damages through 2019 (Kovacs et al. 2010). A. planipennis attacks on Fraxinus spp. are facilitated by stress pheromones released by the trees. A. planipennis are attracted to these stress pheromones and they enhance the probability of ash tree detection by EAB (McCullough et al. 2009). Initial infestation can occur by A. planipennis being attracted to stressed trees caused by other environmental factors such as drought and low nutrient availability. Once initial infestation of A. planipennis occurs this results in the release of additional stress pheromones, attracting more A. planipennis to the already stressed trees. There is minimal knowledge on the actual dispersal rate and range of A. planipennis due to the beetle not producing long range attraction pheromones. What is known is that the developmental time for larvae regulates the rate and range of dispersal. It has also been shown that multiple generations will not disperse further

2 than 100 meters from the tree they emerged from if there is ample Fraxinus spp. phloem available for the larvae to feed on (Mercader et al. 2011). A. planipennis do not produce long range attraction pheromones and this poses a problem because without the production of long range attraction pheromones identifying new A. planipennis infestations requires the use of surveying strategies that are destructive to the trees (McCullough et al. 2009). The emerald ash borer life cycle, like most other life cycles dependent on sexual reproduction begins with mating. Adult females of A. planipennis have a life span of approximately two-months, with males living only approximately half that time. Mating occurs between adult female and male A. planipennis after they have fed on the leaves of ash trees for at least 7 days. After breeding has taken place, the adults continue to feed for an additional 10-14 days, and it is after this period of time that the females begin to lay eggs on the bark or in small crevices on the outer bark of an ash tree. Females can lay up to 150 eggs, with an average number of 55 eggs laid per female (USDA 2017). The newly hatched, neonate larvae then bore through the bark and into the tree, where feeding on the cambial tissues, specifically the phloem, begins. During the four instar stages of development, the larvae are feeding on phloem and continuing to develop until winter when they become prepupal larvae and remain in this developmental stage until spring (McCullough et al. 2011). When spring arrives, the larvae pupate and develop into adults, when they then bore through the inner layers of the bark leaving their characteristic “D” shape exit holes (USDA 2016). It has been shown that on average, approximately 89 A. planipennis larvae can mature and emerge from 1 m2 of ash tree phloem and that visible decline in canopy condition can occur with just 30 larvae feeding per m2 in an ash tree, dependent on the size of the tree (McCullough and Siegert 2007). The developmental time for A. planipennis larvae to reach maturity can vary between 1 and 2 years, dependent on the overall population. When the overall population of A. planipennis is high the larvae develop in 1 year, but when the population is small the larvae take 2 years to develop (Mercader et al. 2011). Host density often plays a role in the spread and severity of attack by pathogens, pests, and other enemies. Two hypotheses that are particularly applicable to EAB-ash dynamics are the resource concentration hypothesis (Root 1973) and resource dilution

3 hypothesis (Yamamura 2002) (Figure 1). The resource concentration hypothesis states that when there are higher densities of host plants, invasive insects will be able to reach higher populations and emigrate from these areas at lower rates than in less dense host plant populations and this will result in a larger overall negative effect on the host plants (Otway et al. 2005; Rhainds and English-Loeb 2003). The resource concentration hypothesis is important to consider for this research project because if true, it predicts that ash trees in plots with high ash densities will have lower overall health than ash trees in plots with low ash density.

High Survival

) Ash

Low Host Plant (

20 80 140 200

Host Plant (Ash) Density

Figure 1. The resource concentration hypothesis predicts declining host plant survival with increasing host density.

In direct contrast to the resource concentration hypothesis, the resource dilution or diffusion hypothesis (Yamamura 2002) predicts that although host plant densities are high, an initial low number of insect herbivores become diluted among the high density of host plants. The potential for insect herbivores immigration is initially limited, causing the individual insect herbivores to be dispersed among a high density of host plants (Yamamura 2002). An extension of the resource dilution hypothesis is that in areas of low host plant densities the overall negative effect on host plants will be greater over time (Knight et al. 2013) (Figure 2). Studies testing the resource dilution hypothesis have mainly focused on agricultural plant species and other host specific insect pests (Yamamura 2002; Otway et al. 2005).

High Survival

) Ash

Low Host Plant (

20 80 140 200

Host Plant (Ash) Density

Figure 2. The resource dilution hypothesis predicts increasing host

plant survival with host density.

Knight et al. (2013) provide evidence to support the resource dilution hypothesis. They found that when attacked by A. planipennis, ash stands with low densities experienced faster rates of decline than stands with higher ash population densities. The findings suggest that, if EAB populations are more concentrated on host trees in areas with low ash densities, the effects of the larval feeding are more rapidly expressed (Knight et al. 2013). However, the aim of this study was to monitor the decline of ash populations with no treatments applied to alter the rate of decline caused by EAB, and more research needs to test this hypothesis. The use of systemic insecticides has proven to be a successful means of killing invasive insect pets, including A. planipennis (McCullough et al. 2011). Emamectin benzoate is classified as an avermectin insecticide, developed for use on conifers to prevent insect herbivory (Herms et al. 2014). This insecticide is created by fermenting Streptomyces bacteria and it works by disrupting the nervous system of arthropods that feed on the vascular tissues and leaves of treated trees. Once administered, the basally injected insecticide emamectin benzoate (Tree-ägeTM, Arborjet Inc., Woburn, MA, USA) is translocated throughout the vascular tissues of the injected tree, typically within 2 weeks, by natural uptake processes (Herms et al. 2019). Emamectin benzoate is most effective when digested by an insect but it has also been shown to be effective when coming in contact with insects (U.S. EPA 2009). Ecologists have begun to consider possible effective treatment strategies from human disease to potentially halt the damage to plants by invasive insect pests (O’Brien 2017). In particular, herd immunity was first described by Dr. A.W. Hedrich (1933) in his studies of measles, an epidemic that affected millions of people (Standish et al. 2016). Herd immunity, or associational protection has been described as a population becoming resistant to a disease when a subset of that population has been treated to prevent infection (John and Samuel 2000). Associated with herd immunity is the concept of a treatment threshold, or the percent of a population that needs to be treated in order to provide protection to those members of the population that are untreated. Treatment thresholds vary greatly depending on the population and disease for which treatment is occurring (Standish et al. 2016). In

6 regards to plant/invertebrate herbivory, Barbosa et al. (2009) describe the ways in which plants treated with an insecticide may act as sinks for the invertebrates, providing protection to neighboring plants from herbivory. The plants that were treated with an insecticide provide protection to neighboring plants because insects feeding on these treated plants die, preventing damage to neighboring plants. In this study, I tested whether correlates of pest load (A. planipennis) and ash tree survival depended on the density of untreated ash trees in the Allegheny National Forest. Positive correlations would support of the resource concentration hypothesis, whereas negative correlations would support of the resource dilution hypothesis. Additionally, I tested the efficacy of insecticide treatment, whether it conferred associational protection, and if so, whether I could detect a treatment threshold level given the percent of treated ash trees in my plots.

Methods Site Selection In the spring of 2015 I began research on 27 plots distributed throughout the Allegheny National Forest (Figure 3). I was hired as a field technician working for the USDA Forest Service and was tasked with establishing research plots in ash stands located in the ANF. The ANF was chosen for this research project by the U.S. Forest Service for this reason and although signs of EAB were visible, the overall health of ash trees in the ANF exhibited the range of health needed to conduct a large scale research project on the efficacy of the insecticide emamectin benzoate, not only on “healthy” trees, but also those trees that were already showing signs of damage from A. planipennis. The research I conducted was part of an in-situ genetic conservation project to preserve the genetic diversity of ash throughout the ANF. The Allegheny National Forest (ANF) is located in Northwestern Pennsylvania and encompasses more than 517,000 acres of mainly deciduous hardwoods (USDA 2018). In 2015, Pennsylvania had 1,520,041 net volume of live ash trees > 5 inches DBH in thousand cubic feet (Wildmann, 2016). Fraxinus spp. are moderately abundant in the Allegheny

National Forest, accounting for <5% of live basal area tree species (Royo and Knight 2012; Flower et al. 2018). Although its proportional abundance may seem low, its density varies greatly based in part on habitat, as such in stands with high ash density. Areas of the ANF with higher ash densities tend to be at lower elevations and this is due to increased nutrient levels, specifically cations, crucial nutrients for ash trees (Royo et al. 2010). The focal trees in my research plots were located and identified in 2010 by Alejandro Royo and Kathleen Knight, research scientists with the Northern Research Station, USDA Forest Service working on a project focused on using the canopy condition of ash trees in the ANF as a health metric to monitor the decline of ash trees in the ANF.

Field Data Collection Twenty-seven 8-acre treatment plots were established in 2015. Plot boundaries were determined by using a gps unit that had an additional layer showing the plot boundary. The plot boundaries were defined by a 100-meter radius (approximately 8 acres) originating at the plot center tree (the 27 trees tagged in 2010) of each plot (Figure 4). Plot boundaries were marked with three slashes using white tree marking paint. Boundary markings were an important part of this project because the U.S. Forest Service heavily manages stands of trees in the ANF and without a distinct boundary around these research plots the risk of having trees within the plots cut down for timber sales was high. The plots are separated into upper slope and lower slope plots, depending on their elevation. Plot numbers ending in an even number denote lower slope plots while those ending in an odd number denote an upper slope plot. In addition to the slope, some of the plots are referred to as paired plots due to an upper slope plot being located at a position up slope of a lower slope plot. The only criteria for defining upper slope versus lower slope was that if two plots were in close proximity, the plot with the highest elevation would be termed upper slope and the other as lower slope.

ANF Ash Canopy Condition 2015 EAB Insecticide Project

Fal002 Fal006

Legend Fal016 Treatment Plots Fal028Fal024 ANF Forest Boundary Fal026

Fal034 Fal038 Fal166 Fal192Fal062

Fal190 Fal060 Fal050 Fau087 Fal088 Fal054

Fau105

Fal158 Fau147

Fal126 Fal142

± 03.75 7.5 15 Miles

Figure 3. Research plot locations in the Allegheny National Forest. “Fal” refers to lower slope plot locations and “Fau” refers to upper slope plot locations.

100 Meter Radius

Plot

Center

Figure 4

I identified each ash tree > 8” in diameter within the plot boundary. After each ash tree within the boundary was identified, I then attached an aluminum number tag to give each ash tree within the plot a unique identity. Additionally, the geographic location of each tagged tree was plotted on a Trimble JunoÓ gps receiver with sub-meter accuracy. These data points of tree locations were then transformed for viewing on computers and giving the data the ability to be transferred to other gps units (Figure 5).

Plot FAU005

Figure 5. Tree locations of all ash trees in plot FAU005. This plot is representative of all plots and how the locations of ash trees can be viewed on a gps unit.

Once all ash trees in a plot were identified and their geographic coordinates plotted, I began the process of recording health and size metrics. These metrics include: ash canopy condition (AC) (1-5) (Table 1, Figure 6), diameter at breast height (DBH) in inches, presence/absence of EAB exit holes, presence/absence of epicormic sprouts, and the presence/absence of woodpecker holes. Ash canopy condition ratings were done by standing at the base of an ash tree, or other location nearby where the canopy of that tree could be seen and the rating correlating to the appearance of the canopy was recorded. The canopy condition rating scale (1-5) was developed by Smith (2006) as well as FIA canopy health ratings.

Figure 6. Corresponds with the ranking system in table 1. (Knight 2014)

Table 1: The canopy condition rating system used to classify ash tree canopy conditions (Smith 2006).

Rating Description 1 Ash tree with a full, healthy canopy 2 Ash tree with a thinning canopy, but no dieback 3 Ash tree with dieback, defined as dead twigs or branches near the top of the tree, exposed to sunlight. 4 Ash tree with less than 50% of a full canopy, which could occur through a combination of dieback and thinning. 5 Ash tree with a dead canopy, defined as no foliage in the canopy. The canopy is counted dead even if live epicormic sprouts on the trunk or stump are present.

Diameter at breast height was collected by standing at the base of an ash tree and wrapping a loggers tape measure (a flexible tape measure converted to show diameter) around the trunk of the tree at 1.3 meters above ground level on the upslope side of the tree. Presence/absence of EAB exit holes was collected by observing whether or not any exit holes, characterized by their “D” shape appearance, were visible. the presence/absence of epicormic sprouts was also recorded for each ash tree within the plots. Epicormic sprouts are shoots that occur on the main trunk of ash trees and usually occur as a sign of stress and damage to the bole of the tree caused by EAB larval feeding (Knight et al. 2013). In twenty-six of the plots, twenty white ash trees were randomly selected for insecticide treatment by using a random number generator. Sixteen ash trees from the remaining plot were selected for treatment due to limited extra funding. The original number of treatment plots was to be twenty-seven, but due to non-EAB related factors one treatment plot was eliminated. The subset of ash trees (507) in these treatment plots that were selected for treatment were injected with the insecticide emamectin benzoate (Tree- ägeTM, Arborjet Inc., Woburn, MA, USA). This insecticide was injected into the base of the trees through one-way valves that are drilled and tapped into the base of the trees. For maximum efficacy the one-way valves were placed every 6 inches around the trunk of the tree within 12 inches from the soil. The DBH of a tree selected for insecticide injection determines the amount of insecticide needed to successfully treat the tree (U.S. EPA 2010). Due to the process by which the insecticide is translocated throughout the tree, injection treatments were conducted in June and July of 2015, a time of the year in northern Pennsylvania when trees are actively transporting compounds through their vascular systems. The treatments were conducted by Forecon, a private forestry contractor from New York that was hired by the U.S. Forest Service. The treatments were supervised by Steven Forry, a qualified member of the U.S. Forest Service from the Marienville ranger station. In all treatment plots the plot center tree was intentionally left untreated to serve as a control (Figure 4) in each of the plots. In the summer of 2017, I returned to the ANF to begin the second year of data collection. I visited each of the treatment plots, excluding one plot that was selected as a

13 timber cut area and recorded all of the metrics that were recorded in 2015. Since all of the ash trees were tagged and geographic location recorded for display on a gps unit, the time required to record data was drastically shorter than during the initial data collection. Although data collection times were shorter in 2017, there were still delays and plot maintenance was required. The aluminum tags placed on the ash trees were susceptible to porcupine consumption, trees falling on them and knocking them loose from the tree to be buried in leaves from the previous year, and ash trees that had fallen on the side where the tag was located made it difficult to accurately determine which ash tree it was I was looking at. Using the gps points and process of elimination I was able to determine which trees had suffered non-eab related damage and record these changes appropriately.

Statistical Methods Once data collection was complete from the 26 treatment plots, I began transferring the data from paper data sheets that were used for field collection into a .csv file matching using the same format used for the 2015 data. All statistical tests were performed using base R v. 3.3.1 (R Core Team 2016). I began data analyses by dividing the ANF into four quadrants (regions). I decided to divide the ANF into regions because after data collection in 2015 it was clear that the southern portion of the ANF had lower ash health than the northern portion and this observation was supported by data from initial detection reports (USDA 2018). These regions are NE, NW, SE, and SW (Table 2). In addition to region, other metrics of ash tree health that were recorded during data collection were a focal point of data analyses. Among these other metrics were DBH, AC, and presence/absence of epicormic sprouts with AC ranking the highest in regards to analyzing ash tree health. The 2015 and 2017 data sets were kept separate in order to do independent analyses of the two data sets. To test the efficacy of the insecticide treatment on ash trees I calculated the change in canopy condition from 2017 to 2015. This was done by subtracting the 2015 canopy condition from the 2017 canopy condition (AC17-AC15). The average change in canopy

14 condition was then calculated at a plot level. To meet assumptions of constant variance and normality, I log-transformed the absolute value of average change in canopy condition. I multiplied this by the sign of the original data to retain the correct sign in the transformed data. I then did an analysis of covariance (ANCOVA) to compare the variability between the log(change in canopy condition), topographic slope of plots, and the interaction between slope and change in canopy condition. To test the resource dilution and resource concentration hypotheses, I used analysis of covariance (ANCOVA) to test whether average rate of ash decline of all untreated trees depended on the log(density) of untreated ash trees, and whether this relationship differed between upper or lower slope sites. Several other metrics were analyzed to measure the decline in health of ash trees and the possibility of a treatment threshold. When analyzing the epicormic sprout data, trees that had no change and those trees that had an epicormic sprout in 2015 but not in 2017 were combined because they were not gaining a sprout. Those were then compared to the trees that gained a sprout in 2017 and an ANOVA test was conducted to test the significance between the treatment and change in presence/absence of epicormic sprouts. Like the average change in canopy condition data, the presence/absence of epicormic sprout data also had to be log transformed due to the data being a binomial variable. To test the possibility of a treatment threshold the change in canopy condition of the focal tree from each plot was used as well as an ANOVA test to the significance between treatment and change in canopy condition.

Results

Ash Health and Densities: Ash densities in treatment plots varied, ranging from 23 to 173 ash trees per treatment plot. The NE region contained the most plots (n=18) with 1,274 ash trees, followed by NW region (n=4), SW (n=3), and SE region (n=1) for a total of 1,651 ash trees. Due to the variation in ash densities in the treatment plots, the percent of treated ash trees

15 relative to the total ranged from 86.9%--9.7%, including the plot where only sixteen ash trees received treatment. Ash tree canopy condition declined from 2015 to 2017 in both untreated and treated trees (Table 2).

Table 2: A shift in canopy condition from healthy to less healthy can be seen in both non-treated and treated trees from 2015 to 2017.

Non-Treated Ash Trees n=1144 Canopy Condition 2015 2017 1 130 12 2 505 196 3 408 725 4 82 144 5 19 67

Treated Ash Trees n=507 Canopy Condition 2015 2017 1 83 13 2 224 126 3 162 302 4 34 43 5 4 23

Treatment Efficacy: The applied basal injected treatment, emamectin benzoate, in the twenty-six treatment plots had little to no effect on preventing the decline of ash trees (Table 3). Both treated trees and non-treated trees showed a similar average rate of decline in canopy condition (treatment effect, P = 0.33, Table 3). Additionally, slope location had no significant effect on the average rate of decline of ash tree canopy condition (P = 0.06, Table 3) (Figure 7).

Figure 7. Neither treatment, slope, nor their interaction had a significant effect on the average rate of decline in canopy condition. Each point is the average rate of decline for the trees in a given plot. “L” represents lower slope plots and “U” represents upper slope plots.

Table 3. Results of ANCOVA test comparing the variability between log(change in canopy condition), slope of plot, and the interaction between treatment and slope.

df Sums sq. F-value p-value

Trt 1 0.430 0.95 0.33

Slope 1 1.619 3.57 0.06 Trt:slope 1 0.087 0.19 0.66

Average Rate of Decline: Ash health declined more rapidly in plots with low ash density than in plots with high ash density (P = 0.008, Table 4, Figure 8). I found that the rate of decline was approximately halved for a doubling of log-density. The results from this test support the resource dilution hypothesis because lower ash densities exhibit the greatest decline in health. Additionally, I found that decline in canopy condition was greatest in healthier trees (AC1 & AC2) than less healthy trees (AC3 & AC4) (Figure 9). The results of this ANOVA showed a 21.6% (95% CI, 0.92 - 2.00) average decline in canopy condition of all untreated ash trees.

Table 4. Results of ANOVA test comparing the variability between the average change in canopy condition and log(n), slope of plot, and the interaction between log(n) and slope position.

df Sums sq. F-value p-value log(n) 1 1.447 8.56 0.008

Slope 1 0.612 3.62 0.070 log(n):slope 1 0.073 0.43 0.518

Figure 8. Untreated ash trees in plots with lower ash density have a greater rate of decline in health than plots with higher ash density. log(n) is the log of ash density in plots and the rate of decline is the average decline of canopy condition. Upper slope plots are represented by the gray line and dots and lower slope plots are represented by the black line and dots.

Figure 9. Subplots are shown to represent the corresponding ash canopy condition in 2015 (AC1-AC5) of both treated and non-treated ash trees. A positive change in canopy condition represents a decline in health due to the canopy condition ranking system (Table 1).

Other Indicators of Health Decline A major goal of this project was to assess how the percentage of treated ash in a plot was related to the canopy condition of an untreated focal tree at the center of each plot. Analysis of variance showed that the condition of the focal tree was unrelated to the percentage of treated ash (P = 0.56). Epicormic sprouting is often a stress response, and while presence of epicormic sprouts increased by 40% from 2015 to 2017, there was no

20 significant difference between treated and non-treated trees (P = 0.85). The results do show however, that treated trees in upslope plots gained 12% more epicormic sprouts than other slope/treatment combinations.

Discussion Rate of Decline My results show support for the resource dilution hypothesis (Yamamura 2002). Ash trees in plots with low ash densities declined at a faster rate than ash trees in plots with high ash densities from 2015 to 2017. The resource dilution hypothesis predicts that pest populations will reach higher numbers and cause more damage when hosts are at low densities. One way this might happen is with random dispersal by a pest population of fixed size: dispersal to fewer hosts will create a higher pest load than dispersal among more hosts. Another mechanism driving resource dilution depends on pests targeting stressed trees. Since EAB is a host-specific pest that cues in on stress pheromones released by stressed trees, it is possible that in low ash density plots stressed trees that are releasing pheromones are more easily located than in a high ash density plot (McCullough et al. 2009). Bowen and Stevens (2018) focused on ash stands in hardwood swamps and the results from this study show that untreated ash trees exhibit total mortality when left untreated. They conducted research in three hardwood swamps in southern Michigan, close to the EAB epicenter. The results from this study show that large ash trees, those > 5 inches DBH exhibited total mortality but did not address the question of density dependence (Bowen and Stevens 2018). A previous study on the density-dependent survival of ash trees and decline of ash trees in North America due to the effects of the emerald ash borer beetle has been conducted closer to the epicenter of the now widespread epidemic. Knight et al. 2013 conducted a research project to monitor the decline of ash trees in stands throughout Ohio. Their data collection began in 2005 in 31 sites distributed throughout central Ohio and Northwestern Ohio. Using the same metrics for measuring ash tree health as I have used in the research as a part of this project, they recorded the decline in health of the ash trees for

21 six years. At the end of a 6-year study, Knight observed that the survival rate of ash trees was 0, but decline in health occurred faster in plots with low ash density. The results from this study are analogous to my results and show support for the resource dilution hypothesis. This study on the decline of ash due to the effects of EAB is most comparable to my study, but all research to date across varying settings and Fraxinus spp. shows >99% ash mortality if ash trees are left untreated or the treatment is ineffective (Knight et al. 2013; Flower et al. 2013; Bowen and Stevens 2018; Flower et al. 2015; and Smith 2006).

Treatment Efficacy The use of emamectin benzoate to treat the ash trees in these research plots was ineffective at preventing the decline of ash health. Both treated and untreated trees exhibited a decline in health and the slope position of plots had no effect on the efficacy of the treatment. This was an unexpected result as previous studies (Flower et al. 2015 and McCullough et al. 2011) show that using this treatment on lightly affected ash trees (AC1 & AC2) can stabilize canopy condition. The same studies, in conjunction with my results show that ash trees more heavily impacted prior to treatment (AC3 & AC4) have mixed results in stabilizing the canopy condition and often those trees continue to decline in canopy condition. Flower et al. 2015 shows results consistent with the findings of my research. Flower et al. 2015 conducted research at an ash tree plantation with ash trees dated using dendrochronological methods to be ~35 years old at the time of testing. The ash trees in their study exhibited signs of EAB infestation upon start of their research with >80% of ash trees having EAB exit holes present. They applied a basal injected emamectin benzoate treatment to 32 ash trees and had 21 control ash trees that received no treatment. They hypothesized that different initial ash tree canopy conditions would differ in decline after receiving insecticide treatment. Canopy condition was recorded for three years and their results showed that ash trees initially rated AC1 showed no significant decline, AC2 showed improvement or no decline, and AC3 and AC4 showed a decline in canopy condition. At the end of the four-year study all of the untreated ash trees were dead, consistent with their

22 hypothesis. In addition, 16% of the trees that received treatment showed a decline in canopy condition to AC5. This study did not test density dependence, yet it shows that decline in canopy condition occurs in both treated and untreated ash trees. The failure of the applied treatment to protect ash trees meant that this experiment could not test for associational protection and, by extension, any threshold of the effect. The results from my tests show that even the trees that appeared healthy upon receiving the treatment showed a decline in canopy condition. However, this does not mean that all treated trees exhibited a decline in canopy condition, but rather the averages show a decline. This could be due to having only two years’ worth of data and perhaps the treated trees that declined from an AC1 canopy condition rating to an AC2 or AC3 rating will stabilize with continued insecticide application. If the canopy condition of treated ash trees in the ANF does stabilize, the opportunity to test for associational protection may be present. At least one study hints at the possibility of associational protection in ash populations. O’Brien (2017) found that associational protection provided by ash trees treated with insecticide to untreated ash trees may be possible, but found no strong evidence of support for associational protection. O’Brien analyzed ash tree health in metro parks near Dayton, Ohio and hypothesized that treated trees will provide associational protection to untreated ash trees by acting as sinks for EAB. Early in the study O’Brien found that treating ~3% of ash trees in stands showed evidence that associational protection may exist in stands where EAB impacts are low at the time of treatment, yet at the end of the study nearly all ash trees were dead. The existence of a treatment threshold may exist for ash trees in the fight against EAB in a more controlled environment than a large, continuous forest setting. A treatment threshold may exist in a more controlled environment, perhaps in a localized stand of ash trees, the treated ash trees may act as sinks for EAB (Barbosa et al. 2009; McCullough and Mercader 2012). By the treated ash trees acting as sinks, those treated trees would kill EAB before they inflict fatal damage to the untreated trees, thereby providing associational protection. This could prove as a useful in management strategy for combatting EAB because treating only a subset of an ash population in the early stages of EAB infestation

23 could save considerable amounts of money, and if nothing else the trees that are treated and survive will help to preserve the genetic diversity of ash. The use of the potentially beneficial insecticide emamectin benzoate as a treatment against EAB does not come without unwanted risks. The use of this insecticide poses the possibility of affecting non-target species. Since the insecticide is transported through the tree and into the leaves by natural uptake rather than direct application, this results in variable amounts of the insecticide being present in the leaves and fruits. These variable amounts of insecticide may affect non-target species such as mammals, insects, and even fish can be affected by emamectin benzoate residuals (U.S. EPA 2009). There are currently no findings on the exact effects emamectin benzoate can have on these non-target species, only statements warning that its use may cause harmful effects on non-target species.

Plot Slope Location Although not statistically significant, ash trees in upper slope plots showed a slightly greater decline in canopy condition than ash trees in lower slope plots. This is consistent with a previous study examining the effects of lower available nutrient levels at upper slopes than lower slopes (Royo et al. 2010). Royo et al. 2010 hypothesized that the less healthy canopy conditions of ash trees in upper slope position plots were due to lower available levels of cations (calcium and magnesium). While they did not measure soil cations directly, they showed that > 50 percent of lower slope position plots contained plant species that are indicators high cation levels whereas only 7 percent of upper slope position plots contained indicator species. In addition, they found that foliar cation concentrations were 29-39% higher in the vegetation of lower slope position plots than upper slope position plots (Royo et al. 2010). With additional years of observation and data collection the slight difference in canopy decline shown in my data may become significant. This is important to consider because it could alter and shape the spread of EAB as populations disperse from one area to another. If ash trees at upper slope plots tend to be more stressed in general, these could be

24 the areas that are more easily located by EAB that may cue in on plant stress pheromones (McCullough et al. 2009).

Other indicators of Ash Health Decline Exit holes left by EAB as they exit the tree are identifiable characteristics that can be used to gauge the severity of EAB infestation. Data on this metric were recorded for my study, however it was not used for analyses due to minimal exit holes visible during data collection. EAB exit holes are distinguishable by their “D” shaped appearance and are easily identifiable when they appear at heights on trees visible when standing at the base of ash trees. The reason for minimal exit hole counts in my data is that EAB tends to initially attack ash trees in the canopy and work their way down to areas visible on the ground (Cappaert et al. 2005). Since the time of development from larvae to adult, the time at which EAB emerge from ash trees by boring their way out from the inside of the tree it can take several years for visible exit holes to be present. Minimal counts of woodpecker holes in my data is consistent with the lack of visible EAB exit holes at the time of data. Visible signs of woodpecker predation on EAB is not visible during early stages of infestation for the same reason exit holes are not. Although not used in my analyses, mortality of EAB by woodpeckers is an important aspect to combating EAB as they have been shown to account for 9-95% EAB mortality in infested trees (Cappaert et al. 2005). The presence of epicormic sprouts can be another visible indicator of EAB infestation in ash trees (Knight et al. 2014). The presence of epicormic sprouts is generally caused by phloem damage in the bole of ash trees. Although it is a visible indicator of EAB damage caused to ash trees epicormic sprouts can occur from other causes of stress. The results from my research show that the presence of epicormic sprouts increased among all trees and although not significant, treated trees in upslope plots exhibited the largest increase in epicormic sprouts.

Implications and Future Research The results from this study on the density-dependent survival of ash in the Allegheny National Forest show support for the resource dilution hypothesis. Plots in my study with low ash densities exhibited faster rates of decline than plots with higher ash densities and this is consistent with another study that showed, for the first time, support for the resource dilution hypothesis with EAB and ash trees (Knight et al. 2013). Although plots of consistent size were used throughout the ANF those plot boundaries were merely arbitrary lines in a large continuous forest with ash present meaning the plots used in my research are not segmented from the surrounding forest. In an urban setting such as along streets or in parks it may be possible to treat 100% of ash trees with emamectin benzoate before significant declines in canopy condition occur, but in a forest setting such as the ANF that isn’t a possible goal. Strategies for managing segmented ash stands can implement the evidence supporting the resource dilution hypothesis. Managers in areas with segmented ash stands may be inclined to think that removing a portion of ash trees will help slow the decline of ash trees in their areas if treating 100% of the ash population is not possible. Doing so would only accelerate the rate of decline because with lower ash densities A. planipennis are more concentrated to those remaining ash trees where their populations can rise and their negative effects are more rapidly expressed. As mentioned in the introduction to this thesis, my research is only a portion of a larger research project on ash in the Allegheny National Forest. In addition to my research on the decline and treatment efficacy of insecticide on ash trees affected by EAB in the ANF an in-situ genetic conservation project is also taking place in my research plots. The goal of the genetic conservation project is to analyze the genetic diversity of ash in the ANF and through insecticide treatment preserve the genetic diversity of the ash population in the ANF. In 2016 foliar samples were taken from 17 of my treatment plots for genetic analyses. Flower et al. 2018 conducted genetic analyses of 352 ash trees, 274 treated trees and 78 non treated trees. Their findings showed that if all treated ash trees that were sampled survive with continued insecticide treatment that 97.7% of the alleles represented by the sample population will be preserved. A secondary goal of this research was to find an optimal

26 number of ash trees that need to be treated to still conserve a substantial amount of genetic diversity. The results showed that reducing the number of treated trees by half would still capture 81.1% of alleles. This is important to consider because although my data from the first two years of insecticide treatment shows the treatment to be ineffective, if only half of the treated trees survive a high percent of the genetic diversity of ash in the ANF will be preserved. I am hopeful that the funding that provided me the opportunity to participate in this research project will continue to be granted for the next 7 years. I am also hopeful that data collection in my research plots continues so a detailed understanding of the density dependent survival of ash and treatment efficacy in a large forest setting can be more understood than what is presented in this thesis.

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