Shade-tolerant species may become more important in ash-dominant forests after emerald ash borer

Shade-tolerant tree species may become more important in ash dominant forests after emerald ash borer (Agrilus planipennis) infestation LESLIE M. KOLLAR AND CAITLIN A. MORRISSEY Department of Biology, Washington & Jefferson College, 60 South Lincoln Street, Washington, PA 15301

Abstract. Removal of an overstory tree species has the potential to change forest composition depending on the species’ dominance in the community. Infestations affecting canopy tree species in the past century include hemlock woolly adelgid (Adelges tsugae), chestnut blight (Cryphonectria parasitica), and Dutch elm disease (Ophiostoma ulmi). Currently, in northeastern United States, ash trees (Fraxinus spp.) are being infested with the invasive, exotic emerald ash borer (Agrilus planipennis), which has resulted in the loss of millions of ash trees. Our objective was to predict what will take the place of ash in 2 forested sites that differ in the levels of overstory ash. We expect that the gaps created by loss of ash will be replaced by shade-tolerant species such as sugar maple (Acer saccharum) because these species are already dominant in the canopy and well established in the understory. In this study, we established 6 400-m2 forested plots in each of 2 sites (Washington Co., PA and Hancock Co., OH) as a part of the Permanent Forest Plot Project (PFPP) sponsored by the Ecological Research as Education Network (EREN). In each plot, we calculated importance values (IVs) based on with ash and without ash for each inventoried species. IVs significantly varied across species at Abernathy Field Station (AFS) (ANOVA, F = 7.02; d.f. = 13; p = 0.00) and Rieck Field Station (RFC) (ANOVA, F = 3.67; d.f. = 16; p = 0.001), but IVs did not significantly change across species after ash was removed from the calculations at AFS (ANOVA, F = 0.25; d.f. = 1; p = 0.62) and RFC (ANOVA, F = 0.868; d.f. = 1; p = 0.358). Although our data was not statistically significant when ash were removed from IV calculations, we predict that sugar maple (AFS and RFC) and wild black cherry (AFS) will be the likely species to fill canopy gaps because these species are shade-tolerant, dominant in the forest canopy, and well-established in the understory.

Keywords: emerald ash borer; loss of a canopy species; forest succession; ash (Fraxinus); shade-tolerant species.

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INTRODUCTION The emerald ash borer (EAB; Agrilus planipennis) currently infests ash trees (Fraxinus spp.) in many forests in North America, with Michigan and Ohio being the core of the infestation.

The native range of this phloem-feeding, iridescent green beetle is northeastern China, Korea, Japan, Mongolia, Taiwan, and eastern Russia. By 2002, EAB was detected in Detroit, Michigan; however, the beetle is thought to have been present in the area since the 1990s.

Scientists hypothesize that the beetle found its way to the US via packing chips (Cappaert et al.

2005). Since the introduction of EAB, 15 US states and 2 Canadian provinces have reported the presence of EAB, which has resulted in the killing of millions of ash trees (Knight et al. 2012) Female EABs lay their eggs on the surface and in the crevices of ash bark. Following hatching, the larvae eat their way through the bark and feed on the phloem and outer sapwood.

After one year of development, EABs reach the adult stage and exit the tree leaving their telltale sign of infestation, the D-shaped exit hole. The infested ash tree will experience canopy dieback, loss of bark, and production of epicormic shoots. Complete mortality of an afflicted ash tree will be reached after approximately 5 years of infestation (McCullough and Roberts 2002).

Following ash mortality, the trees will fall and leave behind openings in the canopy, allowing for more light to be exposed to the forest understory. Canopy gaps can result as a consequence of disturbance, such as weather, logging, and pathogens, and are a crucial factor in forest regeneration, successional change, and species composition (Banal et al. 2007).

When canopy gaps result from loss of individuals of a foundation tree species, significant changes to forest dynamics may occur. Foundation tree species are taxa that are abundant in a forested area and are responsible for adjusting the habitat in such a way that associated species can survive in the environment. However, a worldwide negative trend towards foundation species decline due to the effects of pathogen infestations has been observed (Ellison et al. 2010). In particular, there has been an increase in outbreaks of non-indigenous pathogens targeting the foundation taxa. In most cases, the chance of extinction is not a key

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concern because individuals of a foundation species are common and abundant. An example of a foundation species in decline is the eastern hemlock (Tsuga canadensis), which has decreased in number over the past several decades due to climate change and insect infestation. A sap-sucking, flightless insect called the hemlock woolly adelgid (Adelges tsugae) is currently targeting this tree species. The hemlock woolly adelgid, which is native to Japan, was introduced to the US in the early 1950s and has rapidly spread since the 1980s. With the co-occurring and increasing effect of climate change, further loss of this foundation species is expected. In turn, the decline of foundation species could lead to long-term successional changes in forest ecosystems (Ellison et al. 2010).

Chestnut blight (Cryphonectria parasitica), whose host is American chestnut (Castanea dentata), is another invasive pathogen that has had profound effects on eastern North American forests. Before the introduction of chestnut blight, American chestnut had been a dominant canopy species in the upland forest ecosystems of eastern North America. However, this important species now exists as an understory tree or shrub after decades of chestnut blight infestation. Following the decline of American chestnut in Coweeta Basin, NC, from 1934 through the 1990s, a significant increase in species diversity was observed (Elliott and Swank 2008). Eastern hemlock, a shade-tolerant species that was not dominant in the canopy, became more important in mostly low-terrain areas near streams. Tulip poplar (Liriodendron tulipifera), which is a species often associated with early succession and disturbance, increased in importance into the 1990s, especially in moist coves. Chestnut oak (Quercus prinus) and red maple (Acer rubrum), which were co-dominant species before the introduction of chestnut blight, were the common species to take the place of American chestnut across all environments within the Coweeta Basin (Elliott and Swank 2008).

Due to the increasing number of non-native pathogens affecting native tree species, researchers are concerned with predicting forest compositional changes and tree species that will become more prominent after the loss of a common canopy species. Using a 50-year

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record of spatial dynamics, Lin and Augspurger (2008) observed a decline in prominently oakhickory forests and an increase in sugar maple (A. saccharum). The authors observed that sugar maple was aggregating in association with disturbances, such as fallen elm (Ulmus spp.) trees killed by Dutch elm disease (Ophiostoma ulmi). Other species that are intermediate shade-tolerant can also aggregate at a gap. Such tree species include wild black cherry (Prunus serotina), white pine (Pinus strobus), red maple (A. rubrum), red oak (Q. rubra), and white ash (F. americana). Similar results were also shown in a simulated stands study using the SORTIE model, in which gaps with 10%, 20%, 35%, and 50% of the total basal area were removed. As gaps became larger, more growth-release episodes were predicted among recruited saplings and more species in total, including both intermediate shade-tolerant and shade intolerant species, were recruited (Banal et al. 2007).

Based upon knowledge of past and current pathogens affecting common canopy species, our goal is to predict whether significant forest compositional changes might occur following EAB infestation of ash trees. Specifically, our objective is to predict what may take the place of ash in 2 forested sites that differ in the levels of overstory ash. We predict a shift towards an increase in prevalence of shade-tolerant species in both sites because they are already dominant in the canopy and well established in the understory.

MATERIALS AND METHODS Study Sites We established 2 sites—one at Abernathy Field Station (AFS; Washington Co., PA) and one at Rieck Field Center (RFC; Hancock Co., OH)—in 2 mesophytic forests. Washington Co. (40.13o N, 80.18o W) has a mean annual air temperature of 9.3o C and receives approximately 971 mm of rain annually (US Climate Data 2012a). The growing season lasts 121-180 days (Penn State). AFS consists of 57 acres and has been disturbed by a gas line right-of-way (ROW) that was installed in the 1930s, and expanded in 2009. The plots in this location were

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established interior and anterior to the ROW. The interior plots were established 5 m from the ROW, and the anterior plots were located 20 m from the interior plots. Hancock Co. (40.96o N, 83.55o W) has a mean annual air temperature of 9.8o C and receives an annual precipitation of 876 mm (US Climate Data 2012b). The growing season lasts 138-224 days (USDA 2002).

EAB afflicted ash trees were first detected in June 2009 in Washington Co. (Penn State, 2012), but the infestation has not yet been detected at AFS. The EAB infestation was detected in Hancock Co. in 2005 (Henry, 2005), and we have observed ash trees afflicted by EAB infestation at RFC.

Plot Establishment At AFS, we established 6 paired 400-m2 plots (square) to contribute to the Permanent Forest Plot Project (PFPP) sponsored by the Ecological Research as Education Network (EREN). We divided each plot into 16 25-m2 subplots (square). Each of the subplots was further divided into 4 quads, representing NW, NE, SW, and SE (Fig. 1). We used a clinometer (Model PM-5/360PC, Suunto, Vantaa, Finland) to measure the slopes of the plot during establishment. At RFC, we established 6 400-m2 (circular) plots to contribute to the PFPP. Three 25-m2 subplots (circular) and 3 1-m2 microplots (circular) were also established for survey.

Data Collection Following PFPP protocol (Kuers et al. 2012), we inventoried all trees with diameters at breast height (DBH; 1.37 m) greater than 2.5 cm within each plot. At AFS, we marked trees with aluminum tags and wire (DBH <5 cm) or with aluminum tags and aluminum nails on the uphill side of the trees (DBH >5 cm). All aluminum tags were labeled with W&J, plot name, and tree number. In the case of a tree with multiple stems, each stem was labeled with a separate aluminum tag (i.e. with one tag labeled 1.1 and the other tag labeled 1.2 as the tree numbers).

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We inventoried trees at RFC by recording a treeâ&#x20AC;&#x2122;s distance from the center of the plot and by its DBH. Following the PFPP protocol (Kuers et al. 2012), we collected data from each inventoried tree, which included species name, number of stems, DBH, soundness, and canopy classification. We recorded specific data that was not required by the PFPP protocol (Kuers et al. 2012) for ash trees within plots. Information collected included level of canopy dieback, overall health of the tree, and number of D-shaped exit holes between 1.25 m and 1.75 m in height on the tree. We collected understory data in 3 subplots and in 3 mini-plots per plot. At AFS, 3 of the 16 subplots were randomly selected (www.random.org) to be surveyed, and the miniplots were established by setting 1-m2 quadrats at the NE or SE (depending on the plot) corners of the randomly selected subplots.

Data Analysis We calculated importance values (IVs) for each species per plot at AFS and RFC. IVs were based upon the number of individuals (>2.5 cm at DBH) present per 400 m2 relative to the total number of trees in the plot (relative density) and the cross-sectional area of the stems at DBH relative to total cross-sectional area in the plot (relative basal area). Dead trees and vines were excluded from our IV calculations. Using Microsoft Excel, we calculated 2 IVs per species: with ash present in the plot and with ash excluded from the plot. We used a Shapiro-Wilk Test for Normality of IV with and without ash present for each species. For species present in more than 2 plots at AFS, we have insufficient (p > 0.1) evidence for non-normality for sugar maple, hawthorn (Crataegus spp.), and wild black cherry and sufficient (p < 0.05) evidence for nonnormality for slippery elm (U. rubra). For species present in more than 2 plots at RFC, we have insufficient (p > 0.1) evidence for non-normality for sugar maple, hawthorn, and American elm (U. americana). We used a univariate 2-way analysis of variance (ANOVA) to detect effects of the presence of ash (fixed factor) and species (fixed factor), and their interaction, on species IVs (dependent variable). Alpha was set at 0.05 for all statistical analyses.

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RESULTS Fifteen tree species were inventoried in the 6 AFS plots, and 20 tree species were inventoried in the 6 RFC plots. The most important species among the plots at AFS were sugar maple (∑IV: 430.31; Table 1) and wild black cherry (∑IV: 300.35; Table 1). White ash (∑IV: 164.12; Table 1), ranked third in importance among inventoried species at AFS. At RFC, the most important species is sugar maple (∑IV: 336.02; Table 2). Green ash (F. pennsylvanica; ∑IV: 79.52; Table 2), white ash (∑IV: 43.63; Table 2), and blue ash (F. quadrangulata; ∑IV: 34.15; Table 2) rank fifth, tenth, and fourteenth, respectively, in importance at RFC. Overall, IVs differed significantly across species at AFS (ANOVA, F = 7.02; d.f. = 13; p = 0.00; Fig. 2) and RFC (ANOVA, F = 3.67; d.f. = 16; p = 0.001; Fig. 3).

To simulate complete ash mortality after an EAB infestation, we removed ash trees from the IV calculations. Across species, IVs with ash present and IVs without ash present were not significantly different at AFS (ANOVA, F = 0.25; d.f. = 1; p = 0.62; Fig. 2) and RFC (ANOVA, F = 0.868; d.f. = 1; p = 0.358; Fig. 3). The interaction factor of ash presence and species was not statistically significant at AFS (ANOVA, F = 0.06; d.f. = 13; p = 1.00) nor RFC (ANOVA, F = 0.106; d.f. = 16; p = 1.00).

DISCUSSION The loss of a canopy species has the potential to have lasting effects on a forest’s composition and ecology (Elliott and Swank, 2008). With the introduction of an increasing number of non-native pathogens, the loss of a canopy species is becoming more prevalent (Ellison et al., 2010). Such exotic infestations affecting tree species in the past century include hemlock woolly adelgid, chestnut blight, Dutch elm disease, and emerald ash borer. Using IVs, the goal of our research was to predict what tree species may take the place of ash trees once they leave the canopy following EAB infestation. The results of our study reveal that importance

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values across species do not significantly change after ash are gone at either AFS (Fig. 2) or RFC (Fig. 3), despite the 2 sites differing in ash overstory.

Although we draw this conclusion from these data, it is unlikely that these forested sites will not experience forest compositional changes over the long-term due to EAB infestation. We believe that our study design and subsequent analysis do not accurately represent the forest composition of the 2 sites. Because our sample size was small (n = 6 plots per site), IVs had the potential to be not representative of the population. For example, only one wild black cherry was found in the 6 plots at RFC. However, since it had a high value for relative basal area, the importance value was skewed towards a higher value (â&#x2C6;&#x2018;IV: 88.97; Table 2). Furthermore, the mean IVs were calculated based upon the number of plots in which a species was present and not based upon its presence or absence in all 6 plots per site. Therefore, this also had the effect of skewing the IVs towards higher values, especially for the species only present in 1-2 plots. Increasing our sample size and accounting for a species absence in plots would have given us a more accurate representation of the 2 sites.

Looking past the statistical results, we draw some conclusions by noting each speciesâ&#x20AC;&#x2122; plot frequency (number of plots a species was present) and individual frequency (total number of individuals in a site). Sugar maple and wild black cherry are the dominant species at AFS (Table 1), while sugar maple seems to be the only ubiquitous species across plots at RFC (Table 2). Since sugar maple and wild black cherry are dominant in the canopy and wellestablished in the understory at AFS (personal observation), these two species are the likely candidates to fill the canopy gaps when the EAB infestation reaches AFS. At RFC, we may soon observe already established small stems of sugar maple filling open canopy gaps and the recruitment of more sugar maple saplings. Sugar maple (AFS and RFC) and wild black cherry (AFS) are also shade-tolerant and intermediate shade-tolerant species, respectively, which have been shown to be well-established in understory environments and to respond well to diffuse light from canopy gaps (Banal et al. 2007).

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Ash regeneration from understory seedlings and saplings is another possibility for overstory ash replacement. Even when forests experience severe EAB infestation, ash seedlings and saplings are not afflicted by the beetle (Kashian and Witter 2011), allowing ash species to theoretically remain competitors in filling the canopy gaps. Since ash species are intermediate shade-tolerant, ash saplings may be able to maintain low levels of growth despite the presence of minimal light (Banal et al. 2007). The minimum overstory ash population needed to support EAB is not yet known, but it is likely that the contribution of ash seedlings and saplings may become more significant with time and as the spread of the EAB infestation moves outward. The potential in ash regeneration relies upon the premise that mature ash trees will produce seeds before they reach mortality due to EAB infestation or other pathogens.

However, ash seed banks have already been observed to be lower compared to pre-EAB levels (Kashian and Witter 2011). Since ash trees are dioecious and reproduce via wind pollination, regenerating ash trees may experience lower reproductive success due to dwindling ash populations. Further research is necessary to predict whether EAB will cause ash trees to follow dynamics similar to Dutch elm disease which causes elm trees to have shorter lifespans but still able to reproduce, or the dynamics of chestnut blight, which has caused American chestnuts to perish before reaching reproductive age (Kashian and Witter 2011).

Competitive factors that are also crucial to consider when predicting what will replace ash are lateral growth increases in trees already established in the canopy and shrub-sapling interactions. Contributions to filling an opening in a forest canopy can come from overstory trees at gap edges, which can respond and increase their stem radial growth rates (Pedersen and Howard 2004). In effect, this competition can limit the role of understory species.

Competitive interactions with non-native shrubs, such as multiflora rose (Rosa multiflora), can limit the ability of saplings to establish themselves in the understory, thus affecting future forest composition and dynamics. A decrease in native tree species has been associated with introductions of invasive shrubs due to direct resource competition and indirect effects of seed

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predation (Meiners 2007). With invasive shrub species such as multiflora rose and Asian honeysuckles (Lonicera spp.) being present in both sites (personal observation), shrub-sapling interactions could be a crucial factor in the future forest composition at AFS and RFC as a consequence of the EAB infestation.

Data at AFS and RFC is planned to be collected over the long-term at the already established plots to contribute to the PFPP. With long-term data, changes in forest composition as a result of EAB can be tracked. Since we believe our data analyses for our current research did not accurately represent the forest compositions of AFS and RFC, another way to analyze data would be to locate all overstory ash trees and to record the small-stems (including both tree and shrub species) and saplings within 12 m of the ash tree. Banal et al. (2007) showed that saplings can respond to diffuse light from peripheral gaps up to 12 m from the gap itself.

Considering, the data from this perspective may provide a more accurate prediction of what will replace ash as a consequence of EAB.

RECOMMENDATIONS Our research involved the collaboration of professors and undergraduate students from Washington & Jefferson College and the University of Findlay. From this collaboration, we benefited from increasing the sample size and expanding the regional focus for our research.

However, we entered some obstacles along the way through our collaboration. One of the biggest obstacles we encountered was the difference in plot construction and data recording.

The professor and students of Washington & Jefferson College set up square plots and divided the entire plot into subplots, and a random number generator was used to randomly select which subplots to survey. The professor and students of the University of Findlay established circular plots, and thus the entire plot could not be easily divided into subplots. Instead of randomly selecting 25-m2 areas to survey as subplots, 3 subplots were automatically established 5 m from the center of the plot. Neglecting to use random selection may have

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prevented us from collecting data that accurately represented the entire plot. Deciding upon one technique for plot establishment based on a deliberation of the positive and negative aspects of each possible technique will be helpful for future collaborations.

Another obstacle we encountered was project commitment. The students at the University of Findlay were not currently enrolled in a research class during the time we were enrolled in our research class, which created a difference in priority levels for the research project. At times, it was difficult communicating and relying on the University of Findlay students to conduct data analyses because of this priority discrepancy. For future iterations of the Biology 412 Long-Term Forest Monitoring class, a beneficial modification may include collaborating with an institution in which students will be enrolled in a class with similar objectives and projects during the same semester.

ACKNOWLEDGEMENTS We would like to especially thank Dr. Jason Kilgore (Washington & Jefferson College) and Dr. Ben Dolan (University of Findlay) for their assistance in overseeing our research. Also, we would like to thank the Abernathy family for generously allowing us to conduct our study on their property in Washington, PA and to thank the University of Findlay for granting us permission to use the Rieck Field Center in Findlay, PA. This project also had support from the Ecological Research as Education Network (EREN) and W&Jâ&#x20AC;&#x2122;s Undergraduate Science Education Grant from the Howard Hughes Medical Institute (#52006323).

REFERENCES Banal, S., Marceau, D.J., Bouchard, A. 2007. Sapling responses to variations in gap densities and spatial configurations modeled using SORTIE. Ecological Monitoring 206: 41-53.

Cappaert, D., McCullough, D.G., Poland, T.M., Siegert, N.W. 2005. Emerald ash borer in North America: a research and regulatory challenge. American Entomologist 152-165.

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Dolan, B., and J. Kilgore. 2012. EAB impacts study: a subgroup study for the EREN Permanent Forest Plot Project. Unpublished.

Elliott, K.J., and W.T. Swank. 2008. Long-term changes in forest composition and diversity following early logging 1919-1923 and the decline of American chestnut (Castanea dentata). Plant Ecology 197: 155-172.

Ellison, A.E., Barker-Plotkin, A.A., Foster, D.R., Orwig, D.A. 2010. Experimentally testing the role of foundation species in forests: the Harvard Forest Hemlock Removal Experiment.

Methods in Ecology & Evolution 1: 168-179.

Henry, T. 2005. Ash tree pest discovered in Hancock County. Toledo Blade. http://www. toledoblade.com/frontpage/2005/04/02/Ash-tree-pest-discovered-in-HancockCounty.html Kashian, D.M., and J.A. Witter. 2011. Assessing the potential for ash canopy tree replacement via current regeneration following emerald ash borer-caused mortality on southeastern Michigan. Forest Ecology and Management 261: 480-488.

Knight, K.S., Brown, J.P., Long, R.P. 2012. Factors affecting the survival of ash (Fraxinus spp.) trees infested by the emerald ash borer (Agrilus planipennis). Biological Invasions.

Kuers, K., Lindquist, E., Dosch, J., Shea, K., Machado, J., LoGiudice, K., Simmons, J., Anderson, L. 2012. Permanent Forest Plot Project (PFPP). Available from the Ecological Research as Education Network at http://erenweb.org Lin, Y., and C.K. Augspurger. 2008. Long-term spatial dynamics of Acer saccharum during a population explosion in an old-growth remnant forest in Illinois. Forest Ecology and Management 256: 922-928.

McCullough, D.G., and D.L. Roberts. 2002. Emerald ash borer. Pest Alert.

Meiners, S.J. 2007 Apparent competition: an impact of exotic shrub invasion on tree regeneration. Biological Invasions 9: 849-855.

L.M. Kollar and C.A. Morrissey Pedersen, B.S., and J.L. Howard. 2004. The influence of canopy gaps on overstory tree and forest growth rates in a mature, mixed-age, mixed-species forest. Forest Ecology and Management 196: 351-366.

Penn State. Length of Growing Season. Available at http://climate.met.psu.edu/www_prod/ features/COAS/PA_growing_season.php Penn State. 2012 Timeline of EAB detection in PA. Available at http://ento.psu.edu/extension/ trees-shrubs/emerald-ash-borer/timeline-of-eab-detection-in-pa US Climate Data. 2012a Climate-Washington-Pennsylvania. Available at http://www.usclimatedata.com/climate. php ?location=USPA1729 US Climate Data. 2012b Climate-Findlay-Ohio. Available at http://www.usclimatedata.com/ climate.php?location=USOH0311 USDA. 2002 Soil Survey of Hancock County, Ohio. Available at http://www.dnr.state.oh.us/portals/12/soils/pdf/survey_pdfs/hancock.pdf

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Fig.1. For all plots at AFS (Washington Co.), each 400 m2 plot was divided into 16 25 m2 subplots (Kuers et al., 2012). Each subplot was further divided into 4 quads (NW, NE, SW, and SE).

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Table 1. Plot frequency (n = 6), tree frequency (n = 277), density (trees m-2), basal area (cm2 at DBH m-2), sum of importance values in all plots (∑IV; relative density + relative basal area), and sum of importance values in all plots when white ash (F. americana) was removed from the calculations for trees inventoried at Abernathy Field Station (AFS).

Species

Plot frequency

Tree Frequency

Density

Basal Area

∑IV

∑IV without ash

Acer saccharum

6

184

0.0767

9.037

430.31

476.86

Prunus serotina

6

45

0.0188

11.547

300.35

380.92

Fraxinus americana

6

22

0.0092

4.220

164.12

---

Ulmus rubra

4

4

0.0017

0.880

115.41

126.42

Quercus rubra

1

1

0.0004

0.651

48.44

52.98

Carya ovate

1

1

0.0004

0.986

35.80

40.40

Acer rubrum

1

3

0.0013

0.422

21.94

26.44

Ulmus americana

1

2

0.0008

0.272

19.57

23.79

Juglans nigra

1

1

0.0004

0.211

17.01

18.54

Liriodendron tulipifera

1

3

0.0013

0.281

16.47

19.62

Crataegus spp.

3

5

0.0021

0.124

14.78

16.48

Celtis occidentalis

1

2

0.0008

0.029

6.16

6.40

Ostrya virginiana

1

2

0.0008

0.011

4.92

5.81

Viburnum prunifolium

1

1

0.0004

0.003

2.55

3.08

Fagus grandifolia

1

1

0.0004

0.003

2.18

2.27

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Table 2. Plot frequency (n = 6), tree frequency (n = 229), density (trees m-2), basal area (cm2 at DBH m-2), sum of importance values in all plots (∑IV; relative density + relative basal area), and sum of the importance values in all plots when ash (Fraxinus spp.) was removed from the calculations for tree inventories at Rieck Field Center (RFC).

Species

Plot Frequency

Tree Frequency

Density

Basal Area

∑IV

∑IV without ash

Acer saccharum

6

130

0.0542

7.320

336.02

356.11

Quercus macrocarpa

1

3

0.0013

5.194

93.23

100.75

Prunus serotina

1

1

0.0004

0.563

88.97

88.97

Ulmus Americana

4

22

0.0092

1.362

88.58

96.35

Fraxinus pennsylvanica

3

3

0.0013

1.237

79.52

---

Carya cordiformis

2

2

0.0008

0.792

56.69

59.03

Ulmus rubra

2

3

0.0013

0.678

53.38

81.77

Fagus grandifolia

1

7

0.0029

2.736

35.05

74.85

Juglans nigra

2

2

0.0008

0.591

46.07

54.15

Celtis occidentalis

2

3

0.0013

0.854

43.96

53.29

Fraxinus Americana

2

2

0.0008

0.322

43.63

---

Aesculus glabra

2

21

0.0088

0.264

42.71

44.17 46.04

Crataegus spp.

3

8

0.0033

0.130

42.06

Fraxinus quadrangulata

1

1

0.0004

0.508

34.15

---

Acer negundo

2

4

0.0017

0.182

34.01

37.07

Acer nigrum

1

5

0.0021

0.264

29.63

31.13

Tilia americana

1

6

0.0025

1.195

22.29

42.65

Carya ovate

1

3

0.0013

0.164

19.21

20.13

Carya carolinae-

2

2

0.0008

0.042

8.20

10.29

1

1

0.0004

0.007

2.65

3.26

septentrionalis Asimina triloba

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Fig. 2. Mean importance values (IVs) of the 14 non-ash tree species inventoried at the Abernathy Field Station (AFS) forest site in Washington Co., PA. Green bars represent IVs with ash trees present, and blue bars represent IVs with ash trees absent. IVs were significantly different across species (ANOVA, F = 7.02; d.f. = 13; p = 0.00) but were not significantly different after ash was eliminated (ANOVA, F = 0.25; d.f. = 1; p = 0.62), while the interaction of ash presence and species was not significant (ANOVA, F = 0.06; d.f. = 13; p = 1.00).

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Fig. 3. Mean importance values (IVs) of the 17 non-ash tree species inventoried at the Rieck Field Center (RFC) forest site in Hancock Co., OH. Green bars represent IVs with ash trees present, and blue bars represent IVs with ash trees absent. IVs were significantly different across species (ANOVA, F = 3.67; d.f. = 16; p = 0.001), were not significantly different after ash were eliminated (ANOVA, F = 0.868; d.f. = 1; p = 0.358), and were not significant for the interaction factor of ash and species (ANOVA, F = 0.106; d.f. = 16; p = 1.00).