Lonicera Maackii)
THE IMPACTS, INVASIBILITY, AND RESTORATION ECOLOGY OF AN
INVASIVE SHRUB, AMUR HONEYSUCKLE (LONICERA MAACKII)
A dissertation presented to
the faculty of
the College of Arts and Sciences of Ohio University
In partial fulfillment
of the requirements for the degree
Doctor of Philosophy
Kurt M. Hartman
November 2005
THE IMPACTS, INVASIBILITY, AND RESTORATION ECOLOGY OF AN
INVASIVE SHRUB, AMUR HONEYSUCKLE (LONICERA MAACKII)
by
KURT M. HARTMAN
has been approved for
the Department of Biological Sciences
and the College of Arts and Sciences by
Brian C. McCarthy
Professor of Environmental and Plant Biology
Benjamin M. Ogles
Dean, College of Arts & Sciences HARTMAN, KURT M. Ph.D. November 2005. Biological Sciences
The Impacts, Invasibility, and Restoration Ecology of an Invasive Shrub, Amur
Honeysuckle (Lonicera maackii) (150 pp.)
Director of Dissertation: Brian C. McCarthy
Invasive species are an environmental problem of increasing global concern.
Invasives have been intentionally and accidentally transported across previously impeding barriers to new regions where they interact with native species. One invasive shrub, Amur honeysuckle (Lonicera maackii), was introduced into the US from
Manchuria in the late 1800s for conservation and horticultural purposes. Since then, it has become ecologically problematic in open areas and forested habitats. The goals of this research were to (1) investigate the impacts of L. maackii on the structure and composition of native plant communities, (2) measure changes in the productivity of overstory trees at invaded sites using dendrochronological techniques, (3) study the growth and biomass allocation of L. maackii seedlings and generate a predictive model regarding their establishment, and (4) investigate the restoration ecology of this species in terms of its eradication and replacement with native tree species. First, using the chronosequence method, sites with various invasion times were sampled, and long- invaded sites were found to have significant reductions in species richness and have simplified structure relative to recently invaded and non-invaded sites. Thus, successional trajectories were likely being diverted by L. maackii. Second, trees were cored, and annual tree-ring growth was measured. Reductions in tree growth indicate that L. maackii is able to successfully compete with overstory trees and significantly suppress productivity. Third, L. maackii seedlings were grown in various combinations
of light and water and glaciated and unglaciated soils. Findings suggest that light was the
most important factor influencing seedling growth. Drought conditions limited
seedlings’ plastic ability to respond to increases in irradiance, and interestingly, glaciated
soil was found to have greater L. maackii growth potential than unglaciated soil. Finally,
the most effective means of restoring sites infested by L. maackii was investigated
comparing the use of a 22-caliber herbicide injection gun and the cut-and-paint
technique. Both methods were equally effective, and L. maackii eradication facilitated
native seedling growth and survival. Overall, findings indicate that L. maackii is an aggressive weed with substantial negative ecological impacts on invaded forest ecosystems. Restoration practices involving prevention and removal of this shrub are recommended.
Approved:
Brian C. McCarthy
Professor of Environmental and Plant Biology Acknowledgements
I would like to express my sincere gratitude to those individuals and organizations that have graciously assisted in this work. My appreciation goes to my advisor, Dr. Brian
McCarthy, for his encouragement, support, and dedication to my education and professional development. My sincere thanks also go to my committee members, Drs.
Irwin Ungar, Morgan Vis, Kim Brown, and James Dyer, for their insights and contributions to these projects. I would also like to thank my family, Ray, Jackie, Bill,
Laura, Mike, Tabitha, Sara, Nellie, Herbert, John, and Eva for their unconditional support and encouragement and many coworkers and friends who assisted in these projects. I am also grateful to the Department of Environmental and Plant Biology, the Ohio Biological
Survey, John Houk Memorial Research Grant Foundation, Five Rivers Metroparks,
Hamilton County Metroparks, Fluor Daniel Fernald, and the US Department of Energy for funding this research.
I would especially like to thank the following who offered multiple forms of support: Mike Cohen, Candace Stewart, Jeanie Heaton, Lee Coleman, Yegan Pillay,
Lorie Fox, Darrin Rubino, Michelle Rubino, Matthew Albrecht, Ryan McEwen, Aswini
Pai, Zachary Rinkes, Audrey Larrimer, Jill Brown, Don Miles, Peter Citro, Karissa Citro,
Justin Walton, Liberty Walton, Harvey Ballard, Phil Cantino, Ahmed Faik, Amy Ryan
Rued, Brandy Morgan, Art Trese, Dawn McCarthy, Dave Nolin, Mary Klunk, Katrina
Arnold, Shawn Foley, John Klein, Jim Mundy, Glenn Matlack, Craig Anderson, Bruce
Carlson, Frank Belleza, Jerry Hintze, Tim Vickers, Jeff Bloodworth, Micah Robertson,
John Graham, Jason Zaros, Katie Lyke, Christy Carter, Steve Carter, Dylan DeRosset, Kevin Lewis, Rob Kaminski, Nanda Filkin, Jeff Lombardo, Rochelle Jacques, Sarah
Bashore, Sarah Stewart, Jyh-min Chiang, Dave Frankel, Lance Glasgow, Christopher
Havran, Dale Cassamata, Bob Verb, Heather Sanders, Harjinder Sardar, Mari-Vaughn
Virginia Johnson, Matt Weand, Lisa Schelling, Peter Schweizer, Wei Zeng, Corie
McCament, Cynthia Riccardi, Cheryl McCreary, Mehai Tomescu, Ross McCauley, J.
Forrest Meekins Egan, Todd Egan, Christine Small, A.Christine Williams Longbrake,
Doug Christen, Aurea Cortes-Palomec, Ross McCauley, Ray Tessner, Rodney Comisar,
Bruce French, Phil Norbert, Shawn Bachtell, Colter Blackford, Rod Norwood, Dave
Klingel, Tracy Parker, Ryan Zellner, Richard Sterns, Peter Jenkins, Gina Carolizzini,
Tara Patrick, Thurman McIe, Deah Lieurance, Brijesh Karakkat, Barbara Grover, Jessica
Cunningham, Trish Lyons, Al Cote, Thich Nhat Hanh, Alexander Sidorkin, Rosalie
Romano, Mary Markowitz, Chris Bailey, Connie Pollard, Harold Blazier, Jeffrey
Harmison, Aaron Mather, Debbie Pierson, James Braselton, James Cavender, John
Mitchell, Ivan Smith, Matt Shipp, Norm Huber, Glenn Diebler, Barb Diebler, Elmore
Beale, Dave Cochran, Rick Keller, Neil Jarvis, Mike Cauley, Helen Michaels, Denise
Pax, Marty Rosezewski, Cliff Reeves, Brandon Messerli, Mark McKinley, Mikihiro Sato,
Robert Bowman, Don Brown, Matt Lenley, Jamie Brunty, and Barry Douglas.
7 Table of Contents
Page
Abstract...... 3
Acknowledgements ...... 5
List of Tables...... 8
List of Figures ...... 9
Chapter 1: Restoration of a forest understory following the removal of an invasive shrub, Amur honeysuckle (Lonicera maackii) ...... 11 Introduction...... 11 Methods ...... 13 Results ...... 20 Discussion...... 26 Chapter 2: A dendroecological study of forest overstory productivity following the invasion of a non-indigenous shrub, Amur honeysuckle (Lonicera maackii) ...... 40 Introduction...... 40 Methods ...... 43 Results ...... 49 Discussion...... 53
Chapter 3: Changes in forest structure and species composition following invasion by a non-indigenous shrub, Amur honeysuckle (Lonicera maackii) ...... 65 Introduction...... 65 Methods ...... 69 Results ...... 75 Discussion...... 79
Chapter 4: Modeling growth and resource allocation of seedlings of an invasive shrub, Amur honeysuckle (Lonicera maackii), in varying light, water, and soil conditions…………………………………………………………………….91 Introduction...... 91 Methods……………………………………………………………………...94 Results ...... 101 Discussion...... 104
Literature Cited ...... 120
8 List of Tables
Table ...... Page
Table 1.1. Analysis of variance of final survival for native tree seedlings...... 34
Table 2.1. Description of sixteen sampled invaded and non-invaded sites ...... 59
Table 2.2. Three-way and two-way MANOVA and follow-up ANOVA results for growth differences for pre- versus post-invasion periods...... 60
Table 2.3. Means of PRE versus POST periods in invaded and non-invaded tree- ring series and t-test results...... 61
Table 2.4. Change in invaded overstory tree growth from PRE to POST periods as predicted by regressing productivity against L. maackii density and L. maackii biomass...... 61
Table 3.1. Comparison of mean species richness for each site along the invasion chronosequence...... 86
Table 3.2. Indicator species results for invasion chronosequence levels within forest strata...... 87
Table 4.1. Edaphic characteristics for glaciated and unglaciated soil treatments...... 111
Table 4.2. Repeated measures analysis of variance analyzing light, water, soil type, and time...... 111
Table 4.3. Four-way multivariate analysis of variance for L. maackii seedling growth...... 112
Table 4.4. ANOVA table of significant one- and two-way factor interactions from preceding MANOVA...... 113
9 List of Figures
Figure ...... Page
Figure 1.1. Percent of final survival of planted seedlings comparing sites across treatments...... 35
Figure 1.2. Final seedling survival (after three years) in eradication treatments...... 36
Figure 1.3. Weibull distribution function estimates for native seedling survival rates in three eradication treatments...... 37
Figure 1.4. Weibull distribution function estimates for native species survival rates in two sites...... 38
Figure 1.5. Seedling height for 1999, 2000, and 2001...... 39
Figure 1.6. Seedling diameter for 1999, 2000, and 2001...... 39
Figure 2.1. Location of 16 sampled sites throughout southwest Ohio, U.S.A...... 62
Figure 2.2. Master chronology for each site indicating raw mean basal area index (BAI) growth for all cored trees at each site (≥ 10cm DBH) versus time ...... 63
Figure 2.3. Magnitude, intensity, and duration of interventions relative to time of initial invasion...... 64
Figure 3.1. Beta-flexible cluster analysis for each stratum using Bray-Curtis similarities...... 88
Figure 3.2. Density regressions of within-stratum changes in structure with increasing L. maackii age at sites...... 89
Figure 3.3. Cross strata congruence analysis using Bray-Curtis similarity and Mantel tests...... 90
Figure 4.1. Shade towers (A) and waterproof roof (B) used to manipulate irradiance and water availability...... 114
Figure 4.2. Lonicera maackii seedling height growth through two growing seasons. Light treatments as proportional irradiance of full sun are indicated by percent values...... 114
Figure 4.3. Treatment means (± SE) for year, light, and water treatments...... 115
10
Figure 4.4. Prediction tree of L. maackii seedling performance constructed using multivariate regression tree (MRT) analysis………...... 116
Figure 4.5. Bar graph describing dependent variables at each multivariate regression tree node……………………………………………………………………...…117
Figure 4.6. Lonicera maackii throughout life history stages: (A) zygomatic flowers formed in pairs, (B) seed of L. maackii including morphologically dormant embryo, and (C) adult plant with acuminate, gradually tapering, leaves (D) fruits located in leaf axils…………………………………………….…..…118
Figure 4.7. Images of adult L. maackii (A) L. maackii dominating the midstory of forests (B) L. maackii with large fruit production in edge habitat (C) understory beneath L. maackii showing low native plant recruitment and diversity (D) resprouting of L. maackii following no treatment with herbicide…....…….119
11 Chapter 1: Restoration of a forest understory following the removal of an invasive shrub, Amur honeysuckle (Lonicera maackii)
Introduction
Invasions of nonindigenous species are often facilitated by anthropogenic disturbances (Hobbs & Huenneke; 1992) and can be problematic in that they can cause further changes in forest attributes including structure, properties, and fundamental ecosystem processes (MacMahon & Holl; 2001). Furthermore, if extremely successful species are the invaders, biodiversity can be essentially reduced to a near monoculture resulting in a community that is low in natural diversity and extremely difficult to restore
(Olson & Whitson; 2002).
The aggressiveness of some invasive species can make wholesale eradication quite difficult; however, their removal at a specific site is a necessary step in the restoration process. Eradication at local spatial scales is especially important because the restoration process, for the most part, proceeds on a site-by-site basis across large areas
(Wiens et al.; 1993).
After removal of invasive species, forest restoration practices may include replanting sites with native species (Ghersa et al.; 2002). This replanting step in restoration is often necessary especially in deciduous forests of the eastern U.S. where viability of seeds is relatively short compared to other habitat types (Barnes et al.; 1998) and where invasives have occupied a site long enough to result in the degradation of a normally short-lived forest seed bank (Collier et al.; 2002).
12 Replanting may have benefits in addition to restoration of forest diversity.
Replanting may inhibit further invasion because native individuals can preempt space and acquire resources, which makes reestablishment by invasives more difficult (Shea &
Chesson; 2002). Natural recovery processes, including succession, are often accelerated by replanting, changing the forest from a non-indigenous, highly modified ecosystem to one with native species and self-sustaining processes. Planting natives may also circumvent the problem of lack of successful recruitment frequently encountered in reestablishment of plant communities (Holmes; 2001). Together these factors contribute to the restoration of a more structurally representative and functional forest community.
Another important benefit from conducting a restoration experiment is that practical experience can be gained in developing a protocol for re-establishing natural forest ecosystems. A plan should successfully integrate abiotic factors (e.g., micro-site variability) as well as biotic factors (e.g., seedling requirements) influencing the restoration of the forest community (Reifsnyder & Lull; 1965). In addition, a consideration of the relative economic costs of different restoration methods can help guide allocation of sponsorship in future, similar restoration programs (Montalvo et al.;
2002).
One of the most challenging invasive plants for forest restorationists in the eastern
US is the nonindigenous shrub, Amur honeysuckle [Lonicera maackii (Rupr.) Herder;
Caprifoliaceae]. This plant has spread to over 26 eastern U.S. states (Hutchinson &
Vankat; 1997), the province of Ontario, Canada, and many counties in southwestern Ohio
(Hutchinson & Vankat; 1997; Trisel; 1997) where this experiment was conducted.
13 Lonicera maackii is only one member of a genus with many known weedy qualities
(Woods; 1993; Schierenbeck; 1994).
A number of factors make L. maackii a threat to native biodiversity and a challenge to restoration practitioners including its ability to resprout following repeated cutting
(Luken; 1990b), possible allelopathic effects on native vegetation (Trisel; 1997), and extended leaf phenology (Trisel; 1997). When L. maackii invades open sites, these areas are often converted into shrub communities (Luken et al.; 1995), and in invaded forests, recruitment and diversity of woody and herbaceous species is often reduced (Hutchinson
& Vankat; 1997; Gould & Gorchov; 2000; Collier et al.; 2002; Gorchov & Trisel; 2003).
Despite efforts to eradicate L. maackii, a fully integrated restoration protocol for the eradication of this species and subsequent replacement with natives has not yet been produced. This precipitated our investigation into the optimal methods for restoring native vegetation following L. maackii removal.
The specific goals of this study were to: (1) quantify the biomass of L. maackii in the study area, (2) compare methods of Lonicera maackii eradication in terms of effectiveness and ease of application, (3) compare survival and growth of native tree seedlings planted among L. maackii eradication treatments, (4) evaluate the effects of tree protectors on native seedling survival, (5) and explain the influence of microenvironmental factors on tree seedling growth and survival.
Methods
This experiment was carried out at the Fernald Environmental Management Project
(FEMP) Site (39°18'20" N, 84°41'50" W), a 425 ha facility located ca. 29 km northwest
14 of Cincinnati, Ohio. The old production facility at FEMP was used for the manufacture of high-grade uranium and thorium to support the U.S. weapons defense program until 1989 at which time remediation and restoration became the primary goal at the FEMP site
(U.S. Dept. of Energy; 2002). This research project was conducted in the "North
Woodlot" within the FEMP site, which is approximately 65 ha and is located north of the old production facility. The north woodlot area contains four general types of habitats including old-fields, previously mowed meadows, regenerating forest, and mature forest
(McCarthy; 1999a).
This area has been subject to considerable anthropogenic-related disturbance (e.g., mowing, roads, grazing), which may have facilitated the influx of L. maackii. A floristic study conducted on the site by McCarthy (1999b) reported that out of 332 taxa, 30.5% were non-indigenous with the most invasive species being Rosa multiflora (multiflora rose), Alliaria petiolata (garlic mustard), Festuca elatior (tall fescue), and Polygonum persicaria (lady’s thumb smartweed); however, he reported that Lonicera maackii was the most problematic non-native plant in the forested areas.
This area lies within Butler County and has a climate that is typical of most of southern Ohio with cold winters and hot summers. The mean temperature for December to February is 0 ºC, and the mean temperature from June to August is 22 ºC (National
Climate Data Center and National Oceanic and Atmospheric Administration; 2002). The total annual rain precipitation is 53.3 cm, and the total snowfall is 38.1 cm. Sixty percent of the total precipitation usually falls from April through September, although precipitation is present in every month. The growing season is 172 days (Lerch et al.;
1980).
15 Experimental design
This experiment employed a completely randomized block design (Sokal &
Rohlf; 1995) with eight 5.5 × 13.5 m replicate blocks. Blocks were located in two areas
roughly 150 m apart containing L. maackii stands (sites A and B). Site A has soil that is
primarily Xenia silt clay loam (XeB) and lies on a well drained till plain. Site B lies on
Ragsdale silt clay loam (Ra) and has poorly drained soils with a flat topography (Lerch et al.; 1980). Site A contained a total of five replicate blocks and was dominated by Carya laciniosa (shellbark hickory) in the overstory. Site B contained three replicate blocks and was dominated by a mixture of Acer negundo (boxelder), Cornus florida (flowering dogwood), Fraxinus pennsylvanica (green ash), Prunus serotina (black cherry), and
Ulmus americana (American elm) in the overstory. Three rectangular 4.5 × 5.5 m treatment subplots were established within each block. Treatments were randomly assigned to subplots and were designed to test their efficacy of killing L. maackii and ultimately their influence on the survival of native tree seedlings. Each block consisted of (1) a control subplot in which no L. maackii was removed; (2) a cut subplot in which all L. maackii stems were cut near ground level, removed from plot, and stumps painted with 50% glyphosate isopropylamine salt solution (Roundup; Monsanto Company, St.
Louis, Missouri); and (3) an injection subplot in which L. maackii was killed using an E-
Z-Ject® lance (Odum Processing Engineering Consulting, Inc.; Waynesboro, Missouri)
but left standing. This lance works by pushing a glyphosate-filled 22-caliber capsule
(Bergerud; 1988) manually through the bark of the stem or swollen stem base and into
the vascular system of the selected woody plant. Only stems 1.5 cm and larger were able
to be injected without operator error. On large honeysuckle individuals with two or more
16 stems, each stem was injected separately; otherwise, plants were injected individually.
Prior to eradiation treatments, all honeysuckle stems in subplots were tagged and measured.
Within each treatment subplot, ten individuals each of six species of one-year-old indigenous tree seedlings were randomly planted 0.75 m apart using a dibble bar. Tree species were chinkapin oak (Quercus muehlenbergii), black walnut (Juglans nigra), black cherry (Prunus serotina), green ash (Fraxinus pennsylvanica), flowering dogwood
(Cornus florida), and redbud (Cercis canadensis). Seedlings were chosen as model midstory and overstory species to study the effects of honeysuckle control methods on seedling performance. Furthermore, these species all occur within the county where this experiment took place (Braun; 1989) and were found as canopy or sub-canopy species in the study area (McCarthy; 1999b). It was observed that recruitment of tree species was especially poor below L. maackii stands (also found by Hutchinson & Vankat; 1997;
Collier et al.; 2002), which justified replanting after eradication of honeysuckle. Half of the 1440 tree seedlings were enclosed in 122 cm Protex Pro/Gro Solid Tube Tree
Protectors (Forestry Suppliers, Inc; Jackson, Missouri) to exclude white-tailed deer
(Odocoileus virginianus) and test the effects of deer browse on tubed versus non-tubed seedlings. Blocks were surrounded by two strands of barbed wire (50 & 100 cm ht.) with a 1 m buffer to exclude cattle (present for the first year of the study) but not deer.
Honeysuckle eradication treatments were applied 24 March 1999. Seedlings were planted on 24 March 1999 to 30 March 1999, and deer tree protectors were applied and staked on the north side from 31 March 1999 to 11 April 1999. The experiment ran from
24 March 1999 to 10 Oct 2001.
17 Sampling
Seedlings were measured for height (cm) and basal diameter (mm) at initial
planting. A preliminary analysis of variance (ANOVA) indicated that seedling size was
not significantly different (P > 0.10) among blocks or treatment subplots at initial time of
planting; therefore, an analysis of covariance was not needed. Seedlings were also
measured for height and diameter and mortality recorded (27 May 1999, 10 October
1999, 21 May 2000, 23 September 2000, 25 May 2001, and 7 October 2001).
Environmental data were collected over the growing seasons of 1999 and 2000 on
a treatment subplot level. Soil moisture was measured twice in 1999 (25 June, 21
August) and 2000 (29 July, 29 August). Soil moisture was analyzed gravimetrically
(McCarthy; 1997a). Soil pH was measured using a glass electrode method with a Corning
350 pH / ion meter in a 2:1 water-to-soil solution (25 June 1999, 21 August 1999, 29 July
2000, 29 August 2000). Soil nitrate was measured using absorbent Rexyn 300 (H - OH)
beads (Fisher Scientific; Fair Lawn, New Jersey) buried in the soils’ A-horizon in nylon
mesh bags for roughly three months (20 May 1999 to 21 July 1999 and 21 May 2000 to
22 July 2000). Nitrate was removed from the Rexyn beads with 2M KCl solution and analyzed with a cadmium reduction method using NitraVer 5 Nitrate Reagent (Hach;
Loveland, Colorado). Three soil moisture, nitrate, and pH samples were taken at random
locations in each subplot each time sampling occurred. Light availability was measured
using 35 mm images taken with an 8 mm hemispherical fish-eye lens at a height of 0.5 m
on 30-Jul-99 and 27-Jul-00. These images were digitized and then analyzed using GLIC
software (Canham; 1988). A full description of the protocol can be found in Robison &
McCarthy (1999). One image was taken per subplot (24 total photographs). Air
18 temperature and humidity were measured for eight random locations inside and outside of
deer tree protectors in each subplot on 12 July 1999 and 13 July 2000 with a Corning
thermohygrometer.
Statistical analysis
To assess abundance of L. maackii within blocks and among treatment plots, a
regression model was constructed to estimate honeysuckle biomass (from stem density
and diameter) within blocks and treatment subplots using randomly selected on-site
honeysuckle plants (N = 32). Honeysuckle plants were oven-dried at 105 ºC for 72 h.
An ANOVA was used to compare final native seedling survival among treatments
after three years. Percent survival was used as the independent variable, and the factor
variables were site (random factor), block within site (nested), honeysuckle eradication
treatment (fixed), species (fixed), and tree protector (fixed). Normality and equal
variance assumptions were satisfied using the D’Agostino omnibus test (D'Agostino et
al.; 1990) and the F-Max test (Dowdy & Wearden; 1991), respectively. When necessary,
data transformations (log10 or square root) were conducted in order to meet these assumptions. Untransformed means and standard errors are reported throughout the results.
The shapes of survival curves were analyzed to determine if the native seedlings died at different rates during the three years of sampling. Site, treatment, and species survival distributions were compared using the log rank nonparametric test. This test was utilized because greater than 20% of the seedlings were still alive at the end of the experiment (i.e., censored observations Pyke & Thompson; 1986).
19 Loglinear modeling was used to analyze the causes of mortality for native
seedlings. This method uses an n-dimensional contingency table and a stepdown model
selection process to determine the most important factors related to whether seedlings
died by drought, fungus, handling and transplanting, or deer browsing. Computed Chi-
square values were compared to critical values to determine statistical significance.
MANOVAs were used to examine the microenvironmental patterns as they
related to years (1999, 2000), eradication treatments (control, cut, and inject), and sites
(A and B), which were all used as predictor variables. All factors were treated as fixed,
except site. Spring soil moisture and pH, nitrate, percent open canopy, ambient air
temperature, and relative humidity were analyzed as response variables. Autumn soil
moisture and pH were dropped from the analysis due to multicollinearity, but this did not
affect the MANOVA results. The assumptions of multivariate normality and equal
variance (Scheiner; 1993b) were satisfied prior to analysis via data transformations
(log10). Wilks’ Lambda values were calculated for the overall MANOVA table, and
individual ANOVAs were conducted to determine which factors were important in
producing the overall MANOVA results.
A repeated measures analysis of variance (RMANOVA) was used to analyze
seedling height growth. The data passed the assumptions of residuals following the
normal probability distribution and equal within-subject covariances (Von Ende; 1993).
There was a violation of the sphericity pattern of the covariance matrices (Mauchley’s
criterion = 144.16, P = 0.035); therefore, a Huynh-Feldt Epsilon correction (ε = 0.95) was
used to create a more stringent critical F-value (Crowder & Hand; 1990). Seedling diameter was also analyzed with a RMANOVA. Like the height data, diameters suffered
20 from lack of sphericity of the covariance matrices (Mauchley’s criterion = 620.07, P <
0.001). A Huynh-Feldt correction was used to create a more conservative F-test (ε =
0.88). Site was a random factor, and individual seedling was nested within all other factors such as eradication treatment, species, and tubing, which were fixed. To aid in interpretation of interactions, Cicchetti contrasts were computed (Cicchetti; 1972).
Cicchetti contrasts enable the analysis of every unconfounded comparison by holding all factors constant except one.
All statistical computations of ANOVAs, loglinear modeling, RMANOVAs, and
Bonferroni post hoc tests were conducted using NCSS Version 5.0 (Hintze; 2000).
MANOVA calculations were conducted using SAS Version 8.0 (SAS Institute; 2001).
Bonferroni multiple comparison tests were conducted when significant F-tests were found for ANOVAs. Unless otherwise stated, statistical tests were significant when P <
0.05.
Results
Honeysuckle parameters
The regression model showed that honeysuckle basal area at 5 cm height above ground explained the greatest amount of variance; therefore, this was the parameter that was used to estimate honeysuckle biomass [honeysuckle biomass per individual = basal area (in cm2)* 0.907 + 0.147, R2 = 0.91]. This equation estimates biomass very similarly
to that reported by Luken (1988). Mean biomass of Amur honeysuckle was 361 ± 69 kg ·
ha-1. Mean stem density was 65959 ± 7637 stems · ha-1. Mean density of L. maackii was
21380 ± 3171 plants · ha-1. The mean number of stems per plant was 3.65 ± 0.26. A
21 preliminary analysis of variance was conducted on honeysuckle biomass to evaluate pre-
treatment differences among blocks and subplots. No significant (P > 0.10) differences
were found.
Eradication treatment effectiveness on honeysuckle
Honeysuckle mortality was assessed at the end of the 1999 and 2000 growing
seasons. At the end of 1999, aboveground mortality was 99% for both eradication
treatments. It was difficult to inject stems smaller than 1.5 cm; therefore, delayed
resprouting did occur due to operator error during the initial injection treatment. For
2000, plants had 98% mortality where no operator error occurred in the injection plots
(95% if operator error was included) and 94% in the cut and paint treatment plots.
ANOVA results showed that there were no significant differences (P > 0.10) in honeysuckle mortality among treatment plots or between years.
Ease of application of eradication treatments, planting, and tubing
The cut and paint method required more time to apply (7 hours to eradicate 66 m2
or 1060 person-hours · ha-1) than the injection treatment (3 hours to eradicate 66 m2 or
454 person-hours ⋅ ha-1). In the injection treatment one person operated the E-Z-Ject® lance and another person cleared leaf litter away from honeysuckle stump bases when necessary. Planting 1440 seedlings required 4 days work by 2 people (or 64 person- hours), and assembly and application of tree protectors on half of the seedlings (720 plants) required 8 days of work by 3 people (or 192 person-hours).
Comparison of costs of restoration
The startup costs for the cut and paint method totaled $253 USD, which included clippers ($30), loppers ($65), and glyphosate herbicide (2.5 gallons at $158). The
22 injection startup costs were $599, which included the E-Z-Ject® lance ($467) and glyphosate-filled capsules (1200 capsules at $132). Protex Pro/Gro Solid Tube Tree
Protectors cost $1.74 each. Seedlings cost roughly $0.33 each for 1440 seedlings totaling
$480. The time required for implementation of treatments was reported, but labor costs of eradication treatments, tree seedling planting, and tree protector assembly and installation were not calculated due to the fact that our reports of eradication treatment times are likely overestimated as the application times for persons doing frequent restoration treatments would likely be much less. Total cost including purchase of startup equipment, native seedlings, and tree protectors (but excluding labor) was $831 for the cut treatment and $1177 for the injection treatment.
Comparison of survival for native seedlings
The survival of planted seedlings was 51% ± 5 for 1999, 44% ± 5 for 2000, and
40% ± 5 for 2001. An analysis of variance of final survival of seedlings (after three years) found that two factors, block and tube, were non-significant (P > 0.05) and were not included in subsequent analyses. Site, treatment, species, and site × species were all significant (Table 1.1). Overall survival at site B (56% ± 2) was significantly greater (P
< 0.001) than at site A (30% ± 2). The site × species interaction was significant (P <
0.001) indicating that species survived differently in the two sites (Table 1.1, Figure 1.1).
Survival between control and honeysuckle treatments was also significantly different (P
= 0.002; control survival = 32% ± 3, cut survival = 51% ± 3, injection survival = 45% ±
3). Survival between cut and paint and injection eradication treatments was significantly different (Figure 1.2). No other two or three-way interactions were found to be significant (Table 1.1).
23 Because of significantly different final survival percentages between sites, among years, and among treatments, survivorship was modeled to determine if seedlings died at different rates within these groups. Weibull distribution curves (Dodson; 1994) produced the best fit for species survivorship. Seedlings within control treatments died at different rates than the cut (X2 = 40.67, df = 1, P < 0.001) and injection treatments (X2 = 23.68, df
= 1, P < 0.001); however, the cut was not different than the injection treatment (X 2 =
2.38, df = 1, P = 0.123; Figure 1.3). Because there was a site × species interaction for survival at the end of two years, species survival rates were analyzed separately within sites. Several species (Fraxinus pennsylvanica, Prunus serotina, and Juglans nigra) had fairly constant mortality rates during the experiment at both sites. Other species
(Quercus muehlenbergii at site A and Cercis canadensis and Cornus florida at site B) had high rates of early death followed by constant mortality. Cercis canadensis and Cornus florida at site A had fairly constant but high mortality for only the first two years of the experiment with stabilized survival during the third year (Figure 1.4).
Causes of mortality for native seedlings
The causes of mortality in different treatments were assessed to determine if seedlings died by different means among sites, honeysuckle treatments, and species.
Overall the most common cause of mortality was drought (39.4% of all seedlings) followed by handling and transplanting (6.3%), browsing (3.1%). Powdery mildew
(fungus) was detected on the leaves in early spring and accounted for a small proportion of mortality (1.2%).
Logistic regression results indicated that the factors associated with mortality due to drought were site, treatment, and species (X2 = 34.5, df = 10, P < 0.001). Drought
24 was a greater factor of mortality at site A (29.7%) than at site B (9.7%), and drought was
a greater factor of mortality in control plots (16.5%) than in injection (11.8%) or cut plots
(11.1%). Fraxinus pennsylvanica had less mortality due to drought (2.4%) than the other
five species (6.1 – 8.2%). Factors associated with mortality due to browsing were site
and species (X 2 = 7.6, df = 2, P = 0.022). Overall, mortality due to browsing was 1.9%
with site A having a greater mortality due to browsing (2.8%) than site B (0.3%).
Species’ mortalities due to browsing were as follows: C. florida (1.5%), C. canadensis
(1.1%), and Q. muhlenbergii and P. serotina (0.2%). Fraxinus pennsylvanica and J.
nigra had no incidences of browsing. Mortality due to fungus was low but best predicted
by the factors of site and species (X 2 = 5.4, df = 2, P = 0.067). Site A had a slightly
greater incidence of fungus (0.8%) than site B (0.3%). The incidences of fungus
mortalites per species were as follows: C. florida (0.5%), C. canadensis and Q.
muhlenbergii (0.14%), F. pennsylvanica and J. nigra (0.07%). Mortality due to handling
was only significant for species (X 2 = 441.31, df = 5, P < 0.001). The handling mortality
incidences were mostly associated with C. florida (4.0%) followed by C. canadensis
(2.1%) and J. nigra (0.1%; Figure 1.4).
Seedling growth
Seedling height growth was analyzed for each year, 1999, 2000, and 2001.
Growth was not significantly different between sites (P = 0.40); therefore, site was
subsequently removed from the analysis. Seedlings grew equally well in tubed and non-
tubed conditions (P = 0.79) and among eradication treatments (P = 0.99). There were significant year, species, and species × year effects (all P < 0.001; Figure 1.5).
25 Analysis of seedling diameters used the same factors as those in the height
RMANOVA. Site was nonsignificant (P = 0.83) and was dropped from the analysis.
Diameter growth was not significantly different between tubed and non-tubed seedlings or among treatments. There was a significant year (P < 0.001) and species × year interaction (P = 0.01). There were no significant differences among species’ diameters for 1999 and 2000; however, there were significant species differences for 2001 (Figure
1.6).
Analysis of environmental parameters
Only two factors, site and year, were significant (P < 0.001) in predicting microenvironmental response. There were no environmental differences among eradication treatments. Also, no two- or three-way interactions were significant. Follow- up ANOVAs indicated that spring moisture, pH, nitrate, and temperature were significantly different (P < 0.001) between years. Site A had significantly lower values for environmental measurements (spring moisture, spring pH, and percent open canopy) than site B (ANOVA results). For all of these environmental parameters, 1999 had lower values than 2000. Spring moisture and pH, and percent open canopy had significant
ANOVAs for site; site A had lower values than site B for these environmental measurements (P < 0.05).
The Palmer Drought Severity Index (PDSI) values were substantially different for the three years of the study. For 1999, the PDSI values ranged from a mild drought
(May, June) to moderate and severe drought (Aug to October) to extreme drought
(November, December). The 2000 PDSI values ranged from normal to slightly wet for the entire year, and the 2001 PDSI values ranged from a mild drought (March, April) to
26 very wet in October (October; National Climate Data Center and National Oceanic and
Atmospheric Administration; 2002).
Discussion
The successful restoration of a forest following the eradication of invasive plants includes the restoration of the overall diversity of the site (Sinclair et al.; 1995) as well as
restoring the composition to a close approximation of the original habitat (Harrington;
1999). Also important is the restoration of the community structure (Holmes; 2001) and
ecosystem level processes (Vitousek; 1990). Not all of these attributes are completely
restorable within a single, short-term project, but restorations should include these goals,
which if accomplished, will set the stage for appropriate natural successional trajectories.
The main goal of our experiment was to accelerate succession (see MacDonald;
1993). In our case, after eradicating L. maackii, we desired to increase the rate of
succession by overcoming the problem of limited dispersal of propagules and relatively
unsuccessful recruitment, which is a frequently encountered problem in restoration
efforts (Robinson & Handel; 2000). Planting native species accomplished the restoration
of the forest tree composition, which is similar to a nearby, relatively undisturbed, mature
reference forest (McCarthy; 1999b). Restoring the composition will likely re-establish
the canopy and midstory structure as well as recruitment processes in the future, and this
will likely help facilitate the return of higher level processes as Cairns (1986) states that
most of the functional attributes of a restored system are correlated with the replacement
of its vegetative structure and composition.
27 We also wanted to explain some of the results of the experiment to aid in future restoration efforts (for example the survival of the native seedlings). Seedling establishment is most importantly influenced by the existing environmental conditions during the early stages of life (Walters & Reich; 2000). The differential survival of native seedlings in our experiment was largely due to individual species’ responses to sites’ microenvironmental conditions, which varied according to location, L. maackii eradication treatment, year, and other perturbations such as fungal infection, browsing, and handling mortality.
Actually species-specific responses to site conditions are very common
(Veenendaal et al.; 1996; Sipe et al.; 2001) as are year-to-year differences in survival
(Van Der Meer et al.; 1999). These differences in seedling survival likely will result in a forest composition different than the one originally intended during initial planting; therefore, follow-up procedures such as replanting may be necessary in restorations where survival of native seedlings is relatively unpredictable and a particular forest composition is desired. Our results clearly indicate the need for an awareness of specific site conditions along with the planting of a diversity of tree species suited to that site.
Another factor influencing the survival of seedlings was the presence or absence
(through eradication) of L. maackii. It is not surprising that reducing L. maackii abundance was associated with increased survival of native seedlings. Other restoration projects have also found greater survival following clearing of invasives using herbicide treatments (Sweeney et al.; 2002). Survival of native seedlings was clearly greater when
L. maackii was killed by either eradication method rather than being left intact.
28 The mechanism for greater native seedling survival in eradication plots is not
evident because the microenvironmental conditions that we measured were not
statistically different between the eradication and control treatments. Significantly
greater temperature, light, soil moisture, and pH levels have been found where L. maackii
was killed versus left intact (C. Keiffer 2002, personal communication). Greater light
intensity, although not statistically significant, may account for the increased survival that
we found in eradication plots. Work by Gorchov & Trisel (2003) may offer an
explanation for lower seedling survival in L. maackii control versus removal plots. They
found that native seedlings had increased mortality when grown with L. maackii due
mostly to shoot, but also root, competition. They suspected aboveground competition
would mostly be struggle for light. Trisel (1997) also found that L. maackii plants may be allelopathic since watering with L. maackii leaf extract had effects similar to those of a
10-4 solution of juglone, a known allelopathic chemical. Generally, seedlings in L.
maackii control subplots may have been chemically inhibited or had fewer resources than
in areas where L. maackii was eradicated. Clearly more work needs to be conducted to
elucidate the exact nature of L. maackii’s interference mechanism.
In addition to the two eradication treatments that we used, others have employed
several other methods to control L. maackii with varying levels of success. A 1% foliar
glyphosate spray has been used to control seedlings as well as control adult L. maackii
along heavily invaded edge habitats. The advantage of foliar spraying is that it is
relatively easy to apply (Conover & Geiger; 1993); however, spraying can result in up to
100% mortality to native vegetation in the underlying herb layer (Trisel; 1997). Others
29 have argued similarly against the use of herbicides in restoration areas because of
negative effects on non-target plants (Brockway et al.; 1998).
Cutting alone has been found to be a less than adequate control method for L. maackii because resprouting occurs from a meristematic burl located at the base of stems
(Luken; 1988). Although resprouting following clipping occurs less frequently in forested versus open habitats (30% v. 70%, respectively), clipping alone has been found to actually increase stem numbers (Luken; 1990b). We do not recommend clipping alone unless honeysuckle is growing in a closed-canopy and a clipping regimen will be continued to control resprouts.
Cutting and applying herbicide (i.e., cutting and painting) were found to be effective in our experiment and is one of the most widely used eradication procedures for woody invasive plants (Reinartz; 1997; Olson & Whitson; 2002). A 20% glyphosate solution has been found to be effective in controlling L. maackii in forest interiors, and a
50% solution is more appropriate in open habitats where the plant seems more resistant
(T. Borgman, Hamilton County Park District, 2002, personal communication). Our experiment successfully controlled Amur honeysuckle using a 50% solution for the cut and paint treatment in a young closed-canopy forest.
There are several disadvantages to cutting. Cutting alone and the cutting and herbicide method leave behind stump bases, and piles from cutting take longer than individual stems to decay. Furthermore, herbaceous vegetation does not develop beneath piles. Piles can be chipped or removed, but they may have the advantage of creating wildlife habitat for animals (J. Klein, Hamilton County Park District, 2002, personal
30 communication). The biggest disadvantage of eradication involving L. maackii cutting is that it is very labor intensive.
A number of other L. maackii eradication methods have been reported, but each has drawbacks. Hand pulling can be effective in areas with moist ground, but the plants will likely resprout if root portions remain (Gayek; 2000). Generally hand pulling is not difficult if individual plants are less than three years old or growing on moist soils, but the spread of L. maackii from remaining roots may not make this method effective
(Gayek; 2000). Other methods for controlling L. maackii include using a “weed wrench” to remove whole crowns (100% mortality) or a pulaski axe (98% mortality); however, these methods are very labor intensive (Trisel; 1997).
We found that the injection system may be the best overall method for the eradication of L. maackii. There are several advantages to its use. Injecting produced very little operator fatigue and limited exposure of the operator to herbicide. Also less overall herbicide is used relative to other methods, and the herbicide that is used is restricted entirely to target plants. We also found that injecting was 43% faster than cutting and applying herbicide. This may be of considerable importance in larger restoration efforts. Herbicide injections have been used by others to kill woody plants and prevent regrowth (Johansson; 1985). Franz & Keiffer (2000) successfully used the injection system to eradicate L. maackii in a stand in southwest Ohio. They found that fall injections are more effective than spring, and that it is more effective to inject all stems, rather than a single stem, of a L. maackii plant. Because of the difficulty in injecting small L. maackii stems, we recommend that injection is used only on stems 1.5
31 cm and larger and that the cut and paint method should be used to control smaller
individuals.
Other considerations in restorations of this type include the use of tree protectors,
particularly where large mammal browsing is a problem. Sweeney et al. (2002) found tree seedlings survived best when a combination of herbicide and tubing treatments were applied; however, we did not find that tree protectors increased survival or growth of native seedlings. We found that despite measurement of environmental differences in tubed and non-tubed conditions, there were no significant survival or growth differences for native seedlings. Mortality due to browsing was only 3.1% at this site; however, it is widely known that deer is a keystone herbivore and can have profound impacts on forest composition (Rooney; 2001). Barbed wire fencing around blocks may have inhibited deer browsing, making browsing incidences artificially low. Perhaps if deer populations are a problem locally, then tree protectors would be justified.
It is best to target control of L. maackii populations when they are small. Deering
& Vankat (1999) recommend that early control is best because it is done before plants reach reproductive maturity, which is roughly 4 or 5 years. This approach does have merit as the early control of invasives is often less costly and has a greater chance of success than control measures taken later (Chippendale; 1991). Furthermore, reinvasion is likely unless all plants are eradicated from an area. Restoration activities may make site conditions more favorable for invasive re-establishment. In artificial gaps where
Amur honeysuckle was removed, reinvasion by Amur honeysuckle and garlic mustard was demonstrated to be more likely than in intact stands (Luken; 1997a). When total eradication is accomplished, L. maackii must repopulate from another site, and reinvasion
32 is likely to occur in small, manageable amounts (Luken & Goessling; 1995; Deering &
Vankat; 1999).
Although restoration costs are rarely reported in academic articles, the economics
of a restoration project are very important (Holl & Howarth; 2000). While seemingly
initially expensive, the cost of eradicating and restoring native communities is often
recovered in a short time (Zavaleta; 2000). Various restoration studies have shown that
the cost of the removal of L. maackii can be quite variable. Gayek (2000) reported the cost of a L. maackii restoration to be $8200 for a single two hectare project which included labor for a crew of 30 as well as safety equipment, 28.5 liters of herbicide, and the rental of a brush chipper. Gayek (2000) reported $2000 for the cost of another removal project on four hectares of land, which was less expensive because volunteers were used and no brush chipper was employed. Trisel (1997) indicated lower costs of L. maackii eradication. He gave the startup costs for three methods of L. maackii control including crown removal using a pulaski axe and hand saw ($42), foliar spray using herbicide and backpack sprayer ($165), and the stem cut and paint method using clippers, loppers, and herbicide ($172). However, he did not include the cost of labor in his estimates or account for per hectare area costs. Additionally, he states that these methods were either labor intensive or damaging to native plants.
For our project, cutting and painting was less expensive ($253) in terms of startup costs than the injection method ($599); however, the most expensive part of restoration most likely would be labor for the implementation of eradication treatments and planting of native seedlings. The amount of time required for our experiment to inject an area was
33 roughly 43% less than the cut and paint treatment. Clearly labor is a major cost to restoration efforts of this type and should be considered.
We found that limited recruitment below L. maackii stands necessitated eradicating L. maackii and replacing it with native tree seedlings. This was successfully accomplished, as was a thorough comparison of L. maackii eradication methods and native seedling performance. The end result was a successful restoration with an increase in native woody plant diversity, structure, and most likely associated larger-scale processes in the future.
The best situation would be to predict successful invaders prior to introduction and prevent their establishment; however, with few restrictions on introductions, invaders frequently do enter communities and become problematic (Reichard & Hamilton; 1997).
It then becomes the task of land managers and ecologists to gather information on patterns of invasion, develop the most effective means of controlling invasives, and at the same time, protect native diversity (Byers et al.; 2002). We believe that by combining the findings of our research with prior studies, this knowledge base can be applied to bring about the successful restoration of invaded communities and improvement of ecosystem health.
34 Table 1.1. Analysis of variance of final survival for native tree seedlings.
Source df SS MS F-ratio P-Value Site 1 4.438 4.438 84.07 < 0.001 Treatment 2 1.741 0.704 331.04 0.003 Control vs Treatments * 1 25.27 0.002 Cut vs Inject * 1 8.23 0.014 Site x Treatment 2 0.005 0.003 0.05 0.951 Species 5 8.339 0.668 6.65 0.029 Site x Species 5 1.253 0.251 4.75 < 0.001 Treatment x Species 10 0.403 0.040 0.43 0.902 Site x Treatment x Species 10 0.941 0.094 1.78 0.064 S 52 13.304 0.053 Total (Adjusted) 28 31.917 Total 288 *orthogonal contrasts
35
Figure 1.1. Percent of final survival of planted seedlings comparing sites across treatments (panels A - C). Species abbreviations are as follows: Cercis canadensis (CECA), Cornus florida (COFL), Fraxinus pennsylvanica (FRPE), Juglans nigra (JUNI), Quercus muhlenbergii (QUMU), Prunus serotina (PRSE). Note: all species had greater survival at site B relative to site A, except F. pennsylvanica, which had greater survival in the two eradication treatments of Site A (panels B and C).
36
Figure 1.2. Final seedling survival (after three years) in eradication treatments. Lowercase letters indicate significant differences (P < 0.05).
37
Figure 1.3. Weibull distribution function estimates for native seedling survival rates in three eradication treatments. Survival rates in the two eradication treatments were greater than in the control treatment plots where Amur honeysuckle was left intact. Lowercase letters indicate significant differences (P < 0.05) among treatments.
38
Figure 1.4. Weibull distribution function estimates for native species survival rates in two sites. Overall survival rates at site B were greater than at site A (P < 0.001). Lower case letters indicate significant (P < 0.05) differences among species within a site. See Figure 1.1 legend for species abbreviations.
39
Figure 1.5. Seedling height for 1999, 2000, and 2001. Lower case letters denote significant (P < 0.05) differences among species within a single year. No significant differences (P < 0.05) existed among species for 2000. See Figure 1.1 legend for species abbreviations.
Figure 1.6. Seedling diameter for 1999, 2000, and 2001. Lower case letters denote significant differences among species within a single year. No significant differences (P < 0.05) existed among species for 1999 and 2000. See Figure 1.1 legend for species abbreviations.
40
Chapter 2: A dendroecological study of forest overstory productivity following the
invasion of a non-indigenous shrub, Amur honeysuckle (Lonicera maackii)
Introduction
Due to human activity, the earth is experiencing large environmental changes at an unprecedented rate. The human population is roughly 250% greater than it was in
1950 (Cohen; 2003), and an estimated 17-fold increase in global commerce occurred between 1965 and 1990 (World Resources Institute; 1994). One result of human activity is that previously isolated, invasive species have been transplanted across barriers and established in new environments (Mack; 2001). The ecological and economic tolls of this global transfer are considerable. Invasive species are ranked as the second-greatest cause of losses in biodiversity (Enserink; 1999) and total U.S. monetary losses are estimated to exceed $120 billion · yr-1 USD (Pimentel et al.; 2005).
Important advances have been made in the field of invasive species ecology; however, progress is sorely needed in the development of sound decision-making strategies (Richardson; 2004). Invasive populations and associated impacts are increasing exponentially, and conservation resources are being stretched beyond limits.
A substantial portion of conservation budgets is spent on control and eradication
(D'Antonio & Meyerson; 2002), but the majority of invasive species may not cause significant negative impacts (Williamson; 1996). Therefore, it becomes increasingly important as the first step in land management strategies to demonstrate clearly that an invasive plant is indeed having negative impacts (McCarthy; 1997b).
41 Invasive species impacts occur at a variety of scales including alterations to
genetics, individuals, populations, community composition, and impacts to rare, endemic
or sensitive species (Byers et al.; 2002). A problem with impacts reported at these levels
is that they may include random spatial, temporal, or species-specific ecological variation
(Parker et al.; 1999). Relative to studies at other biological levels, investigations at ecosystem levels may at times be a more appropriate way to study inferred impacts because ecosystem level studies allow changes to be measured for a potentially greater number of species over larger spatio-temporal scales (Edwards; 1988).
Ecosystem level impacts have been characterized into three categories including invasives (1) differing in resource acquisition and use than natives, (2) altering the trophic structure of a system, and (3) causing an altered disturbance regime (Parker et al.;
1999). Well known examples of ecosystem level changes include alterations in the availability of nitrogen by Myrica faya which alters ecosystem development (Vitousek et al.; 1987), modifications by introduced animals to trophic interactions in Hawai’i (Stone;
1985), and changes by introduced grasses in the fire disturbance cycle (D'Antonio &
Vitousek; 1992). A fourth type of ecosystem alteration includes changes in productivity
(Ehrenfeld; 2003). There are numerous studies of changes in productivity following exotic invasion, but most involve herbaceous plants (Robles and Chapin 1995). Studies regarding alterations in woody plant productivity are encountered much less frequently
(Marco & Paez; 2000).
We are not aware of published studies that investigate changes in overstory tree productivity following invasion by a non-indigenous understory plant. This is an interesting ecological question because quite often competition is assumed to be one
42 sided with larger plants negatively influencing smaller plants, not vice versa (Weiner;
1990). Numerous studies of smaller plants negatively affecting larger ones have been reported including shrubs (Peterson et al.; 1988), smaller trees (Yoshida & Kamitani;
1998), herbaceous perennials (Dunbar & Facelli; 1999), and grasses (Elliott & White;
1987). Many examples exist in the silvicultural literature of strategies to reduce native inter-plant competition including thinning practices (Smith et al.; 1997), which have been shown to increase the availability of water and soil nutrients with associated increases in the productivity of remaining trees (Cole & Newton; 1986). Moreover, the forestry practice of thinning from below (i.e., low thinning or German thinning), which involves the removal of lower strata, is known to be associated with increases in radial growth in canopy trees (Nyland; 2002). Invading plants in a lower forest stratum could very likely impact the productivity of overstory forest trees. We, therefore, wanted to investigate similar potential patterns of competition between the overstory trees and Amur honeysuckle (Lonicera maackii), an aggressively-invading understory shrub.
Lonicera maackii has already been shown to have detrimental impacts in eastern deciduous hardwood forests at a variety of biological levels including impacts on individual herb growth (Miller & Gorchov; 2004), seedling growth and fecundity
(Gorchov & Trisel; 2003; Hartman & McCarthy; 2004), seed bank and bud bank levels
(Collier et al.; 2002), and community composition (Hutchinson & Vankat; 1997).
Furthermore, these impacts may be widespread as L. maackii can effectively expand in range via avian dispersal (Ingold & Craycraft; 1983) and has been reported in 26 states in the eastern U.S. and Ontario (Hutchinson & Vankat; 1997), Canada (Pringle; 1973). We wanted to test the ability of L. maackii to impact tree productivity via tree-ring analysis,
43 considering its potential for negative above- and belowground competitive effects
(Gorchov & Trisel; 2003) and successful establishment of dense populations of rapidly growing individuals (Deering & Vankat; 1999).
Based on the available invasive species ecology and forestry literature, we hypothesized that L. maackii would likely decrease overstory productivity and that this would be evidenced as a decline in tree-ring size. Specifically, our goals were to (1) quantify and document levels of possible L. maackii impact on tree growth, (2) determine the timing of significant growth changes, (3) assess the sensitivity of canopy tree individuals according to factors such as site, species, shade-tolerance, size, and age differences, and (4) determine the efficacy of using dendrochronological methods in assessing the impact of invasive species on tree growth.
Methods
A total of sixteen sites were sampled in the vicinity of Cincinnati and Dayton,
Ohio, U.S.A. Sites were equally divided between cities and were randomly chosen from locations on publicly-owned land. Twelve of the sixteen sites were invaded by L. maackii, and four of the sites had not been invaded (Figures 2.1, Table 2.1).
Within each site, we quantified vegetation using the Point-Centered Quarter
(PCQ) sampling method (Krebs; 1999). Fourteen PCQ points per site were sampled along two parallel 50 m transects that were positioned 50 m apart and at least 50 m from forest edges. The nearest tree to each PCQ point was cored using an increment borer, and the distances to PCQ points, species identities, and diameters at breast height (DBH) were used to compute overstory basal area, density, and species composition. Trees were
44 cored only when they were canopy dominants and if they were free from signs of
physical injury, symptoms of disease, and obvious insect damage. At least twelve cores
were sampled at each site (N = 196 trees total). To test for differences in overstory
canopy composition, similarity values were generated using Sørenson's index, and
clustering was performed via the beta-flexible algorithm (β = -0.2). The Multivariate
Response Permutation Procedure (MRPP) was used to test for compositional differences
between invaded and non-invaded sites (McCune & Mefford; 1999).
The time of L. maackii invasion was assessed by cutting, at 5 cm height, one L. maackii shrub in each PCQ quadrant at 5 cm height and counting the annual rings (N =
48 stems / site). The density of L. maackii was quantified by measuring the distance from
PCQ points to the nearest shrub (≥ 30 cm ht) and converting distance to density (Krebs;
1999). Trees were measured and cored from 1-Sept-2000 to 1-Oct-2000, and L. maackii
was sampled from 1-June-2000 to 31-July-2000. Satellite images
(http://www.terraserver.com) from June-2000 were used to delineate forest stand
boundaries and estimate stand sizes.
Biomass of L. maackii at each site was estimated using allometric methods.
Canopy height and spread, number of stems, largest stem radius, and total stem basal area
were measured on randomly selected forest interior shrubs of various sizes (N = 32).
Tree cores were dried, mounted on wooden blocks, and sanded using standard dendrochronological procedures (Phipps; 1985). Radial increment was measured to the nearest 0.01 mm using an Olympus SZ40 dissecting scope (40x), VELMEX unislide measuring device (VELMEX Inc., Bloomfield, NY), ACU-RITE linear encoder (ACU-
RITE Inc., Jamestown, NY), and Quick-Check digital readout device (Metronics Inc.,
45 Bedfore, NH) connected to a microcomputer. The MEDIR computer program Version
2.1 from the International Tree-Ring Data Bank Program Library (IRTDB) was used
during measuring (Krusic et al.; 1997). A master chronology was created for each site to
represent the common growth pattern for all trees and has the effect of removing
(averaging) small-scale, within-site disturbances as well as competition trends of
individual trees (Cook et al.; 1990).
Crossdating of tree rings was conducted by the use of skeleton plotting and with
the COFECHA program (Grissino-Mayer; 2001). Only cores that could be confidently
crossdated were statistically analyzed. Tree age was calculated by counting annual rings
and using a concentric ring pith estimator where tree center was not entirely reached
during coring (Applequist; 1958).
We analyzed a 20-year period of the tree ring series, 10 years prior to (PRE) and
10 years following (POST) L. maackii invasion. Normally, the process of detrending
removes low frequency growth changes such as those resulting from stand maturation
(Fritts; 1976); however, our using a relatively short 20-year portion of the series
eliminated the need to detrend as this would have removed the hypothesized low-
frequency growth changes following L. maackii invasion (Stokes & Smiley; 1968).
Furthermore, we plotted the tree-ring growth across time and no age-related, reverse-j pattern was apparent; thus, detrending due to age was not necessary (Phipps; 1982). We also plotted precipitation, temperature, and Palmer Drought Severity Index (PDSI) from
1949 to 2000 to visualize any notable climate changes occurred during that period
(National Climate Data Center and National Oceanic and Atmospheric Administration;
2002).
46 Both annual radial increment (RI) and basal area increment (BAI) were used as
growth indicators for each tree ring series (Visser; 1995). BAI was used in addition to RI
because BAI often allows growth shifts to be detected more easily in that BAI often
shows a positive, linear trend through time (Fritts & Swetnam; 1989). Standardized
measures for RI and BAI were computed by dividing annual growth by each series’ mean
growth through the 20-year period and were used for all statistical analyses.
Standardized data has advantages over raw data by removing differences in individual
tree productivity and rescaling each series so that it has stable variance and a mean equal
to one (Fritts; 1976).
To compare changes in the rate of growth, the RI and BAI slope was estimated
from regressions of tree series data for each of the PRE and POST invasion periods.
Mean RI and BAI growth was also estimated separately for both PRE and POST periods.
Because non-invaded plots had no actual time of L. maackii establishment, we compared analyses using several time periods throughout non-invaded series and found that using different times did not affect the significance of the results. Differences in PRE versus
POST slope for standardized RI and BAI were calculated and will be subsequently referred to as RISLOPE and BAISLOPE, respectively. Likewise, differences in PRE versus
POST mean growth for standardized RI and BAI were computed and will be referred to
as RIMEAN and BAIMEAN, respectively.
A MANOVA was used to determine which factor variables might best explain differences in PRE versus POST mean growth (RIMEAN and BAIMEAN) and PRE versus
POST rates of growth (RISLOPE and BAISLOPE; (Scheiner; 1993b). Factor variables
included (1) cities, (2) sites nested within city, (3) shade-tolerance, (4) tree size, and all
47 multi-way interactions. All MANOVA tests involving cities (Dayton versus Cincinnati) were found to be not significantly different (P ≥ 0.25); therefore, cities was dropped as a factor from subsequent analyses. Site was used to test whether location had a significant effect on PRE versus POST tree growth. Shade-tolerance tested whether early- versus late-successional trees grew differently in PRE versus POST time periods. Trees were classified as being either tolerant to shade or intolerant (Baker; 1949; Burns & Honkala;
1990). The intolerant group included trees with intermediate shade tolerance. Tree size tested whether small trees (10 - 35 cm DBH) versus large trees (> 35 cm DBH) grew differently in PRE versus POST periods. Site was treated as a random effect, and all other predictor factors were fixed effects (Table 2.2).
Testing species differences in mean growth (RIMEAN and BAIMEAN) and rate of growth (RISLOPE and BAISLOPE) in pooled invaded and non-invaded sites was accomplished using a two-way MANOVA. Individual species differences at each site could not be assessed due to inadequate representation of species at each site; therefore, cores from sites were pooled to test species × invasion status (invaded versus non- invaded; Table 2.2). For both two- and three-way MANOVAs, assumptions of equal variances and multivariate normal distributions of residuals were tested and satisfied prior to analyses (Scheiner; 1993b). Tree size was significantly predicted by tree age (P
= 0.01), which violates the assumption that predictors are independent (Tabachnick &
Fidell; 2001); therefore, tree size was used as a surrogate for tree age.
Following both two-way and three-way MANOVAs, subsequent one-way
ANOVAs were conducted to determine significant predictor variables. Bonferroni multiple comparison procedures were used to detect significant differences among
48 groups. Orthogonal contrasts were performed to test between invaded versus non-
invaded sites for differences in rate of growth and mean growth. Paired t-tests were employed to determine significant PRE versus POST slope and mean growth differences following Bonferroni corrections, and percent changes in mean growth and rate of growth were computed for all PRE versus POST-invasion periods. Density and biomass invasive load effects of L. maackii were assessed by regression analysis of the changes in radial and basal area growth (PRE versus most recent 10 yrs) against the density and biomass of
L. maackii at sites.
Intervention detection was used to quantitatively evaluate the significance and timing of mean BAI growth changes for a 50-year time interval spanning 25 years before and 25 years after L. maackii infestation in invaded sites and from 1949 to 2000 in non- invaded sites. Time-series models were constructed using maximum likelihood estimates for each tree series (Box & Jenkins; 1970), and growth changes were detected by identifying outliers as statistically significant changes in growth patterns (i.e., step interventions; Downing and McLaughlin 1990). Methods of Box & Tiao (1975) were used to estimate possible interventions and allow for detection of changes in tree growth in 5-year increments, as well as, tests for changes in levels of tree growth before and after invasion (Box & Tiao 1975, eq. 5.2, variables w02 and w03, respectively). Frequencies of
positive and negative step interventions through time were analyzed using a repeated
measures multivariate analysis of variance (RMANOVA; Von Ende; 1993). Positive and
negative intervention frequencies were the response variables, and invasion status (i.e.,
invaded versus non-invaded) was tested as the between-subject factor. Time was the
repeated (i.e., within-subject) factor. Adjustments for unequal within-subject covariances
49 were made using Huynh-Feldt corrections to create more stringent F-values (Crowder &
Hand; 1990). Follow-up repeated measures analyses of variance (RANOVA) tests were conducted, and Bonferroni post hoc tests were utilized to discern between significant within- and among-subject factors (Von Ende; 1993). A similar RMANOVA test and following RANOVAs were also conducted using magnitude, intensity, and duration of positive and negative interventions through time as response variables (for calculations see Figure 2.3 caption). Invasion status was the between-subject factor, and time was the within-subject factor.
All statistical analyses were performed using the SAS statistical program Version
9.1 via the PROC GLM statement with the exception of the density and biomass load effects and rate of growth estimates (i.e., slope) which were made using the PROC REG statement (SAS Institute; 2001). All tests were performed using standardized values; however, raw means ± 1 SE are reported. Tests were considered significant when P <
0.05.
Results
Lonicera maackii characteristics
Sites experienced a range of L. maackii invasion times and infestation levels.
Sites were initially invaded 12 to 26 yrs before sampling. The mean density of L. maackii was 2951.0 plants ⋅ ha-1, which was only slightly less than the mean tree sapling density of 3165.2 plants ⋅ ha-1. Lonicera maackii basal area was nearly one-third of overstory tree basal area (Table 2.1). Basal area was found to be the best predictor of adult L. maackii biomass [weight per individual (kg) = basal area (in cm2 units) × 0.907 +
50 0.147, R2 = 0.91]. Mean L. maackii plant density was positively predicted by L. maackii
age (R2 = 0.22; P = 0.04) as was L. maackii biomass ⋅ ha-1 (R2 = 0.67; P < 0.001). There was also a significant, positive relationship between L. maackii biomass at each site and honeysuckle density (R2 = 0.33; P = 0.02), but there was no significant difference
between the two cities in terms of L. maackii age, density, or biomass (all P ≥ 0.31).
Forest stand characteristics
Forest stands were generally even-aged. Individual sites were significantly
different from each other in terms of overstory tree age, density, and basal area, but
invaded sites were not significantly different from non-invaded sites (all P ≥ 0.14; Table
2.1). Mean forest stand size was 112.9 ± 20.3 ha. Canopy composition at sites exhibited
no pattern of grouping related to invasion according to beta-flexible clustering and MRPP
analyses (P = 0.83). Tree species at sites were composed mainly of Fraxinus pennsylvanica (34.4%) and a mixture of moderately common species (5 - 10% each species) including Liriodendron tulipifera, Acer saccharum, Acer negundo, Juglans nigra, Quercus alba, Celtis occidentalis, and several uncommon species (< 5%) such as
Carya cordiformis, Prunus serotina, Q. rubra, Q. prinus, Ulmus americana, Gleditsia
triacanthos, Carya ovata, Catalpa speciosa, Ulmus rubra, Aesculus glabra,
Gymnocladus dioicus, Nyssa sylvatica, Maclura pomifera, Fraxinus quadrangulata, and
Tilia americana (Table 2.1).
Changes in tree growth following invasion
Tree-ring analysis indicated significant growth changes following L. maackii
invasion. The most pronounced changes were in the rates of tree growth. Three-way
MANOVA and follow-up ANOVAs indicated that sites were significantly different in
51 terms of radial (RISLOPE) and basal area growth rates (BAISLOPE; P < 0.001; Table 2.2;
Figure 2.2). Paired t-tests indicated that all invaded sites except site D6 had significantly
reduced RI and BAI rates of growth from PRE to POST periods (P ≤ 0.05), and no
significant differences (P ≥ 0.13) were found for non-invaded sites in RISLOPE and
BAISLOPE (Table 2.3). Orthogonal contrasts for both RISLOPE and BAISLOPE showed that
pooled invaded sites were greater than pooled non-invaded sites. During PRE to POST
periods, the rate of radial growth in invaded sites was 58.02% less than non-invaded sites
(reduced 0.04 mm ⋅ yr-2), and the rate of BAI growth was 53.13% less in invaded versus
non-invaded sites (reduced 0.27 cm2 ⋅ yr-2; Table 2.3).
Three-way MANOVA and follow-up one-way ANOVAs revealed that sites were significantly different from PRE to POST periods for mean radial (RIMEAN) and mean
basal area (BAIMEAN) growth (Figure 2.2; Table 2.2). Paired t-tests demonstrated that all
non-invaded sites experienced significant increases in basal area growth (BAIMEAN)
across time (P < 0.001), while invaded sites showed a variety of patterns of growth including significant increases, decreases, and non-significant differences (Figure 2.2).
The mean radial growth (RAIMEAN) was reduced from PRE to POST periods in invaded sites, but differences were not significant (P = 0.23; Table 2.3). Orthogonal contrasts for
PRE to POST periods, pooled non-invaded sites had significantly greater mean RI and
BAI growth than pooled invaded sites (P = 0.03; Table 2.2). Two-way MANOVA and post tests indicated that four species grew differently depending on whether they were from invaded or non-invaded sites (species × invasion significant interaction; Table 2.2).
Liriodendron tulipifera grew better than Acer saccharum in invaded sites in terms of
52 BAIMEAN, and Juglans nigra grew better than Fraxinus americana in non-invaded sites
(Table 2.2).
Tree size was found to be significant; however, no difference of PRE versus
POST growth between tolerant and intolerant species was exhibited (Table 2.2). Trees
with DBH > 35 cm had significantly greater PRE to POST growth relative to smaller
trees, DBH 10 - 35 cm, in terms of RISLOPE, BAISLOPE, RIMEAN, and BAIMEAN, and this
pattern was consistent across all sites, regardless of invasion status (site × size interaction
n.s.; Table 2.2). During PRE to POST periods, mean radial growth in invaded sites was
17.44% less than non-invaded sites (reduced 0.64 mm ⋅ yr-1), and the mean BAI growth
was 15.80% less in invaded versus non-invaded sites (reduced 1.97 ± 0.98 cm2 ⋅ yr-1;
Table 2.4).
For assessment of the L. maackii population and biomass load effects, we found significant or nearly significant negative relationships between mean radial and mean basal area tree growth and L. maackii population size and L. maackii biomass (All P ≤
0.07; Table 2.4).
Intervention Analysis
The RMANOVA and RANOVA tests on the frequency of interventions found a significant interaction of invasion status × time; results not shown). Post hoc comparisons found that invaded and non-invaded sites were not significantly different through time in terms of the frequency of positive interventions (P = 0.34; 3.15% ± 1.78 of trees with positive interventions). For the within-subject time variable, the frequency of interventions in invaded sites was significantly different in the 5 -10 year period following L. maackii invasion and increased in a linear fashion until the period when the
53 greatest number of negative interventions was measured, 20 years after invasion. In post-
invasion periods, a substantial proportion of the trees was experiencing negative
interventions (41.50%), but no significant positive interventions (i.e., releases in the
growth of other trees) were detected at invaded sites.
The first significant growth reductions were detected 6.25 ± 1.24 years after
invasion, and the timing of growth reductions was fairly consistent with 66% of first
negative interventions (2 SD) occurring four to eight years after invasion. The
RMANOVA and succeeding RANOVAs involving the magnitude, intensity, and duration
of negative interventions found that the invasion status (invaded vs. non-invaded) × time
interaction was significant. These intervention variables were found to be not
significantly different through time in non-invaded series (P > 0.10), but in invaded sites, these variables were significantly different depending on time period (Figure 2.2).
Discussion
While tree-ring chronologies represent simultaneous responses to several growth factors (Schweingruber; 1996), our results indicate that a pattern of reduced, sustained tree growth following L. maackii invasion was among the strongest signals in our tree-
ring chronologies. This signal permitted the timing and quantification of invasive impact
and lends strong support to L. maackii having strong negative influence on the growth and productivity of canopy trees.
An important aspect of forest research is to attempt to predict the productivity of a site based on biotic and abiotic attributes. In Ohio’s deciduous hardwood forests, studies have found a number of site factors that affect tree growth such as parent material,
54 topographic position, and soil classification including surface soil depth, texture, and
drainage (Carmean; 1965; Anderson & Vankat; 1978). Indeed, we did also find
significant differences in productivity among sites; however, we found that site was
relatively unimportant compared to L. maackii invasion status. Eleven out of twelve
invaded sites showed consistent patterns of significant radial and basal area growth rate
decline in the 10 years following invasion. Moreover, non-invaded stands did not show
patterns of significant growth reductions throughout tree-ring chronologies.
Species identity is another important factor in predicting tree productivity
(Desplanque et al.; 1999) as well as the interaction of species and site (Orwig & Abrams;
1997). Not unexpectedly, we did find differences among species performance, and
despite the fact that we were not able to test the interaction of species × site due to
inadequate representation of species at all sites, our finding of Liriodendron tulipifera
having better growth through time than Acer saccharum in invaded sites, and in non-
invaded sites Juglans nigra growing better than Fraxinus pennsylvanica was intriguing
and warrants further investigation of species-specific responses to L. maackii invasion.
Although non-native shrubs are well known for their negative impacts, reduced
cover beneath both native and non-native shrub species has been reported (Hobbs &
Atkins; 1991). Native shrubs do have the ability to dominate their sites (Dickinson et
al.; 1993), but native shrubs in Ohio were not reported to be common historically (Braun;
1916). Presently, L. maackii can occur in great abundance with 100% coverage estimated at some sites (Luken et al.; 1997), and in this sense, L. maackii may be taking advantage of a previously unfilled midstory stratum, i.e., taking advantage of a ‘niche opportunity’
(see Shea & Chesson; 2002). Moreover, Luken (1988) estimated L. maackii biomass
55 production to be nearly equal to that of entire forests! Thus, exotic shrubs in great abundance may be more likely than native shrubs to be detrimental to indigenous plant communities (Collier et al.; 2002).
To our knowledge, no previous study has used dendrochronological methods to investigate changes in overstory tree productivity following the invasion of a non- indigenous species. This type of growth reduction is possible given that numerous studies have indicated growth releases following removal of native species including native shrubs (McClay; 1955; Chang et al.; 1996), small hardwood trees (Grano; 1970;
Strong & Erdmann; 2000), grasses (Gakis et al.; 2004), bamboo (Takahashi et al.; 2003), and entire understory woody plant strata (Fujimori; 2001).
Although we studied the patterns of tree growth over time and not the mechanism of action, interference competition is a likely explanation for reduced tree growth. In dense forests, tall plants are able to compete and limit the growth of smaller plants via light interception, and most often tall plants have a disproportional ability to pre-empt resources (i.e., assymetrical competition; Weiner; 1990). This is known as “overtopping” and is a classic example of resource pre-emption (Schwinning & Weiner; 1998), but it may not be the mode of competition in our study. Belowground interference may be a more plausible type of competition to explain reductions in tree growth following L. maackii invasion due to the differential stratification of L. maackii and trees. When competition is primarily for soil resources, it is often independent of plant size (Casper &
Jackson; 1997). What may be more important than plant size, is that competition is a phenomenon which depends on a multitude of factors including the availability of critical nutrients, effect of plant density, availability of water, efficiency of nutrient uptake, and
56 conversion to biomass (Schwinning; 1996). We should mention that extended
photosynethic activity and chemical inhibition may also be factors associated with L.
maackii competition as it does have a leaf phenology that extends well before and after
other native plants (Trisel; 1997), and negative allelopathic effects have been reported for
L. maackii and two other congeners (Norby & Kozlowski; 1980; Trisel; 1997; Skulman et
al.; 2004).
The observed changes in tree growth patterns were not likely associated with
responses to shifts in climate as we saw no apparent large-scale changes in precipitation,
temperature, or Palmer Drought Severity Index during the period of 1949 – 2000
(National Climate Data Center and National Oceanic and Atmospheric Administration;
2002). Pollution was also an unlikely cause of growth change during this time period because pollution-related declines in tree growth in the Ohio River Valley were already being detected previous to the beginning of our chronologies (McClenahen & Dochinger;
1985). It is normal for large groups of trees to die or exhibit growth declines in response to aging as well as environmental stresses (Mueller-Dombois; 1987); however, the asynchronous pattern of initial invasion among sites, but parallel temporal pattern of decline within sites following L. maackii invasion is evidence that leads us more toward causation, and away from correlation, of invasion-associated declines. Growth reductions were not likely associated with differences in forest age or density because these invaded and non-invaded stands were found to be not significantly different in these attributes. Furthermore, no reverse-j shaped decline in tree growth was detected in non- invaded stands, indicating that tree growth had not declined due to stand maturation.
Finally, measured trees were canopy dominants which do not succumb as quickly to
57 inter-tree competition as suppressed or midstory individuals (Winget & Kozlowski;
1965).
Growth reductions associated with L. maackii have the potential to be quite costly financially because it seems to be impacting the productivity of overstory hardwood trees, a resource with great ecological and economic value. We believe to have shown strong evidence for growth reductions in overstory trees with the presence of L. maackii in the understory. Roughly a 15.8% reduction in basal area growth was observed at invaded versus non-invaded sites. Obviously productivity of trees will vary as a function of the level of understory infestation, relative growth rates, and composition of overstory trees, all of which depend on local site conditions. If losses in overstory tree productivity are common, such as with L. maackii, then the total estimated monetary losses due to invasive plants could be even greater. Nonetheless, the presented data should command the attention of forest managers to consider the impact of invasive forest understory species on overstory productivity.
In the future, it will be important to understand the long-term effects of L. maackii
on the dynamics and species composition of invaded forests. Although our data suggest
that L. maackii seems to be able to successfully compete with canopy trees, as a shrub, L.
maackii does not possess the ability to ascend into their canopy position. Even so, L.
maackii could have strong ecological effects on communities as it has already shown
ability to negatively alter the abundance of the native seed and bud bank (Collier et al.;
2002), seedling growth and survival (Gorchov & Trisel; 2003; Hartman & McCarthy;
2004), saplings (KMH pers. obs.), and tree growth as reported in our study. It will,
therefore, be important to understand L. maackii’s effect on long-term forest dynamics.
58 A dense L. maackii shrub layer may have impacts that other lower forest strata have demonstrated, which is act as an ecological filter in directing canopy tree regeneration
(George & Bazzaz; 1999a). Lonicera maackii may impact long-term inter-individual or
interspecific performance, which could influence long-term canopy accession, species
composition, and successional trajectories (Grime; 2001). Although, a great deal is still
unknown regarding L. maackii, evidence for its pervasive impact makes it important to
limit its further spread, understand its mechanisms of impact, and investigate the
restoration potential of invaded areas in order to maintain sustainable forests.
59
59 Table 2.1. Description of sixteen sampled invaded and non-invaded sites. Sites of C7, C8, D7, and D8 were non-invaded; all other sites were invaded with L. maackii. Overstory dominants were the top three tree species in terms of density.
Overstory Median Understory L. maackii L. maackii Site Latitude, Soil type, Overstory dominant Basal area Overstory overstory sapling basal area L. maackii biomass 2 -1 2 -1 -1 Code longitude % grade ┼ spp. †* (m ⋅ ha ) † Density †§ age † density § (m ⋅ ha ) ‡ density ‡ § (kg ⋅ ha ) ‡ 39.19124 N, Eden silty clay loam C1 84.71330 W (EcD), 15-25% FRAM, JUNI, PRSE 31.2 343.4 65 71.4 1.8 1187.2 1127.3 39.24489 N, Casco loam (EcE), C2 84.62122 W 25-35% FRAM, CACO, PRSE 26.6 682.5 40 178.6 8.2 5607.3 6438.7 39.25010 N, Casco loam (EcE), C3 84.52542 W 25-35 % FRAM, JUNI, CEOC 30.3 484.3 45 1178.6 3.4 3709.1 1923.9 39.28148 N, Miamian silty loam C4 84.38792 W (MnC2), 2-12% FRAM, LITU, ACSA 19.5 334.8 25 1750.0 3.0 2446.6 1429.3 39.23295 N, Cincinnati silt loam C5 84.73143 W (CnC2), 15-25% ULAM, PRSE, GLTR 14.2 291.6 42 7250.0 1.6 2250.1 752.6 39.27628 N, Rossmoyne silt loam C6 84.53698 W (RpB2), 2-8% FRAM, QUAL, ACSA 31.0 422.5 52 821.4 0.6 2790.9 465.9 39.24444 N, Casco loam (EcE), C7 84.72742 W 25-35% FRAM, ACSA, ULRU 34.3 257.3 59 4928.6 - - - 39.12051 N, Pate silty clay loam (PfD) C8 84.81345 W 15-25% FRAM, ACNE, ACSA 21.9 284.6 58 107.1 - - - 39.88738 N, Miamian loam (MiB), D1 84.28391 W 2-6% FRAM, CAOV, JUNI 25.3 293.1 76 1035.7 5.2 1832.2 4253.9 39.71391 N, Miamian silt loam (MiB2) D2 84.27258 W 2-6% CACO, ACSA, PRSE 30.7 278.7 108 357.1 4.3 6208.4 2687.1 39.88583 N, Hennepin silt loam D3 84.15522 W (HeE2), 18-25% QURU, FRAM, ACSA 28.2 450.2 60 5892.9 0.7 3003.2 687.7 39.59489 N, Hennpin silt loam (HeF2) D4 84.35365 W 25-25% FRAM, LITU, JUNI 21.3 366.8 38 6892.9 0.8 1068.9 426.8 39.62081 N, Eldean silt loam (EMC2) D5 84.08420 W 6-12% FRAM, PRSE, ACNE 18.6 509.3 23 35.7 4.3 2798.2 3749.9 39.87267 N, Ross silt loam (Rs), D6 84.08325 W 2-6% FRAM, PRSE, ULAM 32.3 431.5 38 1678.6 1.7 2510.2 1240.2 39.65327 N, Miamian clay loam D7 84.23196 W (MiD2), 12-18% FRAM, ACSA, JUNI 31.2 233.4 96 3607.1 - - - 39.63585 N, Hennepin silt loam D8 84.42258 W (HeE2), 12-25% QUAL, FRAM, CAOV 35.8 255.2 63 14857.1 - - -
Mean - - - 27.2 370.0 55 3165.2 3.0 2951.0 2098.7 SE - - - 2.3 30.1 5 250.9 0.7 453.4 536.4
† Trees ≥ 10 cm diameter at breast height (DBH); ‡ Lonicera maackii ≥ 30 cm ht; § Density in individuals ⋅ ha-1; * Species abbreviations as the following: Fraxinus americana (FRAM), Juglans nigra (JUNI), Prunus serotina (PRSE), Carya ovata (CAOV), Celtis occidentalis (CEOC), Liriodendron tulipifera (LITU), Acer saccharum (ACSA), A. negundo (ACNE), Ulmus americana (ULAM), Gleditsia triacanthos (GLTR), Quercus alba (QUAL), Q. rubra (QURU), Ulmus rubra (ULRU), Carya cordiformis (CACO); ┼ (Davis et al.; 1976; Lerch et al.; 1980).
60 Table 2.2. Three-way and two-way MANOVA and follow-up ANOVA results for growth differences for pre- versus post-invasion periods (10-years prior to invasion versus 10-years post-invasion, respectively). The statistically significant differences (P < 0.05) for follow-up ANOVAs were the following: A = RIMEAN, B = RISLOPE, C = BAIMEAN, D = BAISLOPE.
Parameters Wilks’ F-Value Num Den P-Value Follow-up Lambda DF DF ANOVA
3-way MANOVA Site 0.269 3.47 44 373 < 0.001 A, B, C, D Shade-tolerance 0.993 0.18 4 97 0.949 n.s. Size 0.193 2.16 4 97 0.048 A, B, C, D Site × Shade-tolerance 0.646 1.13 40 370 0.279 n.s. Site × Size 0.799 1.13 20 23 0.315 n.s.
2-way MANOVA Species 0.454 1.63 80 590 < 0.001 C Invasion 0.757 11.97 4 149 < 0.001 C Species × Invasion 0.214 2.72 12 152 0.002 C
61 Table 2.3. Means of PRE versus POST periods in invaded and non-invaded tree-ring series and t-test results. Radial increment (RI) slope is measured in mm · yr-2 units; basal area increment (BAI) slope is measured in cm2 · yr-2; RI mean is measured in mm · yr-1; BAI mean is measured in cm2 · yr-1. P-values are reported after Bonferroni corrections. Significant differences are indicated by asterisks.
Invasion Variable PRE POST % P-Value status change Invaded RI slope 0.05 ± 0.01 -0.01 ± 0.01 -0.10 0.05*
Non-invaded RI slope 0.07 ± 0.01 0.08± 0.02 -0.10 0.13
Invaded BAI slope 1.10 ± 0.08 -0.39 ± 0.05 -0.35 0.01*
Non-invaded BAI slope 1.14 ± 0.10 1.38 ± 0.06 0.21 0.07
Invaded RI mean 2.70 ± 0.06 1.81 ± 0.55 -0.33 0.01*
Non-invaded RI mean 2.68 ± 0.04 2.75 ± 0.64 0.03 0.23
Invaded BAI mean 12.89 ± 1.11 13.53 ± 1.06 0.05 0.20
Non-invaded BAI mean 12.57 ± 1.08 15.13 ± 1.03 0.20 0.03*
Table 2.4. Change in invaded overstory tree growth from PRE to POST periods as predicted by regressing productivity against L. maackii density and L. maackii biomass.
Productivity L. maackii infestation metric Differerence in PRE R2; P-Value Variable vs POST growth ± SE RI Mean Per 1000 L. maackii -0.56 ± 0.10 mm · yr-1 R2 = 0.69; P < 0.001 individuals BAI Mean Per 1000 L. maackii -0.74 ± 0.42 cm2 · yr-1 R2 = 0.34; P = 0.073 individuals RI Mean Per 1000 L. maackii kg -0.79 ± 0.11 mm · yr-1 R2 = 0.57; P < 0.001 biomass BAI Mean Per 1000 L. maackii kg -1.04 ± 0.43 cm2 · yr-1 R2 = 0.46; P = 0.047 biomass
62
Figure 2.1. Location of 16 sampled sites throughout southwest Ohio, U.S.A (enlarged view), and site location within Ohio (map inset). Sites were in the vicinity of the cities of Dayton (D1 – D8) and Cincinnati (C1 – C8). Sites C7, C8, D7, and D8 were non-invaded, and all other sites were invaded at various times. Site codes correspond to Table 2.1 and Figure 2.2. Grey polygons indicate urban and suburban regions. Gray lines indicate rivers and streams. Thick black lines indicate county divisions, and thin black lines designate U.S. and state highways.
63
Figure 2.2. Master chronology for each site indicating raw mean basal area index (BAI) growth for all cored trees at each site versus time. Panels C1 – C8 represent trees in Cincinnati and vicinity, and panels D1 – D8 represent trees in Dayton and vicinity. Panels C7, C8, D7, and D8 designate tree-ring growth from non-invaded sites; all others are from invaded sites. Arrows indicate time of initial L. maackii invasion.
64
Figure 2.3. Magnitude, intensity, and duration of interventions relative to time of initial invasion (time = 0). Significant differences of values within panels (P < 0.05) are given by different lowercase letters. Values among times for non-invaded trees were all nonsignificant. Intervention magnitude, intensity, and duration were calculated using the methods of Gray et al. (2004). Magnitude was calculated by subtracting the long-term mean from each 5-year interval growth value within a series and summing these values over the series period. Intensity was calculated by taking the ratio of the magnitude and duration and was calculated as the average magnitude per intervention. Duration was the mean number of consecutive significant interventions for the series period.
65
Chapter 3: Changes in forest structure and species composition following invasion
by a non-indigenous shrub, Amur honeysuckle (Lonicera maackii)
Introduction
Impacts of invasive species are becoming a matter of great scientific and public
concern as invasive species are not only threatening endeavors related to human
sustainability such as agriculture, commerce, and human health (Sumner; 2003), but they
are also profoundly detrimental to natural areas which have innate ecological value and
provide free ecosystem services (van Wilgen et al.; 2004). Damages due to invasive
species in the United States are estimated at $137 billion USD · yr-1 (Pimentel et al.;
2005), and roughly 5000 exotic species have escaped within the United States (Morse et al.; 1995). Ecologically, invasive species are exceedingly damaging. Following habitat destruction, they are cited as the leading cause of reductions in biodiversity (Wilcove et
al.; 1998), and invasives have the potential to impact natural systems at all biological
levels from genetic to global (Mack et al.; 2000). Campbell (1997) ranked biological
invasions as comparable in concern to other global environmental problems.
With expansions of current invasions expected to continue, studies assessing
invasive species impacts are becoming increasingly important (Williamson; 2002). The
first step towards invasive species’ management is to demonstrate that the invasive
species has a negative effect (McCarthy; 1997b). Most ecological studies, however, use
only short-term approaches, which may not as accurately represent the kinds or degrees
of ecological changes detectable with longer-term studies (Taylor; 1989). Furthermore,
short-term investigations can be particularly problematic in studying long-lived trees and
66 successional processes because both may respond slowly to certain types of changes
(Davis; 1989).
Generally, most long-term ecological impact studies were not designed with invasive species in mind, and specifically there has been little overlap of the disciplines of invasive species ecology and succession. Davis et al. (2001) state that this lack of intersect may be due to the frequent study of “conspicuous invaders” and the belief that invasion is a unique phenomenon requiring “special explanation” which is not applicable to traditional successional studies. The addition of a dominant species to a natural system can profoundly affect a number of community properties, especially the interactions of biota (Thompson et al.; 1995); therefore, there is a likely possibility for highly effective invaders to strongly influence the successional dynamics of communities. Thus, a logical step towards advancing invasive species ecology is to employ classical ecological investigative methods to understand the long-term successional impacts of invasives.
The optimal approach to understanding the complex, long-term impacts of invasion on succession would be to implement well-replicated, controlled studies at sites well before and after invasion. These types of studies have been conducted infrequently; however, one way to augment the paucity of these types of studies is to use chronosequence methods as a substitution of space for time thus extracting temporal trends from a series of different aged samples (Pickett; 1989). Chronosequence studies have been frequently used to examine the spatio-temporal dynamics of communities, and in particular, to describe the processes and patterns of succession (Pickett; 1989). In
67 invasive ecology, these types of studies have been infrequently conducted, but studies of this kind could lend important insights into long-term invasive species impacts.
Forests are a potentially useful system to study higher order, long-term invasion questions given that they are temporally and spatially dynamic, vertically stratified ecosystems. Individuals that successfully establish in lower forest strata are the recruitment capital for what may eventually become dominant canopy trees (Kozlowski;
2002). Most invasive species studies are conducted from a 2-dimensional approach by studying impacts on individuals within the same stratum as the invader, but in forests lower layers have been known to act as ecological filters and influence the composition and abundance of recruiting strata (George & Bazzaz; 1999b). Winnowing events do naturally occur during the development of any “ascending” forest layer (Young et al.;
2001), but in heavily invaded communities, changes to any recruitment layer in a forest could instigate successional divergence. Thus comparison of sites with different invasion times could lend insight into the long-term successional impacts on pattern and process associated with particular invasions. Two requirements of investigating long-term invasive impacts are that (1) the age of the invader must be detectable, and (2) for invaded sites (such as with forests with long-lived woody species), the impacts of invasives must have storage effects which can be measured as changes in abundance, structure, or composition through time. For this experiment we sought to evaluate the possibility of using chronosequence techniques to understand the long-term impacts of invasive species on community succession.
68
As a case study, we wanted to investigate Amur honeysuckle (Lonicera maackii), an exotic, aggressive shrub that is able to virtually dominate the midstory layer of forest interiors as well as successfully invade open habitats and forest edges (Luken; 1988). In forests, L. maackii is reported to have nearly 100% midstory coverage (Luken; 1997a) and have a net primary productivity that is equal to that of entire forests (Luken; 1988).
This shrub was intentionally introduced into the United States in 1898 for horticultural and conservation purposes by the U.S. Department of Agriculture’s Foreign Seed and
Plant Introduction Program (Luken & Thieret; 1996), but avian dispersal (Ingold &
Craycraft; 1983) plus its rapid individual and population growth rates have allowed it to escape to at least 26 eastern states in the U.S. (Hutchinson & Vankat; 1997). In southwest Ohio where we conducted our study, L. maackii was introduced around 1960
(Hutchinson & Vankat; 1998), and despite the fact that shrubs are reported to be historically rare in this area (Braun; 1916), L. maackii invades intact forests quite successfully and dominates the midstory stratum of forest communities. In long-invaded sites, forests are virtually two-tiered systems consisting only of overstory trees and L. maackii shrubs with few recruiting understory plants.
The long-term impacts of L. maackii on forest succession could potentially be profound, and we wanted to utilize chronosequence techniques to test for possible long- term impacts of L. maackii on the composition, abundance, structure, and recruitment processes of forest vegetation. Specifically our goals were to (1) test the long-term effects of L. maackii invasion using a number of univariate and multivariate techniques by comparing the abundance, diversity, and species composition at sites with different
69 invasion chronosequence levels (non-invaded, recently-invaded, and long-invaded), (2) compare differences in patterns of multivariate species composition in relation to invasion status, (3) identify indicator species which demonstrate affinity for sites with different invasion levels, and (4) quantify patterns of between-strata compositional similarity and possible recruitment variation at sites with different levels of invasion.
Methods
The chronosequence method, a space-for-time substitution approach was used to study the long-term effects of L. maackii on forest structure, composition, and regeneration (see Pickett; 1989). Twelve sites with various times of initial L. maackii establishment were sampled, as well as four non-invaded (control) sites, to investigate possible community-level changes associated with invasion. Sites were randomly selected from potential locations in secondary forest stands, were free from apparent, large-scale, post-tree-harvest disturbances, and were fairly homogenous in terms of tree composition and stand age. All non-invaded sites were within 100 m of L. maackii patches and were therefore likely unaffected, yet potentially invasible, by L. maackii.
Satellite images were used to estimate patch size from June 2000 images
(http://www.terraserver.com).
All sites were located in Montgomery and Hamilton Counties in southwestern
Ohio, U.S.A. The region is typified by a mosaic of agricultural land, semi-isolated forest patches and connecting corridors, rivers and streams, and urban and suburban developed areas (Davis et al.; 1976). Total forested land in this region is roughly 13% (Griffith et
70 al.; 1991). The forest type in this area has been classified as beech-maple deciduous forest (Braun; 1950); although white ash (Fraxinus americana) is also a major overstory dominant (Bryant & Held; 2004).
Climate in the region is continental with warm, humid summers and cold, cloudy winters. Temperatures range from a mean low in December of 0 ºC to a mean high in
July of 25 ºC. Precipitation is roughly 100 cm · yr-1 with half typically falling during the
growing season (Davis et al.; 1976). Edaphic conditions naturally varied among
locations, but potential sites were held to criteria with topographies that were flat to
gently rolling with soil texture that ranged from loam to clay loam (Davis et al.; 1976;
Lerch et al.; 1980). Soil substrate was largely glacial till with an underlying limestone
parent substrate (Forsyth; 1970).
Vegetation Sampling
Composition and structure of forest strata were measured using the Point-
Centered Quarter (PCQ) sampling method (Cottam & Curtis; 1953). Sampled vegetation
included overstory trees, saplings, shrubs, vines, tree seedlings, herbs, and seed bank as
well as the L. maackii shrub layer. Fourteen PCQ points at each site were positioned 10
m apart along two parallel 60 m transects. Transects were situated 50 m apart and to
avoid edge effects, were located at least 50 m from forest boundaries. The nearest
overstory tree to each PCQ point with diameter at breast height (DBH) ≥ 10 cm was
cored using an increment borer, and trees were aged by counting annual rings and cross-
dating (Phipps; 1982). Tree species identities were noted, and distances to PCQ points
71 and DBHs were measured and used to compute overstory tree basal area, density, and composition (Krebs; 1999).
The time of initial L. maackii invasion was used as our chronosequence index, and determination of exact invasion time was possible because L. maackii is a semi-ring- porous woody shrub that produces visually detectable annual growth rings (Chiu &
Ewers; 1992). One shrub was cut in each PCQ quadrant at 5 cm ht and annual rings were counted (n = 48 shrubs / site). Distances from L. maackii shrubs to PCQ points were measured and converted to densities (Krebs 1999). To assess woody plant composition and structure, densities of tree seedlings (defined as ht ≤ 30 cm) and tree saplings and non-L. maackii shrubs (defined as ht > 30 cm and DBH < 2.5 cm) were quantified in 5 m2 circular plots in each PCQ point quadrant, respectively. Herbaceous vegetation was estimated via visual percent cover, and L. maackii seedlings were assessed using count data (Daubenmire; 1959). Biomass of L. maackii was estimated via allometric methods.
Randomly selected forest interior shrubs of various sizes were measured in terms of their canopy height and spread, number of stems, largest stem radius, and basal area (N = 32).
To measure the germinable seed bank composition (Brown; 1992), soil plugs were collected at each PCQ point with a bulb planter to a depth of 10 cm. Soil plugs were spread in flats on a vermiculite medium (25L × 25W × 3D cm). Ten seed-free flats containing vermiculite were intermixed among the array to monitor potential seed contamination, and all flats were randomized every two weeks. Germinated plants were removed upon positive identification, and trays were kept in a greenhouse from 16-May-
2000 to 16-Nov-2000. At the end of this period, all germinated, unidentifiable plants
72 were transferred to larger containers and grown for an additional 6 month period until identifiable to species (or genus if species was not identifiable). Sampling of L. maackii seedlings, herbaceous vegetation, vines, and harvesting of soil plugs was conducted from
1-May-2000 to 15-May-2000. Measurement and coring of trees occurred from 1-Sept-
2000 to 1-Oct-2000, and tree seedlings, native shrubs, saplings, and L. maackii adults were sampled from 1-June-2000 to 31-July-2000. Fourteen tree cores, soil cores, and
PCQ points were sampled at each site (N = 224 total PCQ points). Gleason & Cronquist
(1991) was used as the taxonomic authority for all vegetation identification. Cooperrider et al. (2001) was used to assess indigenous or nonindigenous status of species.
Statistical analysis
Ordinary least squares linear regressions were used to compare differences within each stratum of forest structure among L. maackii invasion levels. Strata densities were the dependent variables and initial time of L. maackii invasion at each site was the predictor variable. Species richness (S) within each stratum was compared among L. maackii invasion levels (non-invaded, recently-invaded, and long-invaded) by analysis of defining invasion chronosequence levels, “non-invaded” sites did not include L. maackii;
“recently-invaded” sites included those with an initial time of invasion of 12 – 17 years before sampling in 2000, and “long-invaded” included initial invasive time of 18 - 26 years before sampling. Invasion level at each site was used as the factor variable, and species richness was the response variable. Percent native species for each stratum was compared via ANOVA with percent native composition as the response variable and invasion level as the factor variable. Assumptions of normality and equal variance were
73 satisfied prior to ANOVA's, and log normal transformations were used where appropriate
(Zar; 1999). Untransformed means ± 1 SE are reported.
To compare multivariate species composition among chronosequence levels within a stratum, we performed beta-flexible hierarchical agglomerative clustering using the Bray-Curtis similarity coefficient (S17; Legendre & Legendre 1998). The Bray-Curtis
coefficient was used because it is less affected by outliers relative to other indices,
preserves sensitivity in heterogeneous datasets, and is independent from joint absences.
The beta-flexible clustering method was used because it is able to control space
contraction allowing elements to be better grouped relative to other clustering methods
(McCune & Grace; 2002). Percent cover (%; herbs & vines), density (numbers per m2; seedlings, saplings, and seed bank), and basal area (m2 per ha; overstory trees) were used
to construct similarity hemi-matrices. A natural weighting procedure was performed prior
to calculating similarity matrices by dividing raw measurements by sample unit totals,
which standardizes for both differences in site productivity and scale of measurement
(Mielke; 1984).
Multivariate response permutation procedures (MRPP; Berry et al. 1983) were
performed following cluster analysis as a nonparametric way to test for overall
differences in species composition between (1) invasion chronosequence levels and (2)
non-invaded versus invaded groups. MRPP tests were performed using data from each
forest stratum, and Bonferroni adjustments were made to account for multiple statistical
comparisons (Zar; 1999).
74
To test species’ faithfulness within invasion chronosequence levels (i.e., group affinity), an indicator species analysis was employed (Duffrene & Legendre; 1997). This procedure uses each species’ frequency and abundance within a group (invasion chronosequence level) to calculate importance values (IVs). IVs range from 0 (no indication) to 100 (perfect indication, Duffrene & Legendre; 1997). Species with large
IVs have both large frequency and abundance values and are classified as having a statistically significant affinity for a group. Rare species have small IVs and are not statistically significant (McCune & Grace; 2002).
To test multivariate compositional similarity between forest successional layers and among invasion chronosequence levels, cross-strata congruence analyses were performed (Su et al.; 2004). We used standardized PCQ point abundance values to calculate Bray-Curtis similarities between each pair of vegetation strata at all PCQ points
(N = 56 for non-invaded; N = 84 for recently-invaded and long-invaded). We summarized Bray-Curtis similarities between all logical pair-wise combinations of recruitment strata for each invasion level as a matrix for each, and Mantel tests were used to examine between-strata similarities to compare matrices. Similarities were expressed as correlation values (Legendre & Legendre; 1998), and Monte Carlo procedures with
999 permutations were used for significance testing (McCune & Medford; 1997).
PC-ORD (version 4.01) was used to perform cluster analysis, MRPP, indicator species analysis, Mantel tests, and calculate Bray-Curtis similarities (McCune &
Mefford; 1999). NCSS was used for regression and ANOVA calculations (Hintze;
2000). All tests were considered significant if P < 0.05.
75
Results
Forest stand characteristics
Sampled canopy trees were generally even-aged with trees having a median age
of 55.2 ± 5.2 SE yrs. Sites ranged in canopy age, density, and basal area, but pooled
invaded sites were not significantly different than non-invaded sites with respect to these
variables (all P ≥ 0.14). Overstory tree density was 370.0 ± 30.1 stems ⋅ ha-1, and basal
area was 27.2 ± 2.3 m2 ⋅ ha-1. Forest patches were 112.9 ± 20.3 ha in size. Canopies
were composed mainly of Fraxinus americana (34.4%) but consisted of a mixture of
moderately common species (5 - 10% each species): Acer saccharum, A. negundo,
Juglans nigra, Liriodendron tulipifera, Quercus alba, Celtis occidentalis, and several
more rare species (< 5% each species): Carya cordiformis, Carya ovata, Q. rubra, Q.
prinus, Q. muehlenbergii, Fraxinus quadrangulata, Acer saccharinum, A. rubrum,
Prunus serotina, Ulmus america, U. rubra, Gleditsia triacanthos, Catalpa speciosa,
Aesculus glabra, Gymnocladus dioca, Nyssa sylvatica, Maclura pomifera, Tilia
americana, Robinia pseudoacacia, Fagus grandifolia, Ostrya virginiana, Sassafras
albidum, Carpinus caroliniana, and Crataegus species. Cluster and MRPP analyses
found that canopy composition was not significantly different among invasion
chronosequence levels or between non-invaded versus invaded groups (P ≥ 0.61; Figure
3.1). Canopies contained the least number of species of any layer (31) with increasing
richness represented in the sapling (33), seedling (39), germinable seed bank (52), and
herb (68) layers.
76
Lonicera maackii characteristics
Sites were initially invaded by L. maackii ranging from 12 to 26 yrs before sampling in 2000. Mean L. maackii density was 2951 ± 453 plants ⋅ ha-1 (range 1887 to
6208). Mean L. maackii basal area was 3.0 ± 0.7 m2 ⋅ ha-1, and mean L. maackii biomass was estimated at 2098.7 ± 536.4 kg ⋅ ha-1. Basal area of L. maackii was found to be the
best predictor of individual shrub biomass [L. maackii biomass per individual (kg) =
basal area (in cm2 units) × 0.907 + 0.147, R2 = 0.91]. Mean age of L. maackii was
significantly predicted by plant density (R2 = 0.22; P = 0.04), honeysuckle biomass ⋅ ha-1
(R2 = 0.67; P < 0.001), and honeysuckle basal area (R2 = 0.47; P = 0.02).
Changes in within-layer structure and species richness
Along the invasion chronosequence, regression analyses and ANOVAs provided strong evidence of within sub-canopy-layer structural differences (i.e., densities; P ≤
0.01). For long-invaded versus non-invaded sites, the herbaceous layer demonstrated a
57% reduction in cover; seedlings exhibited a 58% stem density reduction (i.e., 14300 ±
2595 fewer stems · ha-1); and saplings demonstrated the greatest structural difference,
90% fewer stems (i.e., 6645 ± fewer 472 stems · ha-1; Figure 3.2). Conversely, the
germinable seed bank density exhibited a 78% increase in the non-invaded versus the
long-invaded sites. We found that using all sites with controls included (versus using
invaded sites only) did not affect the significance of the results.
Significant declines in seedling and sapling species richness were also detected
along the L. maackii chronosequence using ANOVA and Bonferroni post hoc tests (Table
3.1; P < 0.04). Interestingly, despite findings of reductions in herb density with
77 increasing L. maackii age, no significant declines in herb species richness were detected.
The germinable seed bank, however, did exhibit significant species richness reductions
(Table 3.1). The greatest reductions in species richness along the invasion chronosequence were exhibited by the sapling layer, followed by the seedling and seed bank layers (Table 3.1).
Changes of within-layer multivariate composition
Multivariate species composition examined using beta-flexible clustering and
MRPP analyses revealed that significant differences were evident in non-invaded versus invaded sites for all sub-canopy forest layers (MRPP, P < 0.03; Figure 3.1). Cluster analysis of the seedling and sapling strata showed significant compositional separation among the non-invaded, recently-invaded, and long-invaded sites (MRPP, P < 0.01).
The herbaceous stratum did not show significant differences between the recent- and long-invaded sites, but non-invaded sites were significantly different in composition versus invaded sites (MRPP, P ≤ 0.03). The seed bank stratum did not show significant separation of the recently-invaded sites versus long-invaded sites; however, non-invaded sites clearly clustered separately from the other sites (MRPP, P ≤ 0.002; Figure 3.1).
Species affinity within invasion chronosequence levels
Indicator species analysis found that certain species showed significant affinities for specific invasion chronosequence groups (Table 3.2). Within the seedling, sapling, and seed bank strata, relatively few species were significantly associated with the long- invaded sites as low species frequency and abundance values were common in the long- invaded sites for these layers. All significant indicator species in the seedling and sapling
78 layers were native except Rosa multiflora and Rhamnus cathartica, which were most strongly associated with recently-invaded sites (Table 3.2). The herbaceous layer had the greatest number of significant indicator species. Six herbaceous non-native species were found to have significant indicator values, and all were associated for the most part with recently invaded and long-invaded sites (except Alliaria petiolata which was mostly strongly associated with long-invaded sites). The seed bank layer had the least number of significant indicator species. Non-invaded and recently-invaded groups had the greatest
IVs, but these were not significantly different (P = 0.36).
Changes in among-layer multivariate composition
Cross-strata congruence analysis found that regardless of the layers being
analyzed, a strong pattern of reduced between-strata compositional similarity was evident
as the invasion chronosequence progressed. In all examinations, the greatest between-
strata similarities were found for the non-invaded sites (mean r = 0.55 ± 0.22), followed
by recently-invaded sites (mean r = 0.34 ± 0.14), and long-invaded sites (mean r = 0.14 ±
0.06; Figure 3.3). For the non-invaded sites, the layers with the greatest similarities were
herb vs. seed bank (r = 0.84), which were followed by tree vs. seedling, tree vs. sapling, and sapling vs. seedling (r = 0.65 to 0.67). Also for the non-invaded sites, the seedling
vs. seed bank and tree vs. seed bank had smaller between-strata similarities (r = 0.21 and
0.26, respectively); therefore, despite variance in between-layer similarities,
compositional similarity of strata was reduced with time of L. maackii occupancy (Figure
3.3).
79
Discussion
There is a compelling need to understand the long-term impacts of invasive species, and in particular, impacts on community dynamics. One way that long-term invasive impacts can be studied is by applying chronosequence techniques to investigate similar communities which have been invaded at various points in time. Our study of forest infestation by L. maackii is an effective example of how the invasion of an aggressive, selectively filtering biological agent can influence patterns of community composition, diversity, and structure. The potential strength of this type of investigative approach, particularly in forested ecosystems, is that it combines chronosequence techniques with invasive species to lend insight into the temporal processes, vertical structure, and regeneration dynamics of invaded forest communities.
When a mixed deciduous forests of the eastern United States is disturbed, a secondary forest is initiated which potentially can progress through the successional stages of stem initiation, reorganization, aggregation, stem re-initiation and old growth
(Nyland; 2002). Excluding a brief period immediately following canopy closure, a general increase in seedling and sapling recruitment and sub-canopy stratification occurs with increasing age of the forest stand (Frelich; 2002). Naturally, the composition and structure of forests in our study area is dictated by topography, soil type, species availability, stochastic forces, and stage of succession (Forsyth; 1970); however, in relatively mature forests in the area, we typically expect to find a taxonomically diverse, vertically stratified system of overstory trees and sub-canopy regeneration layers (Braun;
1950).
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In L. maackii-invaded sites relative to non-invaded sites, we found a significant reduction in the density and diversity of a number of forest layers including herbs, tree seedlings and saplings. Sites that were long-invaded were composed almost exclusively of a monospecific layer of L. maackii beneath overstory trees. The extended outcome of
L. maackii invasion is unclear, but infested forest stands appear to be experiencing a pronounced reduction in aboveground stem density and species richness of both woody and herbaceous species. Moreover, in sites that were long-invaded, less compositional similarity was found between forest strata compared to L. maackii-free forests. These results indicate that forests invaded by L. maackii are likely experiencing a diversion in successional trajectory compared to non-invaded sites.
Throughout the landscape where we conducted our study, native shrubs are a rarely encountered life form (Braun; 1916), and despite its relatively recent introduction,
L. maackii has been able to effectively spread and dominate in forest interiors
(Hutchinson & Vankat; 1997). Several studies have reported that closed forests frequently have fewer successful exotics (Webb & Kaunzinger; 1993; Robertson; 1994), and Richardson (1998) states that new life forms, as well as new taxonomic groups can be particularly successful at establishing and dominating ecosystems. Thus, when forests in our area are invaded by non-indigenous shrubs, communities may be “competitively naïve” with respect to shrub competition, and as L. maackii invades, it may be occupying a previously unfilled midstory niche.
Shrub impacts on succession are often debated because contradictory results have been demonstrated. Some shrubs are able to act as chaparone or nurse species by
81 buffering against harsh abiotic conditions, but most of these cases occur in open, arid habitats (Gomez-Aparicio et al.; 2004). In secondary forests, shrubs generally act in an inhibitory fashion on woody plant recruitment and have even been used as a management tool in powerline corridors to intentionally inhibit tree regeneration (Luken; 1990a).
Relative to other plant forms, shrubs seem to be especially competitive. In a global study of 655 woody plants, Binggeli (1996) found that non-native shrubs were the most common growth form of highly impacting invaders, and Holm et al. (1977) classified shrubs, particularly ornamentals, as the most aggressive group of invasive plants.
While it seems that certain non-native shrubs including L. maackii may be altering forest vegetation patterns and recruitment, the phenomenon of low forest layers acting as ecological filters may require further investigation. Native ferns were shown to have negative impacts, especially on the survival, spatial distribution, and composition of tree seedlings (de la Cretaz & Kelty; 1999; George & Bazzaz; 1999b). Poor seedling success of certain tree species below ferns may be due to a number of factors including light reduction, a difficult-to-penetrate frond layer, and possible changes in the behavior of seed predators below ferns (George & Bazzaz; 1999a). Lonicera maackii also has a dense canopy and an extended period of leafout which may likewise result in both quantitative and temporal light attentuation for plants growing beneath it as well as create a physical barrier retarding the growth of native seedlings (Trisel; 1997).
Other studies have suggested that dense understories can alter microhabitat conditions and reduce understory regeneration. Beckage et al. (2000) found that seedling recruitment and understory light levels were not increased beneath Rhododendron
82 maximum shrubs during a four-year period following overstory tree removal and gap creation. The authors suggested that the dense Rhododendron shrub layer may neutralize recruitment opportunities and proposed that a large-scale disturbance of both the understory and overstory may be needed in order to enable tree seedling establishment.
In L. maackii thickets, tree recruitment may also be difficult to initiate. Luken et al.
(1997) found that tree seedling recruitment was not augmented during a three-year period after gap creation involving L. maackii removal. Other studies involving native shrubs
(Niering et al. 1986; Hobbs & Atkins 1991), bamboo (Griscom & Ashton; 2003), and understory grasses (Bowersox & McCormick; 1987) have similarly found that sub- canopy vegetation can act as an ecological filter which can cause delays and/or inhibition of forest succession.
Impacts on lower forest recruitment layers can be ecologically significant because seedlings and saplings are the investment foundation for future overstory development
(Kozlowski; 2002). If a lower stratum, such as L. maackii, functions as a selective filter on forest recruitment, then this sieve can cause disruption in the linkages between lower seedlings and saplings and the structure and composition of overstory trees (see Lawton;
1987). The mechanism by which L. maackii changes sites for competing plants is still unclear. Lonicera maackii invasion has been associated with reduced light, temperature, soil moisture, and pH levels (C.H. Keiffer, personal communication). Furthermore, the significance of micro-site conditions change as plants respond to shifts in ontogenetic development (Valerde & Silvertown; 1998), thus it will be difficult to predict how native
83 species will respond to micro-habitat heterogeneity associated with L. maackii as they pass through life history stages.
Despite the fact that native shrubs are relatively rare in forests of our study region, invasive, non-indigenous shrubs have become common members of the flora. Many increases in range have been documented for invasive shrub species, and these increases are accompanied by reductions in native seedling and sapling recruitment. Merriam &
Feil (2002) studying the impact of Ligustrum sinense on mixed hardwood forests in
North Carolina, found this shrub to be associated with a 42% reduction in herbaceous species richness, and tree regeneration was suppressed almost completely. In a study in
New Hampshire, Frappier et al. (2003) found that Rhamnus frangula presence was more important than abiotic factors in explaining herb abundance and native tree seedling density and species richness. Fagan & Peart (2004) found in New Hampshire that R. frangula was associated with reduced native sapling growth and survival as well as changes in composition toward more shade tolerant species. In a restoration experiment in Ohio, Hartman & McCarthy (2004) reported that several species of tree seedlings experienced reduced survival and growth below L. maackii thickets.
Several other studies involving non-native plants in the eastern United States caution that the already-escaped shrub species of Berberis thunbergii, Rosa multiflora,
Elaeagnus umbellata, and Pyrus calleryana, have competitive properties which could ultimately reduce native species abundance and diversity (Ehrenfeld; 1999; Yates et al.;
2004; Vincent; 2005). Furthermore extending this to international venues, shrubs may also be a problem as Ligustrum robustum, Chrysanthemoides monilifera, and Buddleja
84 davidii are reported to be a serious threat to native plant succession in Australia, La
Réunion Pacific Island, and New Zealand, respectively (Lavergne et al.; 1999; Matarczyk et al.; 2002; Bellingham et al.; 2005). While these studies report changes in native species abundance, diversity, and/or composition following non-native shrub invasion, many investigations were limited in spatial, temporal, and biological scope, which may sacrifice the validity and interpretive power of the investigation (see Booth et al.; 2003a).
Invasion is a very complex process, and when the main goal of an experiment is to measure the impacts associated with invasion, it is important to learn as much as possible regarding the kinds and magnitudes of impacts. One problem with invasive species ecology is that many studies frequently report impacts at only a few locations, from measurement of only a few biological variables, and over a relatively short period of time (Parker et al.; 1999). Thus there is a need for the investigation of long-term changes in patterns and alteration of higher order organizational processes associated with invasion.
Explanation of successional processes on plant community composition and structure has long been a major focus of ecological research (e.g.,Gleason; 1927;
Clements; 1936; Watt; 1947; Curtis; 1956; Drury & Nisbet; 1973; Horn; 1974; West et al.; 1981; Peet; 1992). One model of community dynamics suggests that individuals and/or species that survive over time have specific traits that allow them to successfully pass through various hierarchically arranged biotic and abiotic filters, such as site availability, species availability, and species performance (Lawton; 1987; Pickett et al.;
1987). Other models that may explain our results include individuals in the most heavily
85 invaded habitats may be more stress tolerant or competitive (Grime; 1977). Also, our sampled communities may fit the indeterminant successional models proposed by Drake
(1990), which includes multiple pathways, for example (1) divergent successional trajectories for invaded versus non-invaded sites and (2) convergent trajectories of sites with similar invasion status.
Admittedly certain caveats must be added with regards to our study. First, chronosequence studies assume that sites are comparable across space with the exception of the variable of interest, and second, chronosequence studies also assume that space is a valid substitution for time (Pickett 1989). Another related non-testable assumption is whether observed disparities at sites are due to pre-invasion differences. It has been long debated if certain sites have attributes which make them more invasible (Lonsdale; 1999), and it may be these same attributes which predispose invaded sites to inhibited regeneration, reduced species richness, and less compositional similarity versus non- invaded sites. Finally, given the long life-span of forest species relative to our chronosequence, the long-term consequences of L. maackii invasion are unknown.
Relatively speaking, this plant is a recent addition to the regional flora of this area
(Luken; 1990a).
Overall, we have found that sites invaded by L. maackii were different in a number of attributes including reduced species richness, density, and between-strata compositional similarity. We recommend that additional studies use chronosequence methodologies to investigate the possible long-term effects of invasive species on forest pattern and processes.
86
Table 3.1. Comparison of mean species richness for each site along the invasion chronosequence. Means are compared within each stratum (i.e., row) via Bonferroni post hoc tests, and significant differences (P < 0.05) are indicated by lowercase letters. For comparison of non-invaded versus long-invaded sites, percent differences are indicated (last column).
Stratum Mean (± SE) species richness for chronosequence % difference levels between invaded and non-invaded
Non-invaded Recent-invaded Long-invaded sites Seed bank 14.50 ± 1.25 a 10.66 ± 1.05 a 8.50 ± 1.38 b - 41.4% Herb 19.00 ± 1.87 a 23.50 ± 1.23 a 18.16 ± 0.99 a - 4.4% Seedling 11.00 ± 1.57 a 9.83 ± 1.31 b 7.16 ± 1.01 b - 34.9% Sapling 8.75 ± 1.18 a 7.16 ± 1.30 a 3.66 ± 1.03 b - 58.2% Overstory tree 28.50 ± 3.05 a 30.75 ± 3.50 a 32.50 ± 2.75 a + 1.4%
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Table 3.2. Indicator species results for invasion chronosequence levels within forest strata. Indicator value (IV) is given for each invasion level as well as overall IV. Only statistically significant species with IVs ≥ 5.0 are listed. Non-native species are indicated with an asterisk. IVs denote the relative level of species occurrence per group ranging from 100 (perfect indication) to 0 (no indication).
Stratum Invasion level IV Species Non Recent Long Seedlings Acer saccharum 19 6 2 19.1 Prunus serotina 15 1 0 14.8 Cercis canadensis 0 6 0 6.0 Rosa multiflora* 0 5 2 5.1 Saplings & shrubs Acer saccharum 18 6 2 17.8 Fraxinus americana 6 17 0 16.7 Prunus serotina 14 0 0 14.1 Ulmus americana 14 0 0 13.5 Lindera benzoin 6 0 0 6.2 Cornus racemosa 0 5 0 5.1 Herbs & vines Geum vernuum 7 6 31 31.2 Parthenocissis quinquefolia 5 8 19 19.5 Cryptotaenia canadensis 0 17 1 17.3 Eupatorium rugosum 17 3 5 16.8 Vitis spp. 14 1 6 14.4 Toxicodendron radicans 3 14 2 13.8 Sanicula canadensis 0 0 13 13.4 Carex vulpinoidea 0 11 0 11.3 Alliaria petiolata* 2 8 11 10.6 Impatiens pallida 0 10 2 10.4 Glechoma hederacea 10 1 0 10.1 Osmorhiza claytonii 0 8 3 7.6 Smilacina racemosa 6 0 0 6.2 Polygonum pennsylvanicum 3 6 1 6.0 Erigeron philadelphicus 0 5 0 5.5 Poa sylvestris 1 5 0 5.1 Seed bank Juncus tenuis var. tenuis 4 15 1 14.6 Oxalis stricta 14 3 2 14.1 Pilea pumila 0 1 13 12.6 Molluga verticillata* 0 8 0 8.1 Leucospora multifida 0 0 7 7.4 Carex blanda 5 0 0 5.3 Muhlenbergia schreberi 5 0 0 5.3 Stellaria media* 5 0 0 5.0 Mean 5.7 5.2 3.6 11.4
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Figure 3.1. Beta-flexible cluster analysis for each stratum using Bray-Curtis similarities (β = -0.2). Open squares indicate non-invaded sites. Half-open squares indicate recently-invaded sites. Solid squares indicate long-invaded sites.
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Figure 3.2. Regressions of within-stratum changes in density with increasing L. maackii age. Individual points indicated site means.
90
Figure 3.3. Cross strata congruence analysis using Bray-Curtis similarities and Mantel tests. 1
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Chapter 4: Modeling growth and resource allocation of seedlings of an invasive shrub, Amur honeysuckle (Lonicera maackii), in varying light, water, and soil conditions
Introduction
The purpose of our study was to investigate the influence of interacting levels of light, water, and soil conditions on the overall growth and biomass allocation of seedlings of an invasive shrub, Amur honeysuckle (Lonicera maackii). This non-indigenous shrub has invaded at least 26 states in the eastern U.S. (Hutchinson & Vankat; 1997) and successfully established in a wide variety of habitats including open fields, disturbed forests, and intact forest interiors (Luken; 1990b). Frequently, L. maackii forms dense thickets, reduces community diversity, and reduces herb and tree seedling survival
(Hutchinson & Vankat; 1997; Doersam & Gorchov; 1998; Collier et al.; 2002; Gorchov
& Trisel; 2003). Most empirical studies have focused on two portions of its life cycle: seed germination (Luken & Goessling; 1995; Hidayati et al.; 2000) and adult ecology
(Luken; 1993, ; 1995a, 1995b, ; 1997b, 1997a). No study, however, has investigated the response of L. maackii to multiple environmental cues in the critical seedling establishment phase. This information will be useful in gauging L. maackii’s relative success in different types of habitats as well as understand its within-plant response to heterogeneous abiotic conditions.
The influence of resource availability on the growth dynamics of terrestrial plants has been extensively investigated, and responses to environmental heterogeneity include
92 morphological plasticity such as changes in productivity, resource allocation, and overall changes in architecture (Partridge & Harvey; 1988). Growth allocation is a result of asymmetric partitioning to organs due to environmental cues, and this dynamic plant response has been shown to be environmentally adaptive in particular stages of plants’ life histories (Scheiner; 1993a).
Intra-plant response to resource variation can be quite difficult to predict due to the fact that plants respond in a complex fashion to multiple, interacting cues. These include sub-optimal or excessive resource levels, which a plant may or may not be able to accommodate. Plant reactions to a change in one resource may be constrained by the lack of another resource, or plants may respond in a constitutive fashion. Three of the most obvious limiting resources to plants are light, water, and nutrients. Significant variation exists in how resources can affect plant response, plus reactions depend on whether other resources are held constant or varied. Thus an ongoing set of theories is being revised regarding how plants respond to various levels of resources. The tradeoff hypothesis by
Smith and Huston (1989) predict that individuals grown in shady environments are more negatively affected by drought because greater allocation is put toward shoots versus roots; therefore, plants in low light may be unable to effectively deal with drought. The primary-limitation hypothesis by Canham et al. (1996) states the opposite in that water- stress is less influential in low light because in deep shade, water is less of a limiting growth factor. A third hypothesis by Holmgren (2000) called the above-ground facilitation hypothesis has the same effect but states that positive effects of shading under dry conditions occur because there is less physiological stress than in intense light. A
93 fourth, interplay hypothesis, predicts plant growth to be most facilitated at intermediate light levels, with drought having its most negative effects at deep shade and high light
(Holmgren & Scheffer; 1997). Finally, the independent-effects hypothesis by Sack and
Grubb (2002) predicts that drought reduces growth independent of light levels; therefore, plants respond to these resources orthogonally. These responses may occur at the whole plant or at the modular level.
Relative to other physiognomic types, shrubs may be more plastic in their ability to tolerate extreme environmental stress because they frequently are able to colonize open sites plus tolerate deep shade when trees surpass them in height growth at later stages of succession (Newton & Goodin; 1989). Furthermore, invasive plants stereotypically have been thought of as having a number of traits which facilitate their establishment and success including rapid seedling growth, short period in the vegetative condition, and large ability to adapt to various climatic and edaphic conditions (Baker; 1965). Some progress has been made regarding the prediction of invasive species’ success based on traits (Rejmanek; 2000; Marco et al.; 2002); however, to a large extent, prediction is still regarded as quite uncertain because reactions are based on species-specific performance at sites (Ruiz & Carlton; 2003). As a major focus of this study, we were interested in studying L. maackii response at the seedling stage because knowledge of plant response at this stage is particularly important to understanding establishment patterns and species distributions ( Keever; 1950; Grubb; 1977; Woitke & Dietz; 2002).
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A number of studies have been conducted assessing the performance of seedlings in response to combinatorial light, water, and/or soil treatments. Investigations include experiments conducted using different light treatments during natural drought (Sipe &
Bazzaz; 1995; Veenendaal et al.; 1996); however, contradictory results were reported, perhaps because watering conditions were not experimentally manipulated. A few controlled studies have been conducted using careful manipulation of water and light
(Van Hees; 1997; Baruch et al.; 2000; Holmgren; 2000), but most multi-resource studies involve manipulation of only nutrients and light, most likely because water is difficult to control in a natural setting. Therefore, to our knowledge, the interplay of the factors of water, light, and soil type has not been explored for invasive plants using a carefully controlled, fully-crossed factorial experiment.
Specifically the goals of our experiment were to: (1) investigate the influence of light, water, and soil type on growth of L. maackii seedlings over two seasons, (2) measure within-plant biomass partitioning among treatments, and (3) assess whole plant and organ-specific performance tradeoffs among light, water, and soil treatment combinations.
Methods
Seeds were collected from a single L. maackii population during fall 2000
(supplied by Sheffield's Seed Co., Inc., Locke, NY). We applied seed treatment methods of Hidayati et al. (2000) with dark stratification of seeds in moist sand at 5 °C for 12 weeks. Following stratification, seeds were sown on 1-Feb-2001 into a 50-50 perlite-
95 vermiculite mix, watered every 1-2 days, and grown in ambient sunlight for 30 days in a greenhouse in flats with cells measuring 3 × 3 × 6 cm. Seeds germinated roughly 30 days following sowing and were grown for an additional 30 days until seedlings were transplanted on 1-Apr-2001 into field soil in 3.3 L pots where they remained for the duration of the project. Upon planting in field soil, seedlings were sorted according to height-class groups. Equal numbers from each group were randomly assigned into each treatment combination; therefore, no significant height differences were present prior to treatment implementation (P = 0.94).
Soil type was chosen as a factor, because relative to unglaciated regions, L. maackii appears to be especially well established in glaciated regions of Ohio (pers. obs.).
Soil was collected from two field sites, one glaciated and one unglaciated (Dayton, Ohio;
39.653 ºN, 84.232 ºW and Athens, Ohio; 39.408 ºN, 82.087 ºW, respectively) to maintain soil microbial activity. Each site was adjacent to but not within an area invaded by L. maackii and was therefore likely invasible, yet not affected, by L. maackii. At each location, soil from the A-horizon was collected from 10 plot locations separated by 10 m along a transect. Glaciated and unglaciated soil samples (n = 20 total) were analyzed by
Spectrum Analytic, Inc. (Washington Courthouse, OH). Soil from each site was combined then sifted with 1 cm mesh screen to remove debris, large seeds, and herb propagules. To each pot, 1400 grams of soil was added, and pot drainage holes were lined with black, fine mesh chiffon fabric. Lonicera maackii seedlings were grown at the
West State Street Research Site in Athens, Ohio (39.335 ºN, 82.117 ºW).
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Light was chosen as a treatment variable because L. maackii is able to establish in high light open habitats, in moderate levels of light in semi-disturbed sites, and in very low light levels in forest interiors. Furthermore, we used three levels of light: (1) “low,”
(2) “medium,” and (3) “high” light because low as well as high irradiance can be stressful to plants. Differential light treatments were accomplished by using shade towers composed of high density shade cloth (10% ambient light), medium density cloth (50% ambient), or no cloth (100% ambient). Green-hued shade cloth (Green NOW-Knit, PAK
Unlimited, Inc., Cornelia, GA) was cut, sewn into tubes, and suspended on 35W × 75H cm cylindrical wire cages (Figure 4.1). Each seedling in low and medium light treatments had its own shade tower, and all seedling locations were randomized fortnightly. Each seedling was therefore considered its own replicate (see Hurlbert;
1984).
Water level was used as a treatment factor because L. maackii seedlings and adults appear to be especially successful in establishing near waterways and low lying areas (pers. obs). Furthermore Luken & Thieret (1995) reported finding L. maackii growing in its native range in frequently disturbed, forested floodplains.
Moisture levels were maintained by using watering methods similar to those employed by Canham et al. (1996). Two watering regimes, (1) wet and (2) drought, were maintained during the periods of L. maackii leafout (ca. 1-Apr to 1-Nov). Wet treatments were watered to excess (i.e., field capacity) every two weeks, and for the drought treatments, the amount of water (45% water by weight) needed to bring the soil back to
50% field capacity was added every two weeks. Equal watering of all treatments would
97 have confounded the effects of light and water due to variation in transpiration rates among light treatments. This was resolved by determining the mean field capacity weight
(i.e., wet weight) for each soil type, then before each watering period, a random sub- sample within each treatment was weighed in the field using a spring balance to determine the amount of water needed to bring the soil back to 50% field capacity. To exclude rainwater and control soil moisture treatments, all pots were positioned under a
75 m2 roof of clear polyethylene film roof (0.2 mm thick). The roof was well ventilated
at the bottom and sides to facilitate air flow (Figure 4.2). Shade towers also were
constructed with a gap at the bottom to aid ventilation. Temperatures were found to be
not significantly different beneath and outside of the roof (P > 0.10). Because the polyethylene roof resulted in light attenuation, light levels in low, medium, and high light treatments were 4%, 43%, and 84%, respectively. After planting of seedlings, pots were placed 0.75 m apart and sunken into the ground in auger-drilled holes to facilitate natural temperature conditions for roots. Ground holes were lined with empty pots as a barrier to control for soil contamination with existing ground soil. Following leaf-drop and before leaf-out, seedlings were exposed to ambient light levels and precipitation.
Plants were grown for 18 months after planting in field soils beginning 1-May
2001. Height growth, basal area, and leaf numbers were measured monthly during the growing seasons. Half of the plants were harvested each year during the first week of
Oct in 2001 and 2002 during which seedlings were transported for measurement in coolers then divided into leaf, stem, and root portions. Wet leaf mass and leaf area were quantified within 24 hrs, and leaf area was measured using a LI-3100 leaf area meter (Li-
98
Cor Inc., Lincoln, NE, USA). Organ portions were then oven-dried at 105 ºC for 72 hrs.
Also during the final harvest for each year, we measured total and average branch length, number of branches, total leaf area, number of leaves, mean area per leaf, specific leaf area (SLA), leaf area ratio (LAR), total biomass, above- and below-ground biomass, root- shoot ratio, and mass fractions of stem, leaves, and roots. SLA was computed as the total leaf area / wet leaf wt. LAR equaled the total leaf area / total plant wt. Mass fraction of stem, leaf, and roots were computed by dividing that organ’s portion into the total plant mass so that the total mass fraction of each plant was equal to one. Coarse and fine root dry masses were also measured, defined as diameters of > 0.05 mm and ≤ 0.5 mm, respectively (Hendricks et al.; 2000).
Statistical analysis
To measure the effects of light, water, and soil across time, repeated measures
analysis of variance (RANOVA) was conducted with seedling height as the dependent
variable. Data were checked for satisfying the assumptions of normal distribution of
residuals, sphericity, and equal within-subject covariances (Von Ende; 1993). The use of
a single response variable, height, was used because leaf number and stem diameter were
found to be significantly correlated with seedling height (P < 0.05).
Multivariate analysis of variance (MANOVA) was performed to assess the
influence of the fixed factors of year, light, water, and soil type on several dependent
variables expressing L. maackii growth and allocation. Multicollinearity of the data was
repaired by removing significantly correlated dependent variables from analysis, thus
eight response variables were used. We satisfied assumptions of multivariate normality
99 and equality of the variance-covariance matrices via removal of outliers and lognormal transformations (Tabachnick & Fidell; 2001). After the 4-way MANOVA, follow-up
ANOVAs were conducted using significant predictor variables from the MANOVA and the same L. maackii growth and allocation response variables. All statistical tests were performed using NCSS statistical software (Hintze 2000). Tests were considered significant if P < 0.05.
We employed CART analysis to model the relationship between year, light, water, and soil type versus L. maackii growth and biomass allocation. CART analysis was appropriate because it employs binary splitting criteria to create an easy to interpret topology that divides data into progressively more homogeneous categories (Breiman et al.; 1984). Furthermore, CART is an appropriate validation procedure to follow GLM analyses (such as RANOVAs, MANOVAs, and ANOVAs) because GLM procedures rely on assumptions such as eigenvectors being linear combinations of dependent variables, residuals following normal probability distributions, and dependent variables which are uncorrelated, all of which are difficult to satisfy simultaneously (Lausen et al.;
1994).
Strictly speaking, we employed a subset of CART called multivariate regression tree (MRT) analysis because we measured multiple, continuous response variables rather than categorical criteria and used least absolute deviations from multivariate group centroids to develop a model to fit observations (De'ath; 2002). The Gini splitting criterion was used to create regression trees because it frequently provides the most accurate model and works by finding the largest class per node and isolating that class
100 from the rest of the data (Breiman et al.; 1984). Correlated variables were down- weighted in the model if they did not yield predictive improvement by using a geometric reductive case-weight factor relative to their correlation ranking (W = 0.1), and response variables were relativized by converting differently scaled values to a common mean so that they could be effectively compared.
Bootstrap aggregation (i.e., bagging) was also employed in generating the MRT which is a data re-sampling technique. It is often effective in stabilizing regression trees, reducing misclassification error rates, and generally produces more accurate results than classification using a single predictive engine (Breiman et al.; 1984). In bagging, we iteratively created 20 classification trees with a random sub-sample of the data, and precursor trees were averaged to produce the final tree. For each of the iterations, we used 75% of the data, and to avoid overfitting, the tree was pruned by a cross-validation procedure which sets the cumulative gains of misclassification to 1 SE above the minimum test rate (Jang; 1997).
To give an indication of the effectiveness of the MRT model, we estimated the misclassification rate, and to report how the dependent variables were influenced by the dichotomous split, the sum of squares deviation from the multivariate mean at each node was estimated for each dependent variable. This was accomplished by computing the sum of squared Euclidean distances for each response variable from the group mean
(De'ath; 2002). For MRT analyses, we used the CART software program version 5.0
(Steinberg & Colla; 1997).
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Results
For the RANOVA, we found light, water, and light × water to be significant (P ≤
0.02; Table 4.1, Figure 4.2). After two years, plants grown in high light conditions displayed the greatest height growth, and those grown in deep shade exhibited the poorest growth. For the most part, growth rates were consistent within treatments, and growth curves were relatively parallel (Figure 4.2). Despite having considerable stress, seedlings in low light and low water conditions still grew throughout the experiment and had very low mortality. Only 6 out of 196 plants across all treatments exhibited mortality throughout the experiment.
In the MANOVA, year, light, water, and all two-way interactions involving these factors were significant (P ≤ 0.03; Table 4.2). Consistent between both the MANOVA and RANOVAs, soil and all interactions involving soil were not significant (P ≥ 0.45;
Tables 4.2, 4.3). For follow-up ANOVAs, we found that year was significant for all response variables except root-shoot ratio, and light and/or year × light were significant for all response variables (P < 0.001 for all). The water × light interaction was significant for total seedling biomass (P < 0.02), and water and/or year × light interaction was significant for most growth indices (aboveground biomass and total biomass) as well as other composite variables (root-shoot ratio, SLA, SMF, and RMF; P < 0.04; Table
4.4).
Between years, seedlings demonstrated different patterns and levels of responses in terms of growth and allocation in different light and water treatments. For both watering conditions in the deepest shade, seedlings exhibited very poor growth relative to
102 plants in the intermediate and highest light levels (e.g., total biomass, aboveground biomass, leaf area; Figure 4.3). The root-shoot ratio was greatest in drought conditions except in the highest irradiance in the second year. Specific leaf area and LMF demonstrated reductions in both watering treatments with increased irradiance levels.
Interestingly for all light levels, leaves in the first season in high water treatments had greater SLA, while leaves in the low water treatments had greater SLA in the second season. Plants in the first year had a greater root-shoot ratio in low light conditions, but in the second year this ratio was greater in medium and high light conditions (Figure 4.3).
We found that MRT analysis was successful in predicting L. maackii seedling performance. Group creation produced 11 splitting nodes and 12 terminal leaves (Figure
4.4). The tree was divided into two main branches, year one and year two. Within the first year’s branch, light was the most important factor with plants in deep shade performing most poorly. For the first year, the second most important factor was water.
Seedlings in low-water treatments demonstrated wilting during the later portion of nearly every watering cycle and exhibited reduced growth and plasticity across variable light treatments. Plants growing in high irradiance during their first year grew slightly better than plants in medium light, and despite the fact that soil was not significant in previous
GLM analyses, soil type was a predictive factor in MRT analysis (Figure 4.4). Soil in the glaciated area was Miamian clay loam (MiD2), and soil in the unglaciated area was
Westmoreland-Guernsey silt loam (WhC). Soil test results indicate that levels of pH,
Mg%, CEC, and Ca% were greater in glaciated soils relative to unglaciated soils (Table
4.1).
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For the second year’s growth, light was the first splitting criterion which separated deep shade conditions from the other light treatments. Unlike the first year’s
MRT branch, light was a splitting factor twice, and plants in high light conditions performed much better than those in medium light. It appears that L. maackii seedlings responded differently the first and second year with relatively more dependence on high water the first year and more dependence and responsiveness to high irradiance the second year. For both years, the best performance resulted from those seedlings growing in wet soil, high irradiance, and glaciated soil conditions.
MRT analysis predicted seedling performance quite effectively with our model correctly classified 85% of the observations. As a standard practice in MRT analysis, it is important to identify the variables which are most influential in creating node splits; therefore, we looked at the variance explained for each dependent variable at each node
(Figure 4.5). For the first node split which separated the first and second year seedlings, the most important criteria were growth parameters (total leaf area, aboveground biomass, and total biomass). For the second node involving light, strongly reacting criteria were the same growth parameters as in node one as well as light responsive variables such as total leaf area and leaf mass fraction. The split at node three, which separated seedlings growing in either drought or wet conditions, was defined by aboveground biomass, root-shoot ratio, RMF, and SMF. The fourth and fifth nodes involved light and soil type, respectively, and had nearly equal variance explained across parameters with the exception of total leaf area and SLA. Node six and seven involved light, and strongly responding metrics were growth variables (total leaf area,
104 aboveground biomass, total biomass, root-shoot ratio) and light responsive variables
(total leaf area, SLA, and LMF). Node eight involved water, and important variables were above-ground biomass, root-shoot ratio, and RMF. Nodes nine, ten and eleven were patterned similarly, and this equal dispersion of importance across variables suggested that little variability was being explained by any one subset of dependent variables in the lower portion of the regression tree.
Discussion
In the following we discuss the independent and interactive effects of light, water, and soil type on the growth, biomass allocation, and plasticity of L. maackii seedlings and consider the ecological significance of these factors on seedling performance. We also discuss the prediction of L. maackii seedling establishment given our findings and compare and contrast that with other studies involving L. maackii adults. Finally, we consider L. maackii growth in the context of heterogeneous habitats, human and natural disturbances, and consider possible management strategies.
In natural environments, plants respond to spatially and temporally variable resource availability. Light, water, and soil nutrients are the most frequently limiting resources to plants; therefore, knowledge of a particular species’ response to the availability of these resources is important to understanding its distribution (Geiger &
Servaites; 1991). Furthermore, understanding this response is essential because differential reactions among and within plant species is an important part of determining the coexistence and competition of species within particular habitats (Smith & Huston;
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1989). For aggressively invading species in particular, understanding adaptive response to various levels of multiple resources may be critical to predicting their success and establishment in invaded habitats. In landscapes with large natural and human-associated environmental heterogeneity, such as in regions where L. maackii shrubs have invaded, plants must adjust to a multi-dimensional matrix of constantly fluctuating resource spectra. Our experiment, although fairly simplistic in design, has the potential to be predictively powerful as we have carefully controlled resource levels along major gradients that are likely to be important in L. maackii establishment. Furthermore, combinations of these gradients are represented in invaded and potentially invaded habitats; therefore, this information may be able to advance prediction of L. maackii seedling establishment under different conditions (e.g., under various types of topography, successional development, geographic location, and levels of disturbance).
In particular, light access for plants is important because irradiance is the starting point for many morphological and physiological processes as it supplies energy for photosynthesis, transpiration of water, and movement of soil nutrients. Plants are typically light-limited, and in deep shade conditions they often allocate greater growth to
aboveground biomass (Monk; 1966). However, when plants are grown in high irradiance
and have ample supply of other nutrients, they have typical light responses such as
greater overall biomass, total leaf area, total aboveground biomass, relative growth rate,
and LMA (Hughes 1966). Lonicera maackii seedlings exhibited large levels of plasticity
in response to light variation given ample soil moisture. A study of L. maackii adults also
found considerable plastic response such that plants grown in full sun had greater
106 photosynthetic rate, total mass, relative stem growth, leaf thickness, number of leaves, leaf area, and stomatal density relative to plants in medium and light treatments (Luken;
1995b). Also in a comparative study of L. maackii adults with a native co-occurring shrub, Lindera benzoin, Luken et al. (1997b) found that L. maackii equaled or exceeded
L. benzoin in growth and plasticity in low light treatments and greatly exceeded L. benzoin in medium and high light treatments.
In our investigation, we strictly studied morphological variation among treatments; however, physiological adaptation is nearly always accompanied by phenotypic changes. This was found to be the case with adult L. maackii. Lieurance
(2004) reported that L. maackii shrubs grown in open habitats exhibited the greatest photosynthetic rate, quantum efficiency, light compensation point, respiration rate, and nitrogen concentration relative to shrubs in edge and interior plots.
Previous studies clearly demonstrated that large variation in morphological and physiological plasticity in response to light is possible for L. maackii adults; however, these investigations did not control for the concurrent availability of other resources such as water or nutrients. Water is frequently a limiting resource, and its importance cannot be understated. Water is involved in many processes including photosynthesis, expansion of developing organs, leaf turgor, nutrient absorption by roots, and relative to other resources, water is taken up in large quantities (Geiger & Servaites; 1991). Water availability varies widely and is influenced by a number of factors including topography, soil water holding capacity, total amount and timing of precipitation, and evaporation and transpiration processes (Booth et al.; 2003b); therefore, in order for plants to successfully
107 establish and maintain populations at local and regional levels, they must be adaptable to a wide range of moisture conditions which co-vary with the availability of other resources.
In our study, we found that in low light treatments, L. maackii seedlings demonstrated a much reduced plastic response to varying water availabilities, and this was true of nearly all response variables. Sanford et al. (2003) found increased overall growth of both native and alien woody seedlings in dry, open environments compared to forest conditions; however, allocation to various organs in each environment was species dependent. Sack (2004) found for European tree and shrub seedlings that allocation was species specific, but responses to shade and drought were independent (i.e., orthogonal).
Other studies of seedlings involving treatments of light and water availability also found reductions in the ability of plants to respond to different moisture treatments in low light
(Canham et al.; 1996; Van Hees; 1997; Holmgren; 2000).
Although we specifically tested glaciated versus unglaciated soils and not the specific effects of nutrient levels on seedling growth, we did find through MRT analysis greater overall seedling growth in glaciated soils. Other studies regarding specific nutrient resource interactions have found significant plant growth and allocation differences with nutrient variability plus interactions of nutrients with other resources
(Chapin; 1991). We found that pH, Mg%, CEC, and Ca% were greater in glaciated relative to unglaciated soils, which may indicate greater abundance of some soil resources in glaciated areas. Although soil type is spatially heterogeneous according to parent material, hydrology, and a multitude of other abiotic and biotic factors (Kleb &
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Wilson; 1997), our results indicate that L. maackii seedlings establishing in glaciated soils, relative to unglaciated, may have overall greater performance potential; however, a caveat should be made regarding interpretation given our limited sampling of only one site each in glaciated and unglaciated areas.
We found that the most stressful treatment combination for L. maackii seedlings was in drought in deep shade, and this coincides with the hypothesis proposed by Smith
& Huston (1989) in that plants should respond negatively to drought in low light conditions. Therefore our data suggests that L. maackii seedling responses to light and water availability are not orthogonal but instead are co-dependent. Thus our data involving L. maackii seedling performance coincides with the tradeoff hypothesis proposed by Smith & Huston (1989). Evidence of this tradeoff is L. maackii’s lack of production of aboveground biomass in low water-low light conditions. Plants in wet conditions responded with positive aboveground growth with increasing light levels for both years. Despite considerable stress involved with the low water-low light treatment in our experiment, few plants died, and no seedlings exhibited negative growth rates.
Thus, L. maackii seedlings may be demonstrating constrained niche differentiation by being less plastic in low light-low water conditions, they but may still be able to survive until conditions improve.
It would be difficult to distinguish if growth differences were due to overall resource availability or perhaps due to ontogenetic growth variation. Individuals under different stress levels progress though life stages at different rates and thus respond with varying growth and resource allocation (Evans; 1972). Also for woody plants, the relative
109 importance of resources often change over the life span of individuals (Burns & Honkala;
1990). We did find differences in resource importance between the first and second years’ growth in the MRT analysis, as seedlings in the first year had greater importance assigned to water relative to the second year which assigned greater importance to light.
Overall, we found that light intensity was the most important factor explaining seedling growth followed by water. Perhaps at later life stages, L. maackii may be less dependent on water, and this may be due to increased physiological adjustment or perhaps stored reserves. Overall, plastic adjustment may contribute to the survival and reproductive success of the individual given specific abiotic conditions (Huston & Smith;
1987). Furthermore, invasive species may be able to take advantage of quick changes in resources resulting from natural or human-caused disturbances (Claridge & Franklin;
2002).
These results contribute information regarding L. maackii seedling response to specific abiotic resource availabilities and resource interactions. We should caution, however, with regard to extrapolation of these results in ranges beyond our treatment levels and for the prediction of L. maackii performance for resource combinations which we did not examine. For example, changes in soil pH can have very large effects on the availability of nutrients (Chapin; 1991).
Given our results, we agree with a number of previous recommendations regarding L. maackii control. Luken (1993) stated that open habitats as well as forest interiors should be prioritized for L. maackii removal. We also recommend prioritization for L. maackii removal in low-lying areas that have high light availability. In support of
110 this, Medley (1997) and Gayek & Quigley (2001) found that adult Lonicera maackii abundance was positively to be related to the proximity of streams and also to low-lying, convex topography. This corroborates our findings with the importance of water availability to seedling establishment, but we should note that light intensity has been well supported by numerous studies as being the most important factor in L. maackii success. In another study, although water was not controlled, L. maackii seedling distribution was found to be positively related to edge habitat and high light intensity
(Luken & Goessling; 1995). We also recommend alternatives to habitat disturbance which could increase resource levels such as light and nutrients, especially in areas where soil moisture is already abundant (see Huston; 2004). Overall, we found our experiment to be valuable in lending insight into the dynamics of L. maackii seedling growth, resource allocation, life history (Figure 4.6), and prediction of negative influence (Figure
4.7) in different habitat types.
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Table 4.1. Edaphic characteristics for glaciated and unglaciated soil treatments. Daggers indicate ppm and extraction by Mehlich-3 analysis. Asterisks indicates significant differences (P < 0.05) between glaciated and unglaciated soil.
Soil Parameter Glaciated Unglaciated Sand % * 37.2 ± 3.7 24.7 ± 2.7 Silt % * 32.6 ± 2.7 53.2 ± 4.5 Clay % 31.6 ± 3.1 23.5 ± 3.9 pH * 7.1 ± 0.11 6.8 ± 0.8 Organic matter % 4.4 ± 0.4 3.5 ± 0.3 P † 28.2 ± 4.8 23.5 ± 3.2 K † 160.0 ± 12.6 120.3 ± 10.2 Mg † * 109.5 ± 19.9 212.9 ± 20.4 Ca † 1997.2 ± 207.5 2172.3 ± 195.3 CEC* 19.4 ± 3.4 13.3 ± 1.1 K % 1.5 ± 0.3 1.7 ± 0.2 Mg % * 12.9 ± 1.1 11.5 ± 0.8 Ca % * 84.6 ± 3.7 57.4 ± 5.8
Table 4.2. Repeated measures analysis of variance analyzing time, light, water, and soil type.
Factor df SS MS F-value P-value Light 2 148022.8 74011.4 67.30 < 0.000 Water 1 22114.9 22114.9 20.11 < 0.000 Light × Water 2 9445.5 4722.7 4.29 0.017 Soil 1 616.7 616.7 0.56 0.456 Light × Soil 2 254.1 127.0 0.12 0.891 Water × Soil 1 4.8 4.8 0.00 0.948 Light × Water × Soil 2 128.2 64.1 0.06 0.943 Time 86 94569.7 1099.6 0.85 0.828 S 935 1208107.0 1292.1 Total 1032 1491918.0
112
Table 4.3. Four-way multivariate analysis of variance for L. maackii seedling growth.
Factor Wilks’ Lambda df1, df2 F-value P-value Year 0.105 11, 127 97.56 < 0.001 Water 0.841 11, 127 2.17 0.020 Light 0.081 22, 254 28.93 < 0.001 Soil 0.928 11, 127 0.89 0.556 Year × Water 0.825 11, 127 2.44 0.008 Year × Light 0.273 22, 254 10.55 < 0.001 Year × Soil 0.955 11, 127 0.54 0.874 Water × Light 0.785 22, 254 1.48 0.034 Water × Soil 0.903 11, 127 1.23 0.271 Light × Soil 0.820 22, 254 1.20 0.250 Year × Water × Light 0.812 11, 127 1.27 0.194 Year × Water × Soil 0.921 22, 254 0.98 0.464 Year × Light × Soil 0.805 22, 254 1.32 0.158 Water × Light × Soil 0.826 22, 254 1.15 0.291 Year × Water × Light × Soil 0.847 22, 254 1.00 0.466
113
Table 4.4. ANOVA table of significant one- and two-way factor interactions from preceding MANOVA. Significant factors are indicated by underlined P-values.
Year× Year × Water Source Parameter Year Water Light Water Light × Light Total leaf area MS 13.179 1.327 9.543 0.124 0.083 0.195 F-value 196.820 19.820 142.520 1.850 1.240 2.910 P-value < 0.001 < 0.001 < 0.001 0.177 0.292 0.058 Aboveground biomass MS 13.156 0.880 10.793 0.144 0.386 0.114 F-value 336.700 22.520 276.230 3.690 9.880 2.920 P-value < 0.001 < 0.001 < 0.001 0.057 < 0.001 0.057 Total biomass MS 18.560 0.821 12.608 0.203 0.124 0.139 F-value 549.390 24.300 373.220 6.020 3.660 4.100 P-value < 0.001 < 0.001 < 0.001 0.015 0.028 0.019 Root-shoot ratio MS 0.016 0.049 0.114 0.001 0.220 0.005 F-value 2.100 6.550 15.140 0.070 29.340 0.620 P-value 0.149 0.012 < 0.001 0.795 < 0.001 0.538 Specific leaf area MS 0.058 0.002 1.387 0.020 0.006 0.003 F-value 5.04 0.180 121.110 1.750 0.54 0.25 P-value 0.026 0.672 < 0.001 0.187 0.250 0.776 Root mass fraction MS 0.034 0.002 0.003 0.000 0.010 0.000 F-value 75.630 3.880 6.620 0.260 22.160 0.270 P-value < 0.001 0.051 0.002 0.610 < 0.001 0.762 Stem mass fraction MS 0.011 0.001 0.007 0.001 0.008 0.000 F-value 38.610 4.350 26.040 1.910 27.440 0.810 P-value < 0.001 0.039 < 0.001 0.169 < 0.001 0.447 Leaf mass fraction MS 0.104 0.000 0.003 0.002 0.001 0.000 F-value 376.500 1.210 12.460 6.390 1.970 1.590 P-value < 0.001 0.273 < 0.001 0.013 0.143 0.208
114
A. B.
Figure 4.1. Shade towers (A) and waterproof roof (B) used to manipulate irradiance and water availability. Shade towers were used to control irradiance in deep shade (4% full sun) and medium light (43%) treatments. Seedlings in high irradiance (84%) were grown without towers. Waterproof roof was used to shed precipitation allowing controlled watering treatments.
120 84% W G 84% W U 84% D G 100 84% D U 43% W G 43% W U 80 43% D G
height (cm)height 43% D U 4% W G 60 4% W U 4% D G 4% D U 40 Lonicera maackii 20
0 5/01 6/01 7/01 8/01 9/01 10/01 6/02 7/02 8/02 9/02 10/02 Measurement period
Figure 4.2. Lonicera maackii seedling height growth through two growing seasons. Light treatments as proportional irradiance of full sun are indicated by percent values. High watering treatment is indicated by W, and drought is indicated by D. Glaciated soil is indicated by G, and unglaciated soil is indicated by U.
115
Figure 4.3. Treatment means (± SE) for year, light, and water treatments. Soil was not significant in MANOVA or ANOVAs and was therefore excluded from means reporting.
116
A.
Figure 4.4. Prediction tree of L. maackii seedling performance constructed using multivariate regression tree (MRT) analysis. Interior nodes are indicated by hexagons and represent experimental treatments determining splits. Homogeneously performing groups are represented by rectangular terminal leaves. Inside polygons are mean height values and sample size. Greater height within treatment groups is indicated by darker rectangles.
117
B.
Figure 4.5. Bar graph describing dependent variables at each multivariate regression tree node.
118
A. B.
C. D.
A.
Figure 4.6. Lonicera maackii throughout life history stages: (A) zygomatic flowers formed in pairs, (B) seed of L. maackii including morphologically dormant embryo, and (C) adult plant with acuminate, gradually tapering, leaves (D) fruits located in leaf axils.
119
A. B.
C. D.
Figure 4.7. Images of adult L. maackii (A) L. maackii dominating the midstory of forests (B) L. maackii with large fruit production in edge habitat (C) understory beneath L. maackii showing low native plant recruitment and diversity (D) resprouting of L. maackii following no treatment with herbicide.
120
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