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ECOLOGICAL ASSESSMENTS OF IMPACT AND MANAGEMENT OF CORAL ARDISIA (ARDISIA CRENATA), A SHADE TOLERANT INVASIVE SHRUB IN
NORTH CENTRAL FLORIDA
By
GERARDO CELIS AZOFEIFA
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2012
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© 2012 Gerardo Celis Azofeifa
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To my wife Gaby Hernández, my parents Ana and Rafael,
and my siblings Ana and Juanra
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ACKNOWLEDGMENTS
It has been a long process and various institutions and persons have made it
possible, enriching, and foremost enjoyable to its very end. I wish to acknowledge
everyone that has been part of the process. First, Drs. Stephen Humphrey and Thomas
Frazer, directors of the School of Natural Resources and the Environment granted me
most of institutional support to pursue my graduate studies. Second, I would like to
thank my advisor, Dr. Kaoru Kitajima and Co-advisor Dr. Shibu Jose, for their dedication
and support throughout my research. I also wish to express my sincere appreciation of
my committee members, Drs. Michelle Mack, Greg MacDonald, and Wayne Zipperer for
their valuable insights, and of Dr. J. Jack Ewel for support and guidance. I would like to
thank Michael Meisenburg for help finding field sites and for guidance in the
management of exotic invasive plant species. For access to sites and permission to
conduct research, I thank Dr. F. E. (Jack) Putz for the use of his property by the
Newnan’s Lake, Geoffrey Parks and Gainesville City Park and Recreation for sites at
Biven’s Arm and Hogtown Creek, Pam Ganley for Evergreen Cemetery site. Robert
Querns for all his help in the laboratory and greenhouse. The collection of the data
analyzed in Chapter 2 was supported by the Florida Department of Environmental
Protection contract for the period of 2000-2002 to Drs. Alison Fox and Kaoru Kitajima.
The Florida Exotic Pest Plant Council (FLEPPC) funded my herbicide experiment
reported in Chapter 4. The University of Florida Natural Area Teaching Laboratory
funded research in management of exotic species in natural areas. I wish to express my
sincere appreciation to all of these three organizations. Finally, I thank my wife, family
and friends for their unconditional support, without which none of this would have been
possible.
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TABLE OF CONTENTS
page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES ........................................................................................................ 10
LIST OF TERMS ........................................................................................................... 12
ABSTRACT ................................................................................................................... 13
CHAPTER
1 INTRODUCTION .................................................................................................... 15
Invasion of Forest Understories: The Role of Exotic Shrubs and Shade Tolerance ............................................................................................................. 15
Forest Understory Invasion .............................................................................. 15
Shade Tolerant Shrubs of Horticultural Origin .................................................. 16 Colonization, Naturalization and Spread of Shade Tolerant Invaders .............. 17
Overall Objectives of the Study ............................................................................... 18
2 INVASIVE EXOTIC SHRUB, ARDISIA CRENATA, REDUCES NATIVE PLANT DIVERSITY IN FOREST UNDERSTORIES IN FLORIDA ...................................... 20
Materials and Methods............................................................................................ 23 Design .............................................................................................................. 23
Abiotic Environmental Factors .......................................................................... 25 Statistical Analyses .......................................................................................... 26
Results .................................................................................................................... 27 Sites ................................................................................................................. 27 Native Species Richness and Cover ................................................................ 28
Multivariate Association of Native Species Cover ............................................ 29 Discussion .............................................................................................................. 30
3 INFLUENCE OF SHADE TOLERANT INVASIVE SHRUB, ARDISIA CRENATA ON OAK SEEDLING REGENERATION IN MESIC FOREST IN FLORIDA ............ 43
Material and Methods ............................................................................................. 46 Site and Study Design ...................................................................................... 46 Environment Conditions ................................................................................... 48 Oak Seedlings: Planting and Measurements of Growth and Survival .............. 49 Statistical Analyses .......................................................................................... 50
Results .................................................................................................................... 51
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Site Characteristics .......................................................................................... 51
Seedling Survival .............................................................................................. 51 Seedling Biomass ............................................................................................. 52
Discussion .............................................................................................................. 53
4 DOES HERBICIDE TRANSLOCATION CORRELATE WITH SEASONAL CARBOHYDRATE BALANCE IN AN EVERGREEN SHRUB ARDISIA CRENATA? ............................................................................................................. 62
Materials and Methods............................................................................................ 66
Field Experiment .............................................................................................. 66 Herbicide application and efficacy measurements ..................................... 68 Biomass allocation and root carbohydrate storage .................................... 69
Greenhouse Experiments ................................................................................. 70
Statistical Analyses .......................................................................................... 72 Results .................................................................................................................... 74
Field Experiment .............................................................................................. 74 Effects of season and mowing on root sugar and starch concentrations ... 74
Herbicide efficacy in the field ..................................................................... 74 Greenhouse Experiments ................................................................................. 75
Discussion .............................................................................................................. 76
Influence of Herbicide Timing on Efficacy......................................................... 77 Influence of Mowing on Herbicide Efficacy ....................................................... 77
Herbicide Translocation .................................................................................... 78
5 CONCLUSIONS ..................................................................................................... 97
APPENDIX
A ADDITIONAL TABLES AND FIGURES for chapter 2 ............................................. 99
B ADDITIONAL FIGURES FOR CHAPTER 3 .......................................................... 104
C ADDITIONAL FIGURES FOR CHAPTER 4 .......................................................... 110
LIST OF REFERENCES ............................................................................................. 112
BIOGRAPHICAL SKETCH .......................................................................................... 117
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LIST OF TABLES
Table page 2-1 Study site locations and soil characterisctics. ..................................................... 34
2-2 Percent ground cover for each growth form in A. crenata presence and absence. ............................................................................................................. 35
2-3 Eigenvectors of principal component analysis for A. crenata cover, understory native species number, understory native species cover, overstory native species cover, soil moisture, percent light, and diversity. ......... 35
2-4 Eigenvectors of principal component analysis for A. crenata cover, understory native species number, understory native species cover for growth forms, overstory native species cover, soil moisture, percent light. ........ 36
3-1 Study site location, soil characteristics, and A. crenata biomass. ....................... 56
3-2 Logistic mixed model results for seedling survival 240 days after transplanting of the two oak species and two treatments (Ardisia crenata presence and absence). ..................................................................................... 57
3-3 Logistic mixed model results for seedling survival 600 days after transplanting of the two oak species and two treatments (Ardisia crenata presence and absence). ..................................................................................... 57
3-4 Logistic mixed model results for seedling survival 240 days after transplanting of the two oak species and two treatments (Ardisia crenata canopy pull-down and no pull-down). ................................................................. 58
3-5 Logistic mixed model results for seedling survival 600 days after transplanting of the two oak species and two treatments (Ardisia crenata canopy pull-down and no pull-down). ................................................................. 58
3-6 Linear mixed model results for two oak seedling biomass at 600 days after transplanting comparing three treatments (Ardisia crenata absent, A. crenata no pull-down, and initial harvest). ....................................................................... 59
3-7 Linear mixed model results of two oak seedling biomass at 600 days after transplanting comparing three treatments (Ardisia crenata no pull-down, A. crenata pull-down, and initial harvest). ............................................................... 59
4-1 Study site locations. ............................................................................................ 80
4-2 Field experiment biomass and leaf area of harvested Ardisia crenata individuals. .......................................................................................................... 80
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4-3 Greenhouse experiment biomass and leaf area of harvested Ardisia crenata individuals. .......................................................................................................... 81
4-4 Linear mixed model results for root starch concentration of Ardisia crenata in mowed and unmowed fields at herbicide application date. ................................. 81
4-5 Linear mixed model resutls for root simple sugar concentration of Ardisia crenata in mowed and unmowed fields at herbicide application date. ................ 81
4-6 Linear mixed model results for herbicide efficacy index for adult plants after 6 and 12 months following the four herbicide application dates in the mowed and unmowed fields. ........................................................................................... 82
4-7 Linear mixed model results for the herbicide efficacy index for seedlings after 6 and 12 months after the four herbicide application dates in the mowed and unmowed fields. .................................................................................................. 82
4-8 Analysis of variance results for root starch concentration of Ardisia crenata plants grown under low and high light treatments in the greenhouse. ................ 82
4-9 Analysis of variance results for root simple sugar concentration of Ardisia crenata plants grown under low and high light treatments in the greenhouse. ... 83
4-10 Analysis of variance restuls for radioactivity of 14C triclopyr in Ardisia crenata plants grown under low and high light treatments in April 2011. ......................... 84
4-11 Analysis of variance resutls for radioactivity of 14C triclopyr in Ardisia crenata plants grown under low and high light treatments in October 2011. ................... 85
4-12 Radioactivity for leaf water-wash, total absorbed, the treated leaf, and translocation in April 2011. ................................................................................. 86
4-13 Radioactivity for the total recovery and leaf wash in October 2011. ................... 86
4-14 Radioactivity for treated leaf under two light treatments in October 2011. .......... 86
4-15 Analysis of variance resutls for radioactivity of 14C triclopyr translocated to different plant tissues (leaves, stems, roots, meristems) of Ardisia crenata plants grown under low and high light treatments overtime in April 2011. .......... 87
4-16 Radioactivity under two light treatments in leaves, meristems, stems and roots at April 2011. ............................................................................................. 87
4-17 Radioactivity across time in leaves, meristems, stems and roots at April 2011. .................................................................................................................. 88
4-18 Analysis of variance resutls for radioactivity of 14C triclopyr translocated in Ardisia crenata plants grown under low and overtime in October 2011. ............. 88
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4-19 Radioactivity found across time in leaves, meristems, stems and roots at October 2011. ..................................................................................................... 89
A-1 Percent cover of native and exotic species for forest understory and overstory. ............................................................................................................ 99
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LIST OF FIGURES
Figure page 2-1 Experimental design and percent cover of Ardisia crenata. ................................ 37
2-2 Species accumulation curves of forest understory native species. ..................... 38
2-3 Forest understory native species richness in relation to A. crenata cover. ......... 39
2-4 Forest understory native species cover in A. crenata invaded and uninvaded plots. ................................................................................................................... 40
2-5 Principal component analysis correlation biplot for all species. .......................... 41
2-6 Principal component analysis correlation biplot for growth forms. ...................... 42
3-1 Probability of survival for each oak individual seedling at each site for 240 and 600 days census based on generalized linear mixed model........................ 60
3-2 Oak seedling biomass for initial harvest and treatments. ................................... 61
4-1 Schematic of proposed mechanism of carbohydrate movement in a forest understory evergreen plant in relation to seasonal light availability. ................... 90
4-2 Herbicide field experiment setup. ....................................................................... 91
4-3 Field experiment plots with herbicide barrier ...................................................... 92
4-4 Plot photos for October herbicide application (field experiment)......................... 93
4-5 Seasonal total non-structural carbohydrates at each herbicide application date in the field for mowed and unmowed adult A. crenata plants. .................... 94
4-6 Herbicide efficacy after 6 and 12 months after herbicide treatment application date in the field for mowed and unmowed adult A. crenata plants. .................... 95
4-7 Seasonal total non-structural carbohydrates at each herbicide application in the greenhouse for shaded and sun A. crenata plants. ...................................... 96
A-1 Experimental setup for each site. ..................................................................... 103
B-1 Monthly temperatures during study period taken from nearest meteorological station to study sites at Gainesville, Florida, USA. ........................................... 104
B-2 Monthly precipitation during study period taken from nearest meteorological station at Gainesville, Florida, USA. ................................................................. 105
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B-3 Mean monthly temperatures during 27 years (1984 to 2011) at Gainesville, Florida, USA. .................................................................................................... 106
B-4 Monthly precipitation during 27 years (1984 to 2011) at Gainesville, Florida, USA. ................................................................................................................. 107
B-5 Oak seedling biomass for initial and treatments (zeros excluded). ................... 108
B-6 Light availability for plots without Ardisia crenata (Absent), plots with A. crenata canopies pulled down (Pull-down), and plots with A. crenata canopy intact (No Pull-down). ....................................................................................... 109
C-1 Mean monthly temperatures during 27 years (1984 to 2011) at Gainesville, Florida, USA. .................................................................................................... 110
C-2 Monthly precipitation during 27 years (1984 to 2011) at Gainesville, Florida, USA. ................................................................................................................. 111
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LIST OF TERMS
SPECIES A populations of organisms capable of interbreeding and producing fertile offspring.
EXOTIC A species found outside its native range because of human-mediated transportation.
INVASIVE Plant species whose populations expand explosively in new environment, with significant impacts on local species.
PROPAGULE A structure in a plant from which a new individual may rise, such as seeds.
SHRUB Perennial, multi-stemmed woody plant that is usually less than 5 meters (16 feet) in height. Shrubs typically have several stems arising from or near the ground, but may be taller than 5 meters or single-stemmed under certain environmental conditions (USDA).
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
ECOLOGICAL ASSESSMENTS OF IMPACT AND MANAGEMENT OF CORAL ARDISIA (ARDISIA CRENATA), A SHADE TOLERANT INVASIVE SHRUB IN
NORTH CENTRAL FLORIDA
By
Gerardo Celis Azofeifa
December 2012
Chair: Kaoru Kitajima Cochair: Shibu Jose Major: Interdisciplinary Ecology
Undisturbed closed-canopy forests, traditionally thought to be resistant to exotic
plant invasion, are shown to be invadable by certain exotic species, primarily shade
tolerant trees and shrubs. The potential impacts of understory invaders on community
composition, structure, and function of natural forests remain largely unknown. In this
dissertation, I investigated several problems relevant for ecology and management of
the invasion of closed-canopy hardwood hammock forests of north central Florida by
Ardisia crenata, a shade tolerant shrub.
First, I investigated the effects of local abundance of A. crenata and abiotic site
characteristics on the richness and abundance native understory plants across five
mesic forest sites near Gainesville, Florida. In the presence of A. crenata understory
species richness declined by 25% and the total understory cover of native species by
34%, affecting all growth forms (trees, shrub, vines, and herbs).
Next, I conducted a manipulative field experiment to evaluate the competitive
impacts of A. crenata on survival and growth of transplanted seedlings of Quercus
virginiana and Q. hemisphaerica in the understory of four forest sites around
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Gainesville. Seedling survival and growth decreased in the presence of A. crenata over
two growing seasons, and the experimental reduction of aboveground competition from
A. crenata increased light availability and seedling survival.
In the last set of field and greenhouse experiments, I investigated ecological and
physiological factors that potentially affected the efficacy of triclopyr, a herbicide widely
used for foliar-application to control A. crenata. In the field, I examined root
carbohydrate dynamics and efficacy of herbicides as a function of growing season and
mowing. I found that herbicide application was effective in growing season regardless of
mowing. However, removal of seed sources that occurred with mowing was important
for prevention of rapid population recovery. Greenhouse experiments with radio-labeled
triclopyr herbicide showed that a the small amount of herbicide was absorbed, but a
high proportion was translocated to the roots.
In conclusion, my studies support a view that A. crenata has a negative impact on
native plants including tree seedlings in the forest understory by competitively reducing
light availability. The use of triclopyr herbicide for control is recommended during warm
summer months.
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CHAPTER 1 INTRODUCTION
Invasion of Forest Understories: The Role of Exotic Shrubs and Shade Tolerance
Forest Understory Invasion
Invasion by exotic (non-native) plant species has become a growing
concern worldwide in recent decades. Invasions occur in a wide range of
terrestrial and aquatic ecosystems around the globe. The process of invasion
requires an exotic species to disperse to adequate habitats, establish and persist
in the new community (Catford, Jansson, & Nilsson 2009). Humans often
facilitate dispersal in particular through horticultural and agricultural trades. The
process of establishment is also facilitated by changes (enrichment or release) of
resources in disturbed ecosystem (Davis, Grime, & Thompson 2000). Human-
induced disturbances, both those analogous to natural disturbances and novel
types, are becoming more prevalent (Vitousek et al. 1997) especially where
exotic species propagule pressures are high (Vilà & Ibáñez 2011), leading to
increased cases and extents of invasions by exotic species (Bradley & Mustard
2006). In general, disturbance is required by many exotic species that are pre-
adapted to disturbance and/or high resource conditions for colonization and
population growth (Sax & Brown 2000).
Because of the disturbance-dependent life history of many invasive exotics,
undisturbed systems are considered to be less vulnerable to invasion. Some
consider that undisturbed ecosystems, especially species-rich systems such as
tropical forests, are resistant to invasion (Elton 1958), because in intact species-
rich systems, no empty niches are available for alien species to colonize.
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However, the growing evidence suggests the contrary. Once abundant, exotic
species impacts can affect community structure and ecosystem functions (Vilà et
al. 2011; Pyšek et al. 2012). The impact of invasive exotics on species diversity
may be dependent on spatial scales; there is a negative association between
native and exotic species richness at small spatial scales, whereas at large
scales there is a positive association (Fridley et al. 2007).
Shade Tolerant Shrubs of Horticultural Origin
Mature forested ecosystems with closed canopies are a good example of
an undisturbed ecosystem, where resources such as light are a limiting factor for
plant growth and establishment. Many of the species invading these ecosystems
still require natural disturbances such as tree fall gaps to establish and then can
continue to survive after canopy closure (Gorchov et al. 2011). These
requirements are similar to the life history traits of many resident trees and
shrubs (Richardson & Rejmánek 2011). However, there may be a group of
species that do not require disturbances to establish and persist in forest
understories. This group is dominated by shade tolerant shrubs (Martin, Canham,
& Marks 2008).
Human mediated transport is the mechanism of exotic species movement
around the world, including shrubs. The horticulture industry has played an
important role in such transport; 31% of all exotic invasive shrubs in the world
were introduced by horticulture (Richardson & Rejmánek 2011). In regions with
the presence of high numbers of exotic invasive tree and shrub species (more
than 100 exotic invasive species) such as North America, 77% of all invaders
were introduced by the horticulture industry (Richardson & Rejmánek 2011). In
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an effort to limit introductions of potentially invasive plants, the State of Florida
has regulations restricting the introduction of exotic species shown to be
detrimental (FLEPPC 2011).
Ardisia crenata Sims. (Myrsinaceae) represents a clear example of shade
tolerant invasive exotic shrubs of horticultural origin. A. crenata was introduced
and promoted by the horticulture industry as an ornamental (Wirth, Davis, &
Wilson 2004). Photosynthetic light response curves of A. crenata exhibits a low
light compensation point ~6 μmol m-2 s-1 which allows it to persist in shade
(Gerardo Celis, unpublished data). A. crenata is capable of forming dense
monodominant patches (Burks & Langeland 1998) with cover reaching >90% of
ground (personal observation) and 300 stems per m2 (Kitajima et al. 2006).
Dispersal is limited, but birds are the main dispersers (Meisenburg 2007). It is
native to east Asia (mainly from southern China to southern Japan) and genetic
analyses suggest that A. crenata came to Florida from southeastern China
multiple times and then spread from there (Niu et al. 2012), but horticultural
trades somewhat obscure this pattern (Dozier 1999; Kitajima et al. 2006).
Colonization, Naturalization and Spread of Shade Tolerant Invaders
Light availability under closed forest canopies is low (9% of incident
radiation in southern hardwood forests; Canham et al. 1990), and it constrains
growth and survival of many plants including seedlings of overstory species.
Survival in shade depends on morphological, physiological and genetic
characteristics that contribute to maintenance of positive carbon balance. Such
characteristics include not just optimization of shade light utilization (Chazdon &
Field 1987; Lusk et al. 2011), but also defensive traits against herbivores and
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pathogens (Kitajima 1994; Alvarez-Clare & Kitajima 2007) and storage that allow
survival during periods of negative carbon balance (Poorter & Kitajima 2007;
Myers & Kitajima 2007). In case of exotic invaders, also important are the traits
that allow them to compete with native species (Keane & Crawley 2002). For
instance, garlic mustard Alliaria petiolata, an understory exotic invasive forb
exhibits low degrees of herbivory (Ricklefs 2010).
Success of exotic invasive species is often attributed to escape from natural
enemies, but success may also be the result of successful acquisition of
resources including light. Woody exotic species displayed traits significantly more
conductive of resource acquisition than native species (higher specific leaf area,
larger and thinner leaves, lower wood density) (Tecco et al. 2010). An alternative
hypothesis by which understory exotic species successfully invade is that they
reduce resources available to competitors. The exotic shrub, Lonicera maackii
invading forests in eastern United States has shown to reduce the amount of
understory light available to other species and therefore facilitate its own invasion
by competitive suppression (Miller & Gorchov 2004).
Overall Objectives of the Study
Given the importance and the potential impacts of shade tolerant exotic
invasion on forest understories, an integral approach that considers ecological
and life history characteristics of these types of invaders is needed for effective
management. The process in search of such an approach should include 1)
identification of the impacts of an exotic species on ecosystems, 2)
understanding of the mechanisms by which exotic species produce impacts, 3)
identification of the best control methods to reduce impacts and 4) evaluation of
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the economic costs and benefit (Epanchin-Niell & Hastings 2010) and public
willingness to address control methods (García-Llorente et al. 2008). This
dissertation is an effort toward development of such an integral approach.
Chapter 2 assesses the impacts of a shade tolerant exotic invasive shrub, A.
crenata, in the understory community of a closed canopy forest; how the native
understory species richness and cover are associated with the local abundance
of A. crenata when abiotic environmental variables such as soil moisture and
light availability are simultaneously considered. I also ask how these associations
may differ among plant growth forms. In chapter 3, I evaluate one of the potential
mechanisms by which A. crenata displaces native species, the impact of light
competition from A. crenata on survival and growth of seedlings of two common
canopy tree species, when the influence of microenvironmental variations are
considered simultaneously. Chapter 4 evaluates factors that influence efficacy of
herbicide control of A. crenata; exploring the effects of mowing on herbicide
efficacy and the impact of seasonal variation on herbicide translocation.
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CHAPTER 2 INVASIVE EXOTIC SHRUB, ARDISIA CRENATA, REDUCES NATIVE PLANT
DIVERSITY IN FOREST UNDERSTORIES IN FLORIDA
Exotic plant invasions occur in a wide range of terrestrial and aquatic
ecosystems around the globe. The process of invasion requires exotics species
to disperse to adequate habitats, establish and persist in the new community.
Invasion by exotic species is generally linked to disturbance-associated
resources pulses in the ecosystem (Davis et al. 2000). Disturbances can be
natural or anthropogenic, and can vary in magnitude, ranging from branch and
tree falls to large blow downs by hurricanes in forested ecosystems. However,
anthropogenic disturbances are becoming more prevalent (Vitousek et al. 1997),
especially where exotic species propagule pressures are high (Vilà & Ibáñez
2011), and consequently are particularly conductive to exotic invasion (Bradley &
Mustard 2006). While disturbance-associated resource fluctuations are important
in facilitating colonization and initial population growth by exotic species adapted
to these changes (Sax & Brown 2000), once established, such species are
shown to alter community structure and ecosystem functions to further promote
their population growth (Vilà et al. 2011; Pyšek et al. 2012).
On the other hand, undisturbed ecosystems, in particular species rich
ecosystems, are considered to be more resistant to invasion by exotic species
(Levine, Adler, & Yelenik 2004). The basis of this view is that all potential
ecological niches are occupied by species already present in the community, and
resource competition among them results in resistance against invasion by exotic
species (Elton 1958). The understories of closed-canopy forests, where
resources such as light are a limiting factor for plant growth and establishment,
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are often viewed as relatively undisturbed and invasion-resistant. Many of the
species invading the forest understory still require natural disturbances such as
tree fall gaps to establish, even though they may continue to survive after canopy
closure (Gorchov et al. 2011). These requirements are similar to the life history
traits of many resident trees and shrubs (Richardson & Rejmánek 2011).
However, there may be a group of invaders that do not require disturbances to
establish and persist in forest understories. This group is dominated by shade
tolerant shrubs (Martin et al. 2008). Their impacts to understory community as
well as the recruitment of overstory species need to be evaluated.
In this study we used Florida’s hardwood hammocks forests to explore the
invasion of shade tolerant invasive species. Hardwood hammocks are
characterized by multiple layers of trees, shrubs and herbs, dominated by a
dense canopy consisting of a mix of evergreen and deciduous trees (Veno 1976).
They can be further classified by the degree of water availability (xeric, mesic,
and hydric) (Vince, Humphrey, & Simons 1989). The north central Florida
hardwood hammocks are in a transitional zone from the southern mixed
hardwood forests to the tropical forests of southern Florida (Platt & Schwartz
1990). Dominant species are broad-leaved evergreen (e.g., Quercus virginiana
and Magnolia grandiflora), needle-leaved evergreens (e.g., Pinus glabra and P.
taeda), and deciduous hardwoods (e.g., Liquidambar styraciflua and Carya
glabra).
Florida has a long history of exotic species introductions (Gordon & Thomas
1997) and about 1,400 species currently form part of the resident flora.
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Approximately 11% have become invasive (FLEPPC 2011) and threaten many of
Florida’s natural communities. Despite having high species diversity (Monk
1965), hardwood hammocks are being invaded by exotic trees (e.g.,
Cinnamomum camphora), vines (e.g., Discorea bulbifera), herbs (e.g.,
Tradescantia fluminensis), and shrubs including Ardisia crenata (Burks &
Langeland 1998). A. crenata is a shade tolerant shrub, which can grow and
reproduce under very low light conditions (Kitajima et al. 2006).
In spite of growing recognition of the potential impacts of forest understory
invasion by shade tolerant shrubs, quantitative assessments of their impacts are
rare compared to invaders of other types of habitats. In this study, we quantified
how A. crenata may affect diversity, richness and structure of native plant
communities in the understory of hardwood hammocks. More specifically, we
addressed the following three questions:
1. How are understory native species richness and cover associated with presence and abundance of A. crenata?
2. How are forest understory species richness and cover, as well as A. crenata cover, associated with abiotic environmental variables such as soil moisture and light availability?
3. Are these associations similar regardless of plant growth form?
We predicted a negative association between A. crenata abundance and
native understory species richness and cover. This negative association is
expected to be stronger with native trees and shrubs growth forms than herbs
and vines, because similar life-forms with similar resource requirements may
compete more with each other.
23
Materials and Methods
Exotic invasive plant species impacts on ecosystems are sometimes
difficult to quantify due to the lack of prior knowledge of the state of the
ecosystem before plant invasion. Plant invasion usually occurs from a focal point
and then begins to spread to peripheral areas. The spread will be determined by
dispersal capacity of the species into new areas. A. crenata fruits are rarely
dispersed and they can persist up to a year on the plant (Meisenburg 2007). The
limited dispersal results in high concentration of seedlings (~ 600 individuals per
square meter) can be found under adult plants (Kitajima et al. 2006) and slow
spatial spread. Personal observations of heavily invaded sites around Gainesville
over multiple years has witnessed areas adjacent to the focal points of A. crenata
invasion under the similar environmental conditions became invaded overtime.
Design
We selected five mesic hardwood forest sites near Gainesville, Florida,
where dense patches of A. crenata appeared to be actively expanding (i.e., many
large reproductive adults surrounded by smaller individuals at the periphery). All
sites were relatively undisturbed forests dominated by broadleaf evergreen and
deciduous canopy trees, such as Quercus spp. Two sites were within protected
natural areas (San Felasco State Forest (SF), Coclough Pond Nature Park (NL));
while others were private lands adjacent to public natural areas (Micanopy (MC),
Newnan’s Lake (NL), and Payne’s Prairie (PR); see Table 2-1). In
communication with the landowners and land managers of these sites, we
ensured that there were no active removal efforts before the end of 2001 when
this study was completed. At each site, we located a dense patch of A. crenata,
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and approximated the position of the central invasion point according to the
presence of large-sized reproductive adults of A. crenata (e.g., multi-stemmed
individuals > 1 m in height with fruits). This position was marked with a rebar for
the duration of the study.
A. crenata stem density and native plant cover within and around each
patch were estimated in four 1-m wide transects radiating from the central
invasion point in a stratified randomized manner; one transect radiated from the
center point in a randomly chosen compass direction within each 90o quadrant
(0-90, 90-180, 180-270, and 270- 360o). Within each transect, we recorded
presence of A. crenata individuals greater than 20 cm in height at every 1 m
segment. A. crenata may be potentially reproductive above 20 cm in height
(Kitajima et al. 2006), and this minimum size threshold also ensured consistent
detection threshold because smaller individuals can be easily overlooked. Each
transect was extended 10 m beyond the distance at which the last A. crenata
individual was observed (e.g., if the last A. crenata was observed at 16 m from
the central point along a particular transect, the length of this transect was 26 m
including 10 m in which no A. crenata occurred). The size of the invaded area
was a polygon defined by this location of the last A. crenata along the four
radiating transects (Figure 2-1), while area beyond this zone was considered to
be in the “uninvaded” area. If the transect ran into a road, pond, or water body
was also terminated.
We chose five random distances within the invaded area and three in the
uninvaded area to set 1 m x 1 m plots along each transect. In each plot, we
25
quantified the density and percent cover of A. crenata, and recorded the identity,
percent cover of all native plant species in the understory (below 0.8 meters
above the ground, within 1 m x 1 m PVC pipe frame) and overstory (by visual
approximation). Other exotic understory species were accounted for in each plot
(a total of 8 species across sites, with the average cover of 1.7%), but were
excluded from statistical analysis. Also recorded was the percent cover of the
bare ground if present. The survey was repeated in spring (April) and fall
(August) of 2001, so that both early-emerging and late-emerging species could
be accounted for. A total of 99 species were recorded across the four sites,
including six species that were encountered only in the fall survey (Appendix A,
Table A-1). The percent cover in the overstory, including all vegetation above 0.8
meters height, was approximated in the same manner, often resulting in greater
than 100 percent cover due to overlapping foliage.
Abiotic Environmental Factors
Soil was sampled with a 2.5 cm diameter x 20 cm deep corer after
removing litter layer. Two cores were collected from each transect, one randomly
chosen plot inside and another from the uninvaded area. Thus, the total number
of soil cores per site was eight. The soil was brought back to the lab to
determine gravimetric soil moisture after drying to a constant mass at 105°C. Soil
moisture (volumetric) was also measured in all plots with a soil moisture probe
(Theta probe type ML1, Delta-T devices, Cambridge, England), calibrated
against gravimetric soil moistures of sample cores from the same locations. Soil
was sampled once within two days in May. No rain events occurred in the area
during four days prior to the measurements. Four replicate measurements were
26
taken within each plot to account for spatial heterogeneity, and the plot mean
moisture was used for statistical analysis. Dried core samples were homogenized
and analyzed for nutrient contents in the Analytical Research Laboratory of the
University of Florida. Mehlich-1 extraction was made from each subsample of 5
g, for which phosphorus (P), potassium (K), calcium (Ca) and Magnesium (Mg)
concentrations were determined with the ICP method following EPA Method
200.7. Total organic matter content was estimated with the Walkley Black (WB)
method.
Light (photosynthetic active radiation, PAR) was measured at 0.8 meters
above the ground (above A. crenata canopy) with a line quantum sensor (LI-COR
Inc., Nebraska, USA) once during Spring season, and expressed relative to the
reference PAR taken simultaneously with a quantum sensor and data logger in
the nearest site under 100% open sky.
Statistical Analyses
All statistical analyses were conducted using R (R Development Core Team
2012) and used a significance level of P = 0.05. A linear mixed model was used
to test differences of soil characteristics between invaded and uninvaded areas.
The model included invaded status (in and out) as fixed effect and site as a
random effect. Variables evaluated were soil moisture and nutrients and were
transformed (natural log for nutrients) to satisfy the normality assumption. A
Tukey post hoc test was used to identify differences among levels.
A. crenata cover was compared among sites with a non-parametric tests of
Kruskal-Wallis, and a Nemenyi-Damico-Wolfe-Dunn post hoc test (Hollander &
Wolfe 1999) was implemented to test differences between sites. Species
27
accumulation curves were estimated to compare species richness between plots
with A. crenata presence and absence using R package vegan (Oksanen et al.
2012) which calculates mean and standard deviation from random permutations
of the data. A Shannon-Weiner index (Diversity) was calculated for each
individual plot. A generalized linear mixed model assuming Poisson distribution
and treating site as a random factor was used to evaluate the relationship of
native species richness and A. crenata cover. In order to account for site
variability, site was set as a random effect.
In order to summarize how native species cover, richness and diversity
were associated with soil moisture, percent light, overstory cover and A. crenata
cover, we used a principal component analysis (PCA). The Kraiser-Guttman
criterion was used to determine significance of eigenvalues. Further, PCA was
run after separating native species richness by growth forms (Tree, Herb Vine
and Shrub) to examine which of these life forms may show stronger negative
association with A. crenata abundance. Light and soil moisture were log
transformed to approximate normality.
Results
Sites
The “A. crenata-invaded” area of the polygon differed substantially among
the five sites; BM = 550.3 m2, CP = 510.7 m2, NL = 3,084.8 m2, PR = 133.2 m2,
SF = 989.7 m2 (Appendix A Figure A-1). The five sites also differed in soil
nutrients; phosphorus (F4,34 = 17.1, P < 0.001), potassium (F4,34 = 4.9, P =
0.001), magnesium (F4,33 = 5.8, p = 0.008) and calcium (F4,34 = 4.2, P = 0.006).
Organic matter did not differ (F4,34 = 0.5, P = 0.77), with Newnan’s Lake (NL)
28
being the least fertile among the four (Table 2-1). Soil moisture was the lowest at
Payne’s Prairie (PR), whereas other three sites had similar levels (F4,152 = 21.2, P
< 0.001) (Table 2-1). A. crenata cover significantly differed among the five sites
( = 20.8, P<0.001), primarily due to much higher cover at Newnan’s Lake (NL)
compared to other three sites (Figure 2-1). Among abiotic environmental factors
measured, only soil moisture was different between A. crenata invaded plots with
uninvaded plots (F1,151, P = 0.03), uninvaded plots having higher soil moisture.
Native Species Richness and Cover
Native understory species richness in presence of A. crenata was 25%
lower (61 species, 84 plots) than where A. crenata was absent (81 species, 73
plots) (Figure 2-2). Based on the species accumulation curves, sampling effort
was sufficient to detect differences between plots with A. crenata presence and
absence. A minimum of 33 m2 of sampled area or 33 plots was required to detect
differences. The mean species richness for forest understory species was
compared among sites at a comparable sample area, and it was highest at San
Felasco State Preserve (SF) 49 species (32 m2 sampled area), followed by
Colclough Pond (CP) 39 species (32 m2), Micanopy (MC) 37species (32 m2),
Payne’s Prairie (PR) 34 species (32 m2), and Newnan’s Lake (NL) 31 species (29
m2).
Native understory species richness was negatively associated to A. crenata
cover ( = 23.5, P < 0.001), indicating that increases in A. crenata cover
reduced native species richness. This trend was similar among sites (Figure 2-3).
Native understory species cover was reduced by the presence of A.
crenata. Invaded plots had on average 34% less cover (Figure 2-4). However, A.
29
crenata was not merely replacing native species cover, but it increased total
ground cover by reducing bare ground. Overall mean total cover per plot
including A. crenata was 9.3% higher ( = 11.2, P < 0.001) than plots without A.
crenata. All growth forms were negatively associated with the presence of A.
crenata; herbs ( = 5.5, P = 0.02), vines ( = 4.0, P = 0.046), and most notably
for trees ( = 8.6, p = 0.003), and shrubs ( = 10.4, P = 0.001) (Table 2-2).
Multivariate Association of Native Species Cover
The PCA plot shows that native species richness and cover were negatively
associated with A. crenata cover, but were largely independent of soil moisture,
light, and overstory cover (Figure 2-5, Table 2-3). A. crenata cover (Accov) had
the highest loading on principal component 1 (PC1), which accounted for 33% of
the total variation. This strong contribution of A. crenata cover to PCA was not
unexpected given the sampling design that attempted to span a wide variation in
local density of A. crenata. Both richness (UnspNo) and cover (Uncov) of
understory native species showed strong negative loading to PC1, and Shannon-
Weiner diversity index showed weaker negative loading with PC1. By contrast,
overstory cover showed little relationship with PC1. The second principal
component (PC2) accounted for 22% of the total variation, mostly in relation to
abiotic environmental variables of soil and light. Overstory cover (Ovcov) and
moisture (Moist) had negative loadings, while light availability (Light) had positive
loading, indicating greater overstory cover meant lower light availability and
higher soil moisture (Figure 2-5).
30
When understory cover was considered separately by growth forms (tree,
shrub, vine and herb) in PCA (Figure 2-6), PC1 accounted for 22% of the total
variation, again largely explained by A. crenata cover (Accov) with high positive
loading and understory native species richness (UnspNo) and herbaceous cover
(Herb) with large negative loadings (Table 2-4). Shrub cover (Shrub) and Tree
cover (Tree) were also negatively associated with PC1, but less strongly so. PC2
accounted 18% of the total variation, reflecting variations in understory tree
species cover (Tree, positive), light (Light, positive) and Overstory and shrub
cover (Ovcov and Shrub, negative) in the order of factor loading (Table 2-4).
These relationships indicate a difference in light requirements between trees and
shrubs. Moisture was not significantly associated with the first two principal
components.
Discussion
Our findings demonstrate that mesic hardwood hammock forest
understories are not immune to the invasion of shade tolerant exotic shrubs and
their impacts. The presence of Ardisia crenata shrub was negatively associated
with understory community species richness and cover. Overall, native species
richness was reduced by 25% and cover by 34%, indicating that A. crenata
modified understory community structure. In a temperate forest, a shrub,
Lonicera maackii, is shown to reduce species richness by 53% and cover by 63%
on average, with greater reduction with increasing residence time of L. maackii
(Collier, Vankat, & Hughes 2002). We did not know the residence time of A.
crenata at each site, but within and across the five sites, native species richness
was negatively associated with local abundance of A. crenata. Not only was A.
31
crenata decreasing native cover, it also increased total cover by 9.3% making
understories more densely vegetated.
Invasion by exotics may cause a species composition shift in favor of
specific growth forms. A. crenata had a negative effect on all growth forms (trees,
shrubs, vines and herbs). We found that taking into account the influence of A.
crenata, understory cover of tree species was more strongly associated with light
than shrubs. This means that tree species require more light than shrubs for
persistence in the understory (Herault et al. 2011). The shrub and tree understory
dynamics of hardwood forests in the northeast U.S. are negatively associated to
each other (Ehrenfeld 1980), where understory species suppress overstory
species and vice versa. This interaction between shrubs and trees is disrupted by
A. crenata impacting all growth forms in southern hardwood hammocks. Over
time this could lead to a homogenization of species in the understory.
Exotic species impacts are sometimes confounded with changes in
environmental variables, thus exaggerating the impact attributable to exotic
species (Surrette & Brewer 2009). The unaccounted factors can sometimes
result in positive correlations between native and exotic species (Gilbert &
Lechowicz 2005). Alterations in community species composition where exotic
species have invaded can be due to interaction of exotic species and ecosystem
change. This is likely the case of disturbed ecosystems in which exotic ruderal
species invade. However, exotic species can be the direct cause of change in the
species composition by introducing novel traits or functions to an ecosystem
(Bauer 2012). The impacts of A. crenata on native species richness and cover
32
were independent of abiotic microenvironmental variables affecting key
resources in the understory, such as light. A. crenata was not only replacing
native species cover, but also making understories more densely vegetated.
Hardwood hammock forests of Florida have a history of complex
disturbance regimes (fires and hurricanes) of varying scales over space and
time. These have created multilayered structured communities rich in species
and dependent on cyclic disturbance regimes (Platt & Schwartz 1990).
Exploitation of light after disturbances in the understories plays an important role
in growth and survival of individuals, often facilitating population establishment of
invasive exotic plants. Subsequently, forest understory communities can be
affected by changes in the environmental condition created by invading species.
For example, shade cast by Acer negundo reduces native species richness by
45% and aboveground biomass by 81% in riparian forests in SE Europe where
A. negundo is an exotic invasive tree (Bottollier-Curtet et al. 2012). The authors
of this paper consider that the observed changes to light levels were novel to the
system and hence reduction of native species. Similarly, A. crenata has crown
leaf display characteristics that reduce light availability underneath (Kitajima et al.
2006).
The results of this study suggest that A. crenata impacts on hardwood
hammocks community can threaten key processes and functions of this
ecosystem. What are some of the mechanisms that allow A. crenata compete
with understory native species? Further research is needed to understand these
33
mechanisms, which will enable us to quantify impacts and to design effective
mitigation effects. One of such mechanisms will be examined in the next chapter.
34
Table 2-1.Geographical coordinates, soil characteristics and %light relative to a nearby open site (measured at height of 80 cm above ground) of the five study sites in Alachua County, Florida, USA. Means and standard deviations, significant statistical differences between sites or A. crenata invaded zone are identified by different superscript letters. Means (± standard deviation) are shown for each site, as well as for the data pooled across sites for invaded vs. uninvaded plots. Variables were transformed using natural log to satisfy normality.
Site (abbreviation)
Latitude & Longitude
Soil Moisture (m3/m3)
P (mg/kg) K (mg/kg) Ca (mg/kg) Mg (mg/kg) Org. M. (%) Light (%)
Micanopy (MC)
29°35'N, 82°22'W
0.078 (0.018)
370.0 (321.1)a
35.7 (28.7)ab
1395.5 (1341.8)ab
169.6 (191.5)a
1.79 (0.67)a
5.7 (7.9)a
Colclough Pond (CP)
29°37'N, 82°19'W
0.100 (0.020)
283.1 (64.9)a
61.7 (83.5)a
1979.4 (977.3)a
72.8 (58.8)a
1.84 (1.07)a
8.1 (20.2)b
Newnan’s Lake (NL)
29°37'N, 82°12'W
0.062 (0.124)
41.3 (75.4)b
16.8 (18.0)b
1028.9 (2308.3)b
30.8 (47.6)b
1.77 (0.99)a
14.9 (22.5)a
Payne’s Prairie (PR)
29°35'N, 82°21'W
0.053 (0.009)
40.3 (11.6)bc
60.8 (39.4)a
1103.4 (733.7)ab
76.4 (49.1)a
1.55 (0.61)a
7.3 (14.4)ab
San Felasco State Preserve (SF)
29°43'N, 82°27'W
0.076 (0.014)
136.9 (104.0)ac
30.1 (8.6)ab
1225.7 (997.0)ab
64.7 (17.9)a
1.95 (0.36)a
7.6 (9.8)a
Invaded 0.074 (0.023)b
174.3 (166.2)a
43.9 (60.9)a
1370.5 (1116.1)a
72.5 (53.0)a
1.91 (0.78)a
4.8 (6.1)b
Uninvaded 0.103 (0.091)a
180 (226.3)a
40.1 (33.1)a
1343.8 (1474.1)a
95.2 (128.3)a
1.69 (0.73)a
15.3 (23.9)a
35
Table 2-2. Percent ground cover (Mean and range) for each growth form in A. crenata presence and absence plots across the five sites in Alachua County, Florida, USA.
Growth form Absent Present
Tree 10.7 (0.0 – 70.0) 4.2 (0.0 – 50.0)
Vine 5.8 (0.0 – 54.0) 4.0 (0.0 – 30.5)
Shrub 5.4 (0.0 – 60.4) 5.9 (0.0 – 36.0)
Herb 5.9 (0.0 – 27.1) 5.6 (0.0 – 50.8)
Table 2-3. Eigenvectors (factor loading; eigenvector is scaled to the square root of its eigenvalue) of Principal Component Analysis of 157 plots for A. crenata cover (Accov), Understory native species number (UnspNo), Understory native species cover (Uncov), Overstory native species cover (Ovcov), soil moisture (Moist), percent light (Light) and Shannon-Weiner index (Diversity), across five sites in Alachua County, Florida, USA.
PC1 PC2 PC3
A. crenata cover 1.503 -0.612 0.377 Understory native species richness
-1.949 0.154 0.171
Understory native cover -0.770 1.181 -1.347 Overstory native cover -0..560 -1.231 -1.077 Moisture -0.840 -1.098 0.697 Light -0.021 1.626 0.887 Diversity index -1.828 -0.301 0.697 Eigenvalue 2.333 1.536 1.039 Proportion of Variance 0.333 0.219 0.148 Cumulative proportion 0.333 0.552 0.700
36
Table 2-4. Eigenvectors (factor loading) of Component Analysis of 157 plots for A. crenata cover (Accov), Understory native species number (UnspNo), Understory native species cover growth forms; Trees (Tree), Herbaceous plants (Herb), Shrubs (Shrub), Vines (Vine), Overstory native species cover (Ovcov), soil moisture (Moist), percent light (Light), across five sites in Alachua County, Florida, USA.
PC1 PC2 PC3
A. crenata cover 1.538 -0.117 0.288 Understory native species richness
-1.660 -0.178 0.232
Overstory native cover -0.352 1.277 0.735 Moisture -0.595 -0.300 1.550 Light -0.180 -1.10 -1.181 Shrub cover -0.104 1.264 -1.028 Tree cover -0.800 -0.975 -0.424 Vine cover -0.755 -0.502 0.376 Herb cover -1.129 0.948 -0.661 Eigenvalue 1.952 1.604 1.510 Proportion of variance 0.217 0.178 0.169 Cumulative proportion 0.217 0.395 0.564
37
CP MC NL PR SF
−80
−60
−40
−20
0
20
X = 17.7ab X = 10.1
aX = 40.5
bX = 26.6
ab X = 10a
−40 −20 0 20 40 −40 −20 0 20 40 −40 −20 0 20 40 −40 −20 0 20 40 −40 −20 0 20 40
Distance from origin (m)
Dis
tance
fro
m o
rigin
(m
) Percent
cover
0
20
40
60
80
100
Figure 2-1. Plot location at each of the five sites, showing cardinal orientations and length of transects, in Alachua County, Florida, USA. Each dot indicates the location of a plot and its size (and shade of color) indicates percent cover of Ardisia crenata. The first five plots were randomly selected along each transect within the invaded area, but a given plot within the invaded area may not contain any A. crenata individuals due to heterogeneous distribution of individuals.
38
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20
40
60
20 40 60 80Cumulative number of plots
Cu
mu
lative
nu
mbe
r o
f sp
ecie
s
Ardisia
Absent
Present
Figure 2-2. Species accumulation curves of forest understory native species for
plots in the area invaded and uninvaded by A. crenata across five study sites in Alachua County, Florida, USA. Solid line indicates the mean species richness and shaded bands indicate 95% confidence intervals.
39
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MC
NL
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SF
Figure 2-3. Forest understory native species richness in relation to A. crenata
cover at each of the five sites (indicated by different colors) in Alachua County, Florida, USA. Lines are fitted mixed model predictions and light colored shaded area is 95% confidence interval for each site (See Table 2-1 for site codes). Points are native understory species richness for each individual plot. Native species richness was significantly and negatively related to A. crenata cover (X2 = 23.5, P < 0.001).
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Absent
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Figure 2-4. Forest understory native species cover in A. crenata invaded and
uninvaded plots at each of the five sites in Alachua County, Florida, USA (see Table 2-1 for abbreviation definitions of sites). A. crenata presence had a significant effect on native species cover (X2 = 18.2, p < 0.001). Red stars are means. The top and the bottom of each box correspond to the first and third quartiles (the 25th and 75th percentiles). The median is indicated by the thick horizontal line. Whiskers indicate the highest/lowest values that is within 1.5 * IQR of the box boarder, where IQR is the inter-quartile range, or distance between the first and third quartiles. Black dots are outliers.
41
Figure 2-5. Principal component analysis correlation biplot of the first two principal components (percent of total variation) for vegetation and environmental characteristics measured during the spring sampling across the five study sites in Alachua County, Florida, USA. Loadings for the explanatory variables: understory species number (UnspNo), understory species diversity (Diversity), understory native species cover (Uncov), overstory natives species cover (Ovcov), soil moisture (Moist), percent light (Light), and Ardisia crenata cover (Accov) are shown as vectors and the scale is on the left and bottom axes. Each point represents an invaded or uninvaded plot.
42
Figure 2-6. Principal component analysis correlation biplot of the first two principal components (percent of total variation) for vegetation and environmental characteristics during the spring sampling across the five study sites in Alachua County, Florida, USA. Loadings for the explanatory variables: understory species number (UnspNo), native tree species cover (Tree), native herb, forb and graminoid species cover (Herb) native vine cover (Vine), overstory natives species cover (Ovcov), soil moisture (Moist), percent light (Light), and Ardisia crenata cover (Accov) are shown as vectors and the scale is on the left and bottom axes. Each point represents an invaded or uninvaded plot.
43
CHAPTER 3 INFLUENCE OF SHADE TOLERANT INVASIVE SHRUB, ARDISIA CRENATA ON
OAK SEEDLING REGENERATION IN MESIC FOREST IN FLORIDA
Low light availability under closed forest canopies (9% of incident radiation in
southern hardwood forests; Canham et al. 1990) constrains growth and survival of
many plants including seedlings of overstory species. In fact, native tree species
capable of persisting in understories show some level of shade tolerance, i.e., the ability
to withstand low light levels during some part of their life cycle (Valladares & Niinemets
2008). Degrees of shade tolerance varies among species, and this variation is linked to
traits such as high tissue density that enhances leaf lifespan and stem survival (Alvarez-
Clare & Kitajima 2007), and carbohydrate storage that enhances survival and recovery
from episodes of negative carbon balance (Myers & Kitajima 2007). These traits allow
shade tolerant seedlings to persist under limited light availability near their light
compensation points (Givnish 1988; Baltzer & Thomas 2007) where even small change
in light availability can significantly influence carbon balance of seedling performance.
Hence, within-species variation in seedling growth and survival of shade tolerant
species can be linked to temporal and spatial variations in light within the forest
understory in tropical (Montgomery & Chazdon 2002) and temperate forest (Canham
1989).
The shade stress in understories of closed canopy forests is long believed as a
barrier to invasion by exotic invasive plants, many of which are disturbance-dependent
(Davis et al. 2000). Yet, increasing number of studies report that undisturbed closed
canopy understories are invaded by shade tolerant exotic species (Martin et al. 2008).
The negative impact of these shade tolerant invaders on the understory communities
occurs in terms of reduction of species richness, cover, and biomass of native trees
44
(Bottollier-Curtet et al. 2012), and of native shrubs (Collier et al. 2002)(Chapter 2).
However, most of these studies did not test the mechanisms or processes by which the
invasive species exclude native species or their broader impacts to ecosystem
properties. Competition for limiting resources has been suggested, but rarely tested
(Levine et al. 2003).
These species that invade undisturbed forests are predominantly shrubs (28% of
global species, followed by trees 23%, herbs 36%, vines 17%, and grasses 11%)
(Martin et al. 2008). Further, these species exhibit greater shade tolerance where they
are non-native compared to where they are native, most likely due to enhanced carbon-
balance associated with the “enemy release” from host-specialized herbivores and
pathogens (DeWalt, Denslow, & Ickes 2004). Thus, the lack of natural enemies enables
invasive species to allocate resources to growth or further enhance capacity to capture
limiting resource giving it an advantage over native species. In addition, the competitive
ability may be enhanced by the “novel weapons” of allelopathic chemicals released to
the soil by the invaders, to which indigenous species are not adapted and thus
experience reduced growth or seed germination (Callaway & Ridenour 2004; Cipollini,
McClain, & Cipollini 2008).
In this paper, we investigated native species displacement mechanisms and
processes associated with invasion by shade tolerant shrubs. As an example of such
invader, we chose A. crenata which invades the shade understory under the closed
canopy of hardwood hammock forests in north central Florida (Chapter 2). It is
speculated that the leaf display patterns of A. crenata locally casts a deep shade
enhancing its competitive ability for light over native species (Kitajima et al. 2006). A
45
reduction of light availability in closed-canopy forests understories can have an
important implication in regeneration of overstory species sensitive to changes in light
availability (Poorter 2007).
A. crenata Sims. (Myrsinaceae) is a shade tolerant evergreen shrub that was
introduced and promoted by the horticulture industry as an ornamental (Wirth et al.
2004). It is classified as a Category 1 Pest Plant by the Florida Exotic Pest Plant
Council (FLEPPC 2011). A. crenata is capable of forming dense monodominant patches
(Burks & Langeland 1998) with cover reaching >90% of ground (Chapter 2) and 300
stems per m2 (Kitajima et al. 2006). Dispersal is limited, but birds are the main
dispersers (Meisenburg 2007). Genetic analyses suggest that A. crenata originated
from southeastern China multiple times and then spread from there (Niu et al. 2012).
In order to test whether light competition is one of the mechanism by which A.
crenata suppress natives, a manipulative field experiment was conducted. The overall
objective of this study was to explore the influence that A. crenata has on seedling
regeneration of oaks that currently dominate the overstory of hardwood mesic forests in
north-central Florida. Two oak species evaluated were Quercus hemisphaerica and Q.
virginiana. These are common species found in the overstory of these forests. More
specific objectives were the following:
1. To determine the effects of A. crenata invasion on survival and growth of seedlings of Quercus hemisphaerica and Q. virginiana, two common canopy oak species.
2. To assess the effects of aboveground competition on growth and survival of oak seedlings in the dense stand of A. crenata by comparing the intact plots vs. plots in which A. crenata stems are pulled down.
3. To establish the influence of microenvironmental variations and their potential interaction with A. crenata invasion on oak seedling survival.
46
We hypothesized that A. crenata would have a negative effect on survival and
growth of both oak species. Further, we hypothesized that the negative effect would be
at least partly attributable to aboveground competition for light availability, and predicted
that pulling down A. crenata stems would enhance light and seedling performance. We
also hypothesized that Q. virginiana, the more light-demanding of the two species
(Spector & Putz 2006), would exhibit greater negative effects of A. crenata presence.
Seedling growth and survival were expected to be improved by light availability and soil
moisture.
Material and Methods
Site and Study Design
Hardwood hammocks are important elements of Florida’s landscape. These forest
communities are characterized by dominance of evergreen broadleaf trees, often mixed
with some evergreen conifers and deciduous trees, under which layers of subcanopy
and understory vegetation are present. They are further classified by soil water
available, such as xeric, mesic, and hydric hammocks (Vince et al. 1989). The
hardwood hammocks of north central Florida occur in a transitional zone between the
mixed hardwood forests in the south eastern US and the tropical evergreen forests of
south Florida (Platt & Schwartz 1990). Examples of common dominant species include
broadleaf evergreen species such as Quercus spp. and Magnolia grandiflora, evergreen
conifers such as Pinus glabra and P. taeda, and deciduous hardwood species, such as
Liquidambar styraciflua and Carya glabra.
We selected four sites from mesic hardwood hammocks near Gainesville, Florida,
where we could locate dense patches (> 80% of understory cover) of actively invading
populations of Ardisia crenata (See Table 3-1 for site names and locations). These sites
47
were at least 1.5 km from each other, with a maximum distance of 15 km. All sites were
closed-canopy forest fragments that exhibit typical species composition for the
hardwood hammocks in the area, without any sign of major disturbance. In
communication with the landowners and land managers of these sites, we ensured that
there would be no active removal efforts before and through the end of 2011 when this
study was completed. At each site, we established 36 plots, each measuring 1.5 m by
1.5 m. Of these, 24 plots were established within densely invaded patches (A. crenata
“present” plots) and 12 additional control plots in adjacent areas where A. crenata was
yet to invade (A. crenata “absent” plots) within 0.5 m of which contained no A. crenata
individuals. Within the invaded area, the 24 invaded plots were paired by proximity, and
one of each paired plots was randomly chosen to receive the “pull-down” treatment to
reduce above-ground competition, in which A. crenata stems taller than 25 cm were tied
to plastic coated wires and pulled down toward outside of the plot. The intended effect
of this “pull-down” treatment was to increase light availability while maintaining
competition in the rooting zone of the oak seedlings to be transplanted (see oak
seedlings section below). Although, the “pull-down” treatment could have also
influenced belowground competition, we expected that it was minimally influenced by
the treatment because the pulled-down stems remained alive and sometimes
resprouted from the base. A. crenata plants in the second of each plot pair were left
intact (“no pull-down”). The rational for pairing plots was the importance of stratified
randomization to ensure that microenvironmental factors were comparable between the
“pull-down” and “no pull-down” treatments.
48
Environment Conditions
After seedlings were planted, light availability was measured as photosynthetically
active radiation (PAR) in all plots at 35 cm (average height of seedlings) above the soil
surface with a line quantum sensor (Li-COR, Lincoln, Nebraska). In the plots with A.
crenata, PAR was also measured at 10 cm above A. crenata of the tallest individual
within the plot to evaluate A. crenata’s effect on light availability. Simultaneously, we
continuously monitored reference light in a completely open area nearest to each site to
express the measured PAR as %PAR transmission relative to the light in a nearby open
area. Measurements were taken under clear-sky conditions from 11 am to 3pm, once
after seedlings were planted.
After seedlings were planted, volumetric soil moisture was estimated once in each
plot with a soil moisture probe (Theta probe type ML1, Delta-T devices, Cambridge
England). Four measurements were taken from different positions within each plot to
account for soil heterogeneity. At each site 6 soil cores (diameter 5 cm and depth 10
cm, volume 196 cm3) were sampled, one chosen randomly from the 3 invaded plots and
another from the 3 non-invaded plots. Each core sample was measured for bulk density,
gravimetric water content and nutrients. After homogenizing, a subsample of 5 g from
each soil core was used to quantify availability of phosphorus (P), potassium (K),
calcium (Ca) following extraction with 20 mL of the Mehlich-1 extraction solution
(0.0125M H2SO4 and 0.05M HCl), and nitrate and ammonium nitrogen (NO3+ and NH4
+)
following extraction with 1M KCl. The extracts were filtered through Whatman 42 filter
paper and sent to the University of Florida IFAS Analytical Research Laboratory, for
determination of P, K, and Ca concentration with ICP (Inductively Coupled Plasma
49
Spectrometry, EPA Method 200.7) and NO3-N and NH4-N with an Alpkem Auto
Analyzer (EPA Method 353.2).
Oak Seedlings: Planting and Measurements of Growth and Survival
In April 2009, bare root seedlings of similar size, 350 each of Q. hemisphaerica
(10 month old) and Q. virginiana (12 month old) were purchased from a local nursery,
Central Florida Lands and Timber Nursery, L.L.C. Seedlings were planted in the field at
the end of April 2009 at all sites with 3 seedlings of each species within each plot,
separated by 50 cm. A dibble bar (7 cm wide, 20 cm long, and 1.9 cm thick) was used
to create a 24 cm deep hole in the ground to plant seedlings. Soil and surrounding
vegetation disturbance was kept at a minimum. Seedlings were tagged with flagging
tape to prevent confusion with other oak seedlings that were present in each plot prior
to planting.
A month after planting, height was measured for all seedlings to the nearest mm.
Two additional height measurements were taken, one in December 2009 (240 days
after planting) and another in December 2010 (600 days after planting) when survival
was also recorded.
Randomly selected 20 seedlings per species were destructively harvested at the
time of transplanting, and separated to roots, stems and leaves. Leaves were scanned
with a flatbed scanner, and leaf area was calculated using Scion Image (Scion
Corporation, Frederick, Maryland, USA) to the nearest mm2. Dry mass was measured
after drying at 60oC for 72 hours. We harvested all surviving seedlings after the
December 2010 census, following the same method as the initial harvest. After this final
harvest, we also harvested and determined all aboveground biomass of adults (> 20 cm
height) and seedlings (< 20 cm) of A. crenata within the plots.
50
Statistical Analyses
All statistical analyses were conducted with R (R Development Core Team 2012)
and used a significance level of P = 0.05. We analyzed environmental variables,
including soil nutrients, PAR, and A. crenata biomass, using a one-way ANOVA to test
for differences among sites, after transforming to achieve normality with natural-log
transformation (K) or Box-Cox power (P, Ca) if necessary. Tukey post-hoc test was
used to identify differences between sites. For NH4+-N, which could not be normalized
after any transformation we used Kruskal-Wallis test, followed by Nemenyi-Damico-
Wolfe-Dunn post hoc test (Hollander & Wolfe 1999).
Seedling survival was tested with logistic regression, in which the response
variable was the fate of each seedling (alive vs. dead). In the first analysis, survival
recorded at each census (240 and 600 days after planting) was compared. “Pull-down”
and “No pull-down” plots were within the same A. crenata patch and “Absent” plots were
outside of this patch. Due to the lack of independence, two statistical analyses were
conducted first to compare “Absent” versus “no pull-down,” and the second “pull-down”
versus “no pull-down.” For the first analysis a generalized linear mixed model with
binomial distribution was used to test differences in seedling survival, in which A.
crenata presence and oak species identity were the main treatment factors, seedling
initial height, soil moisture, and light availability were covariates, and site and plot-
nested-within-site were considered as random variables. The second analysis had the
same model structure, except one of the treatment factors was “pull-down” vs. “no pull-
down,” instead of A. crenata presence vs. absence.
Change in biomass was determined by comparing initial harvest of seedlings with
harvest of surviving individuals at the end of the experiment. Due to low survival and
51
insufficient sample size, biomass of live seedlings could not be compared statistically.
Thus, to analyze the impacts of A. crenata on biomass accumulation by oak seedlings,
we assumed the final biomass of dead seedling to be zero, then used a generalized
linear mixed model with a Tweedie distribution, which allows analysis of zero-inflated
data. The results for seedling biomass per live seedling at the end of the experiment are
reported in the Appendix C (Figure C-5).
Results
Site Characteristics
The four sites differed in soil characteristics (Table 3-1); Potassium (F3,20 = 16.9, P
< 0.001), Calcium (F3,20 = 15.2, P < 0.001), NO3-N (F3,20 = 39.0, P < 0.001), moisture
(F3, 144 = 54.8, P < 0.001) and bulk density (F3, 31 = 4.4, P = 0.01). The Newnan’s Lake
site had the lowest fertility and the most fertile site was the Cemetery. However, soil
fertility had no apparent relationship with A. crenata biomass per area. Biven’s site had
the lowest A. crenata biomass per area and Hogtown had about 2.5 times more
biomass per area. Overall invaded plots had higher phosphorus (X2 = 13.2, P < 0.01)
and calcium (X2 = 13.7, P < 0.01).
Seedling Survival
Oak seedling survival was significantly lower in the presence of A. crenata at the
first census (240 days, P = 0.01) and did not differ significantly between Q.
hemisphaerica and Q. virginiana (Tables 3-2 & 3-3; Figure 3-1). Soil moisture measured
at the time of seedling planting significantly influenced seedling survival, which was
lower at lower soil moisture in both censuses (P < 0.001). Light availability at 35 cm and
seedling initial height were not significant factors in the model (P = 0.43 and P = 0.17,
respectively).
52
Similarly, oak seeding survival at the second census (600 days, P < 0.001) also
did not differ significantly between Q. hemisphaerica and Q. virginiana (Table 3-2 & 3-3;
Figure 3-1). Soil moisture measured at the time of seedling planting significantly
influenced seedling survival, which was lower at lower soil moisture in both censuses (P
= 0.003, respectively). Light availability at 35 cm and seedling initial height were not
significant factors in the model (P = 0.11 and P = 0.16 respectively).
Within invaded plots, “pull-down” treatment significantly increased oak seedling
survival compared to “no pull-down" treatment at the first census (P = 0.02), and Q.
hemisphaerica had lower survival compared to Q. virginiana (Table 3-4 & 3-5; Figure 3-
1). Greater initial light availability at 35 cm (mean PAR =13.1%) in the “pull-down” plots
than in the “no pull-down” plots (mean PAR = 8.6%) enhanced seedling survival in the
first census (P = 0.001; Appendix B, Figure B-6). Initial soil moisture and A. crenata
biomass per plot were not significant.
In the second census as well, the “pull-down” treatment increased seedling
survival (P = 0.02) and Q. hemisphaerica had lower survival compared to Q. virginiana
(Table 3-4 & 3-5; Figure 3-1). Initial light availability at 35 cm did not enhance seedling
survival assessed at the second census. Initial soil moisture became significant at the
second census (P < 0.001) where lower soil moisture was associated with lower
seedling survival (Table 3-5). Greater A. crenata adult biomass in the plot also had a
negative influence in survival.
Seedling Biomass
Total mass per seedling decreased significantly from the initial values to 600 days
after planting in the analysis treating dead seedling mass as zero (Figure 3-2). Overall,
the presence of A. crenata had a negative effect on biomass (P < 0.001 Table 3-6). Q.
53
hemisphaerica had a lower biomass than Q. virginiana and this difference was
maintained regardless of the presence of A. crenata (Figure 3-2). Within invaded plots,
the “pull-down” treatment A. crenata had a significant positive effect on biomass of both
oaks (Table 3-7, P < 0.001). However, species varied in response to “pull-down”
treatment; Q. virginiana had a positive response to “pull-down,” where as Q.
hemisphaerica did not (P = 0.004).
Surviving Q. virginiana seedlings were larger than Q. hemisphaerica (Appendix B,
Figure B-5). Also a small number of surviving seedlings in A. crenata presence plot with
“no pull-down” were larger and their final biomass was similar to the initial biomass.
Surviving seedlings in “pull-down” and A. crenata absent plots were smaller on average.
Discussion
The results of this study suggest that the invasive shrub Ardisia crenata is likely to
reduce the recruitment of canopy tree species in the understory of mesic hardwood
forests of north-central Florida. The presence of A. crenata reduced the survival and
growth of seedlings of two oak species: Q. virginiana and Q. hemisphaerica. The
responses to the “pull-down” treatment were consistent with the hypothesis that A.
crenata imposes significant aboveground competition to other understory plants. In the
long run the invasion of A. crenata might alter the structure and species composition of
the mesic hardwood hammocks.
Light in the understories of closed-canopy forests is a limiting resource, and even
small reductions in the availability of light can have direct negative effects on growth
and survival of understory species (Montgomery & Chazdon 2002). Despite this
limitation, A. crenata is capable of invading, reproducing and further reducing the
understory light availability. This study is the first to demonstrate experimentally that
54
resource competition for light imposed by an invasive understory shrub negatively
affects performance of native tree seedlings, although similar effects were observed for
invasive canopy tree, Acer platanoides, which alters both quantity and quality of light
under their canopies, influencing survival of natives in riparian communities of western
Montana (Reinhart et al. 2006).
However, native species that differ in light demands are likely to respond
differently to shading by invasive shrubs. Native shrubs in pine-dominated forests of
northern Minnesota have differential effects on tree seedling survival; survival of light-
demanding species are most strongly affected by shrubs that reduce light availability by
30% than by other growth forms (Montgomery, Reich, & Palik 2010). Many tree species
in closed-canopy hardwood hammock forests depend on disturbances to reach canopy
stature, but light requirements differ among species (Platt & Schwartz 1990). Of the two
oak species we examined, Q. virginiana is considered to be more light demanding
(Spector & Putz 2006), but its survival under A. crenata was better than Q.
hemisphaerica. This difference was the opposite of our initial expectation. But, it could
be attributed to the greater initial size of Q. virginiana compared to Q. hemisphaerica.
Larger size could be associated with larger carbohydrate storage which might enhance
the tolerance to shade and environmental stresses (Myers & Kitajima 2007). Also, Q.
virginiana seedling heights were taller on average (343.5 mm) at initial planting and
could possibly have greater access to light than Q. hemisphaerica (242.9 mm).
Alternatively, Q. hemisphaerica could be more sensitive to belowground conditions in
the area invaded by A. crenata. Species interactions are the net effect of both above-
and belowground competition (Gorchov & Trisel 2003). A. crenata roots were left intact
55
in the pull-down treatment, and hence they imposed competition for belowground
resources and modify soil conditions although root competition may be less compared
to the A. crenata present plot without this treatment. There were only detectable
differences in phosphorus and calcium for soil chemical characteristics between inside
and outside of A. crenata invaded areas. Access to soil resources can be enhanced by
belowground mutualisms and A. crenata establishes effective symbiotic relationships
with native mycorrhizal fungi, enhancing its competitive advantage over native species
(Bray, Kitajima, & Sylvia 2003).
The ecological impacts of forest invasion by understory shrubs may be less
obvious than invasion by canopy dominant species, yet reduction of tree seedling
regeneration can have a long term impact on forest community structures. A. crenata
reduces the diversity of native plants including seedlings of canopy trees in the forest
understory (Chapter 2). The results of this study demonstrate that one of the potential
mechanisms with which A. crenata reduces recruitment of canopy tree seedlings is
aboveground competition. Over time, suppression of seedling recruitment can
significantly change the forest community structure as overstory trees die and become
replaced. Land managers may want to take into consideration the lack of recruitment of
oaks and other native species when managing the natural forests invaded by A.
crenata. In addition to reduction of A. crenata density by manual removal or herbicide,
enrichment planting of seedlings of native species may increase the probability of
success for biodiversity conservation.
56
Table 3-1. Locations, their soil characteristics and Ardisia crenata biomass (means standard deviation) for the four study sites in Alachua County, Florida, USA. Different superscript letters indicate significant difference between mean values by Tukey post-hoc test.
Site Latitude and longitude
Bulk density (g/cm
3)
Soil moisture (m
3/m
3)1
P (mg/kg)
2
K (mg/kg)
3
Ca (mg/kg)
2
NH4+
(mg/kg)4
NO3-
(mg/kg) A. crenata Mass per plot (g)
5
Biven’s Arm (BA) 29°37’28.30"N, 82°20’01.22"W
1.07a
(0.10) 0.026
b
(0.010) 20.8
a
(11.2) 78.7
a
(30.9) 897.5
a
(254.8) 12.5
a
(3.9) 6.10
a
(1.22) 1420
d
(530)
Evergreen Cemetery (EC)
29°37'44.18"N, 82°19'05.75"W
1.02ab
(0.14) 0.059
a
(0.024) 9.0
b
(1.2) 69.5
a
(16.6) 936.7
a
(354.8) 14.6
a
(3.6) 4.62
b
(0.88) 2735
b
(437)
Hogtown Creek (HC)
29°41'53.15"N, 82°20'36.23"W
1.08a
(0.09) 0.029
b
(0.011) 17.3
a
(5.72) 52.4
ab
(18.1) 752.7
a
(86.0) 9.2
a
(0.5) 2.58
c
(0.65) 3629
a
(604)
Newnan’s Lake (NL)
29°37'54.62"N, 82°12'14.47"W
0.88b
(0.20) 0.057
a
(0.014) 7.4
b
(0.51) 34.6
b
(6.2) 362.2
b
(69.3) 10.9
a
(1.5) 1.33
c
(0.28) 1700
c
(369)
Ardisia present6
0.99a
(0.15) 0.039
a
(0.018) 17.3
a
(2.0) 61.0
a
(18.1) 850.9
a
(330.7) 11.4
a
(3.9) 3.61
a
(2.32)
Ardisia absent6
1.03a
(0.16) 0.052
a
(0.025) 9.9
b
(10.3) 56.7
a
(31.7) 623.7
b
(205.6) 12.1
a
(2.6) 3.70
a
(1.80)
1Volumetric soil moisture measured in all plots with a Theta probe. 2 Box-Cox transformation. 3 Log transformation. 4 Non-parametric test. 5Aboveground biomass of A. crenata was determined for all invaded plots (2.25 m2), including both “pull-down” and “no-pull-down” treatments. 6Difference between plots with Ardisia present (n = 12) or absent (n = 12); Soil moisture Ardisia present (n = 96) or absent (n = 52)
57
Table 3-2. Logistic mixed model results for seedling survival 240 days after transplanting of the two oak species (Q. virginiana and Q. hemisphaerica) and two treatments (Ardisia crenata presence and absence, excluding “pull-down” treatment), across the four sites in Alachua County, Florida, USA. Seedling height, light measured 35 cm above the ground, and volumetric soil moisture per plot at transplanting time are used as covariates.
df X2-value P-value
A. crenata presence 1 6.1 P=0.01
Initial height 1 1.8 P=0.18
Initial light 1 0.6 P=0.44
Species 1 3.8 P=0.05
Initial soil moisture 1 7.1 P<0.001
Table 3-3. Logistic mixed model results for seedling survival 600 days after
transplanting of the two oak species (Q. virginiana and Q. hemisphaerica) and two treatments (Ardisia crenata presence and absence, excluding “pull-down” treatment) across the four sites in Alachua County, Florida, USA. Seedling height, light measured 35 cm above the ground, and volumetric soil moisture per plot at transplanting time are used as covariates.
df X2-value P-value
A. crenata presence 1 14.3 P<0.001
Initial height 1 1.9 P=0.16
Initial light 1 2.6 P=0.11
Species 1 0.001 P=0.97
Initial soil moisture 1 8.5 P=0.003
58
Table 3-4. Logistic mixed model results for seedling survival 240 days after transplanting of the two oak species (Q. virginiana and Q. hemisphaerica) and two treatments (Ardisia crenata canopy “pull-down” and “no pull-down”) across the four sites in Alachua County, Florida, USA. Seedling height, light measured 35 cm above the ground, and volumetric soil moisture per plot at transplanting time are used as covariates. As well as, A. crenata seedling and adult aboveground biomass at the 600 days.
df X2-value P-value
Pull-down 1 5.9 P=0.02
Initial height 1 0.09 P= 0.77
Initial light 1 10.5 P= 0.001
Species 1 4.5 P= 0.03
Ardisia adult mass 1 0.2 P=0.66
Ardisia seedling mass 1 0.4 P=0.52
Initial soil moisture 1 0.3 P=0.57
Table 3-5. Logistic mixed model results for seedling survival 600 days after
transplanting of the two oak species (Q. virginiana and Q. hemisphaerica) and two treatments (Ardisia crenata canopy “pull-down” and “no pull-down”) across the four sites in Alachua County, Florida, USA. Seedling height, light measured 35 cm above the ground, and volumetric soil moisture per plot at transplanting time are used as covariates. As well as, A. crenata seedling and adult aboveground biomass at the 600 days.
df X2-value P-value
Pull-down 1 8.4 P=0.004
Initial height 1 0.008 P= 0.93
Light 1 1.2 P=0.27
Species 1 4.8 P=0.03
Ardisia adult mass 1 5.5 P= 0.02
Ardisia seedling mass 1 0.8 P= 0.37
Initial soil moisture 1 12.6 P< 0.001
59
Table 3-6. Linear mixed model with a Tweedie distribution results of two oak seedling biomass (Quercus virginiana and Q. hemisphaerica) at 600 days after transplanting comparing three treatments (Ardisia crenata absent, A. crenata “no pull-down,” and initial harvest) across the four sites in Alachua County, Florida, USA.
df X2-value P-value
A. crenata presence 2 33.5 P<0.001
Species 1 54.4 P<0.001
A. crenata presence*Species 2 0.6 P=0.72
Table 3-7. Linear mixed model with a Tweedie distribution results of two oak seedling
biomass (Quercus virginiana and Q. hemisphaerica) at 600 days after transplanting comparing three treatments (Ardisia crenata “no pull-down”, A. crenata “pull-down,” and initial harvest) across the four sites in Alachua County, Florida, USA.
df X2-value P-value
Pull-down 2 14.1 P<0.001
Species 1 65.2 P<0.001
Pull-down*Species 2 10.9 P=0.004
60
240 day census 600 day census
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Figure 3-1. Box plot of the probability of survival for each individual seedling in a plot based on generalized linear mixed model of Oaks (Quercus virginiana and Q. hemisphaerica) in presence and absence of Ardisia crenata and A. crenata stems “pull-down” and “no-pull-down” at each site for 240 and 600 days census across the four sites in Alachua County, Florida, USA. The top and the bottom of each box correspond to the first and third quartiles (the 25th and 75th percentiles). The median is indicated by the thick horizontal line. Whiskers indicate the highest/lowest values that is within 1.5 * IQR of the box boarder, where IQR is the inter-quartile range, or distance between the first and third quartiles. Black dots are outliers.
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ss (
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Figure 3-2. Box plot of biomass per seedling for the two oak species (Quercus hemisphaerica and Q. virginiana), includes total biomass from the initial harvest, plots without Ardisia crenata (Absent), plots with A. crenata canopies pulled down (Pull-down), and plots with A. crenata canopy intact (No Pull-down) across the four sites in Alachua County, Florida, USA. The analysis includes biomass of dead individuals as zeros. The top and the bottom of each box correspond to the first and third quartiles (the 25th and 75th percentiles). The median is indicated by the thick horizontal line. Whiskers indicate the highest/lowest values that is within 1.5 * IQR of the box boarder, where IQR is the inter-quartile range, or distance between the first and third quartiles. Black dots are outliers.
62
CHAPTER 4 DOES HERBICIDE TRANSLOCATION CORRELATE WITH SEASONAL
CARBOHYDRATE BALANCE IN AN EVERGREEN SHRUB ARDISIA CRENATA?
Invasions by exotic plant species are a growing concern around the globe.
Exotic invasive plants may incur negative ecological impacts by altering
disturbance regimens, nutrient cycling, and productivity of ecosystems, and
displacing native species (Vilà et al. 2011). Furthermore, they incur economic
costs of approximately $120 billion per year in the U.S. alone (Pimentel, Zuniga,
& Morrison 2005). Many invasive exotic organisms have been recognized as
serious threats to natural ecosystems in Florida (Gordon & Thomas 1997). Of the
nearly 1,400 naturalized plant species in Florida, about 11% have become
invasive and threaten many of the State’s natural areas (FLEPPC 2011). In an
effort to counter this trend, Florida spent approximately $230 million from 1980 to
2006 managing aquatic, wetland and upland exotic invasive species (Schmitz
2007). The methods implemented for control of invasive plants in natural areas
include biological control agents (insects and pathogens), mechanical removal
(use of machinery to cut, shear, shred, and crush plants and manual removal),
fire, and herbicides, which may be combined to increase the efficacy.
To achieve a cost effective measure to control exotic species, it is
necessary to consider species specific attributes including growth forms, life
histories, and physiological characteristics. Perennial plants in seasonal
environments change patterns of vegetative growth, photosynthate translocation
and reproduction during the year. If the desired control method, such as
herbicides requires the plant to be actively growing, one should tune time of
63
control with the time when the plant is most actively growing. However, the most
effective time of the year may differ among control methods. Therefore, to
minimize the costs of control and maximize efficacy, a critical factor is to
determine the best time of the year to conduct treatments in relation to treatment
methods.
The current study focuses on Ardisia crenata (Myrsinaceae), which is a
good example of a shade tolerant shrub that persists in the understories of
natural closed-canopy forests. Such shade tolerant invasive plants, many of
which are shrubs, are becoming a growing concern in many forest ecosystems
(Martin et al. 2008). A. crenata was introduced and promoted by the horticulture
industry as an ornamental for more than 100 years (Wirth et al. 2004), but it has
been classified as a Category 1 Pest Plant (i.e., those that have the most serious
impacts on community structures and ecosystem functions) by the Florida Exotic
Pest Plant Council (FLEPPC 2011). A. crenata forms dense mono-dominant
patches in the understory, with high density of adult and seedling stems up to
600 per m2. The impacts of A. crenata in natural areas such as hardwood
hammocks include reductions of richness and cover of native species (See
chapter 2) and reduction of seedling recruitments of overstory canopy species.
Both, in turn, have the potential to modify forest structure in the long term (See
chapter 3).
To reduce the impact of A. crenata on natural areas, typical methods of
control implemented by land managers include manual removal or mechanical
mowing in small populations, whereas mowing and spraying are often combined
64
with applications of herbicides such as glyphosate (N-(phosdphonomethyl)
glycine), 2,4-D (2,4-D-Dichlorophenoxyacetic acid), or triclopyr in large
populations (Langeland et al. 2011). Mechanical removal, such as mowing, in
combination with herbicide application have shown to be useful for control of
perennial plants (MacDonald et al. 1994; Mislevy, Mullahey, & Martin 1999).
Resprouting following mowing reduces storage reserves such as non-structural
carbohydrates in the roots, and a subsequent herbicide application is expected to
be more effective because plants would have reduced capacity to regrow shoots
after the herbicide application (Kalmbacher, Eger, & Rowland-Bamford 1993).
However, if a species has large reserves of non-structural carbohydrates in roots,
it may be capable of resprouting many times. A crenata has some of the largest
concentrations of non-structural carbohydrates in roots compared to other woody
species, possibly enhancing its capacity to resprout (Kitajima et al., 2006).
Of the above mentioned herbicides, triclopyr is preferred by land managers
for its greater efficacy on woody plants. Triclopyr (3,5,6-trichloro-2-
pyridinyloxyacetic acid) is a selective systemic herbicide that mimics the effects
of plant hormones (auxins, up to 1000 times natural levels) which disrupts
hormonal balance and alters growth (Ganapathy 1997; Tu et al. 2001). Triclopyr
comes in two formulations: triethylamine salt (TEA) and butoxyethyl ester
(TBEE). Both can be sprayed on leaves for the desired effect of translocation to
roots and meristems to kill whole plants.
In perennial species the movement of foliar herbicides has been positively
correlated to movements of non-structural carbohydrates from source regions
65
(leaves) to sink regions (active growth) of the plant via the phloem (Devine & Hall
1990). The direction of movement can be dictated by seasonal changes in flows
of non-structural carbohydrates within the plant (Engle & Bonham 1980). For
example, during the spring season when production of photosynthates in the leaf
is low, there is a net movement of non-structural carbohydrate from storage
sources such as roots to active growth meristems and leaves. Hence, there is a
lower movement of foliar-applied herbicides to other parts in the plant.
Land managers have reported the need to spray herbicides several times to
obtain the desired control of A. crenata (Michael Meisenburg, personal
communication). Therefore, a better understanding of the mechanisms of
herbicide movement and dynamics of carbohydrates is needed to increase the
efficacy of control methods such as timing of spraying, dosage, and use of
surfactants and adjuvants. In North Central Florida, A. crenata conducts active
vegetative and reproductive growth (new leaves, shoots, flowers and developing
fruits) during the summer (June through early September). Mature fruits are born
on the plant from December for up to a year until a new crop of fruits mature.
The roots accumulate non-structural carbohydrates (primarily starch) during
the non-growing season of winter and spring months (December through April),
coinciding with increased light availability in semi-deciduous canopy (Kaoru
Kitajima unpublished data). From this season flux we predict that herbicide
translocation coincides with non-structural carbohydrate translocation to roots in
winter (Figure 4-1).
66
The overall objective of this study is to assess the efficacy of mowing and
triclopyr herbicide when employed to the exotic invasive shrub A. crenata during
different seasons, and to determine the absorption and translocation of triclopyr.
More specific objectives are the following:
1. To examine the effects of mowing on root carbohydrate concentration and herbicide efficacy.
2. To test if herbicide effects differ between summer and winter, as predicted by the seasonal carbohydrate dynamics.
3. To quantify the percentage of the applied herbicide that is translocated to the roots.
We hypothesized that control of A. crenata by mowing once and the
application of triclopyr herbicide in winter or spring would have the greatest
control. Furthermore, we hypothesized that this increase in efficacy would be
associated with non-structural carbohydrate translocation to roots.
Materials and Methods
Two experiments were conducted to evaluate herbicide efficacy over time,
one in the field and the other in the greenhouse. The field experiment examined
the seasonal trend of herbicide efficacy in relation to root carbohydrate dynamics;
the greenhouse experiment quantified herbicide movement in the plant in relation
to light level and root carbohydrate status.
Field Experiment
We selected three mesic hardwood forest sites near Gainesville, Florida
(Table 4-1), infested with dense patches of A. crenata (at least 80% ground
cover, size of patches > 1 ha). These three sites were chosen within the vicinity
of the field experiment in Chapter 3, based on the logistic advantages such as
67
existing municipal permits and implementing experiments activities. All sites were
relatively undisturbed forests dominated by broadleaf evergreen and deciduous
canopy trees, such as Quercus spp. In communication with the landowners and
land managers, we were ensured that active removal efforts occurred in these
sites before and through the end of 2011, when this study was completed. At
each site we located a large dense patch of A. crenata and established two
rectangular adjacent blocks, each measuring 11.75 m x 4.25 m, for a total of six
blocks. In one of the blocks at each site A. crenata was treated by mowing
individuals to a height of 5 cm June 2009. Any seedlings < 5 cm could be
potentially untouched by the mowing blades, but most likely they were damaged
and killed by mowing. Cut shoots (stems, leaves, flowers and fruits) of adults (>
20 cm stem height) were removed from the mowed area. The intended effect of
mowing was to reduce carbohydrate reserves in the roots by obligating plants to
resprout and grow over several months, after which herbicide may be more
effective in preventing plants to recover from the roots. Each block was
subdivided into 25 plots, each measuring 0.75 m by 0.75 m, and separated by
0.5 m (Figure 4-2).
At the beginning of the experiment plots were randomly assigned to one of
five treatments (four herbicide application times and one control) and of 5
replicates per treatment. We selected four different times of the year (October
2009, January 2010, April 2010, and July 2010) for the herbicide treatment in
order to evaluate the effect of different seasons on herbicide efficacy. These
treatment times were 4, 7, 10 and 13 months after the mowing, respectively. The
68
first four months (June-October) corresponded to the season of shoot extension
and leaf development, and individuals mowed in June had vigorously resprouted
and regrew shoots mostly prior to the first herbicide application time. The control
plots, with no herbicide application, were maintained over the entire period. Plots
were monitored by taking photos to estimate A. crenata cover every 15 days from
October 2009 until July 2011, one year after the last herbicide application.
Herbicide application and efficacy measurements
Triclopyr ester at 2 % v/v (Remedy Ultra, 10.8 g acid equivalent L-1 TBEE)
with 0.5% nonionic surfactant (DyneAmic) was used for the herbicide treatment.
Herbicide was applied using a 2-gallon hand-pressurized handheld sprayer with
a fan nozzle (Roundup Ortho Heavy Duty, The Fountainhead Group Inc., New
York Mills, New York) on a spray to wet basis (625 L ha-1). Off target application
to adjacent plots was prevented using a Styrofoam lamina barrier (1.5 m height)
covering 3 sides of the plot (Figure 4-3).
Prior to each herbicide application, digital photographs of each plot were
taken from a distance of 1.6 m above ground and repeated every 15 days until
the end of the experiment. Digital images were used to estimate percent cover of
A. crenata using a point intercept method with a grid superimposed on the image
(every 7.5 cm, to create 121 grid points per plot). At each grid point intersect A.
crenata presence was evaluated (present = 1 or absent = 0). Percent cover was
number of intersections with A. crenata divided by total intersections (range from
0 to 100%). Small seedlings were distinguished from “adults” (individuals with
size corresponding to ca. > 20 cm in height) in the photos, and their cover was
estimated separately. The initial cover was used as a baseline for subsequent
69
photographs of the plot. Herbicide efficacy index was calculated as the difference
of A. crenata initial percent cover minus percent cover at the later observation
time divided by the initial percent cover (Figure 4-4).
Biomass allocation and root carbohydrate storage
Five A. crenata individuals greater than 20 cm in height were
haphazardously selected from areas between treatment plots in each block at
each herbicide application date. These individuals were harvested intact and
separated into leaf, stem, and root. Leaves were scanned and leaf area was
determined to the nearest mm2 from scanned images of each leaf using Scion
Image (Scion Corporation, Frederick, Maryland, USA). Plant biomass allocation
was determined after drying at 60oC for 72 hours and weighed to the nearest
0.01 gram.
The concentration of total non-structural carbohydrate (TNC, the total of
simple sugars and starch) per unit root mass (mg g-1) in the primary roots was
determined from harvested individuals. Dried roots were chopped, homogenized
and subsampled prior to grinding with a Wiley mill. From approximately 15 mg of
ground roots from each individual, concentrations of soluble sugars and starch
were quantified. Soluble sugars were extracted with 80% ethanol in a shaking
water bath at 27o C for 12 hours, followed by two additional repeated extractions
with ethanol in a shaking water bath at 27o C for two hours each. The remaining
sample was digested to glucose with a 1.1% hydrochloric acid solution at 100o C
for 45 minutes to collect the glucose-containing solution. This was followed by
repeated rinsing of the residual solids with deionized water at room temperature.
70
Concentration of simple sugars and starch was measured as glucose equivalent
using phenol sulphuric acid colorimetric assay, modified from Dubois (1956).
Greenhouse Experiments
Two repeated herbicide application experiments were conducted to follow
herbicide absorption and translocation with 14C labeled triclopyr herbicide, using
two sets of plants grown from two separate seed collections. Three hundred
seeds of A. crenata were collected from 3 spatially separated populations within
Gainesville, Florida, on February 2010 and on February 2011, seeds were
combined and germinated in rectangular plastic trays filled with a soil mixture
(Fafard Superfine Germinating Mix) in a greenhouse located in Gainesville,
Florida. Temperature in greenhouse was controlled so that it did not exceed
29.4o C and light photoperiod was maintained to not exceed 8 hours of dark. A
total of 40 seedlings were randomly selected with similar amounts from each site
and transplanted into 1-gallon pots filled with the same soil mixture. Plants were
grown in the greenhouse for 6 months and then assigned to two light treatments:
shade (90% neutral shade cloth covering a frame approximately 150 cm x 300
cm x 150 cm) and no shade (sun). Controlled release fertilizer was applied at an
equivalent rate of 112 kg N ha-1 (Osmocote 14-N, 14-P, 14-K). Plants were kept
under these two treatments throughout the experiment. The first experiment used
seedlings germinated in February 2010, with herbicide applications done in April
2011 (on 14 months old plants). The second experiment used a cohort of
seedlings grown from seeds collected on February 2011, and herbicide
application was done in October 2011, when seedlings were 8 months old).
Although the original intention was to use the same cohort of seedlings to
71
examine the effects of the seasonal timing of herbicide application, seedlings
from the seeds collected in February 2010 became too big and root-bound by
October 2011.
Plants were sprayed with a 0.65% solution of herbicide triclopyr amine
TBEE (Remedy Ultra, 3.5 g acid equivalent L-1) to simulate a standard field
application. Immediately following this application, the third most developed leaf
from the apical meristem of the main stem received a 6l drop containing 14C-
labeled TEA at 0.25% by volume with a 0.1% nonionic surfactant (DyneAmic)
(400,000 dpms). This was placed on the center of the leaf between the edge and
the main vein.
Prior to herbicide application, 5 individuals were randomly harvested to
quantify the initial size, total leaf area and dry mass of leaf, stem, and root. Leaf
area was determined to the nearest mm2 from scanned images of each leaf
using Scion Image (Scion Corporation, Frederick, Maryland, USA). Plant
biomass allocation was determined after drying at 60oC for 72 hours. The
concentration of total nonstructural carbohydrate (TNC, starch and simple
sugars) in roots was determined following the same method as in the herbicide
field experiment described above.
After herbicide application, 5 plants from each treatment were randomly
assigned to three planned harvests. For the April 2011 application, harvests were
scheduled at 1, 4, and 7 days after herbicide application. After these harvests it
was noticed that translocation of herbicide was very limited. Therefore, the
October 2011 harvests were scheduled at 7, 14, and 21 days after herbicide
72
application. At the time of harvest the target leaf treated with 14C-labeled triclopyr
ester (TBEE) was washed with 5 ml of water and leaf rinse was collected to
determine the amount of herbicide that did not penetrate the leaf (leaf water-
wash). The leaf then was placed in a vial with 10 ml acetone and agitated 1 min
to remove any triclopyr that was trapped in the cuticle of the leaf and was not
absorbed (leaf acetone wash).
Harvested plants were pressed and oven dried at 60oC. Dried plants that
had received 14C-labeled TBEE were then placed on X-ray film for auto-
radiograph development for 40 days. After this time, tissue was separated to
treated leaf, other leaves, stem, meristem, and roots and weighed to the nearest
0.01 gram. A subsample of 0.2 g (or amount available) of each tissue was
oxidized following the Schöniger combustion technique to liberate 14C as 14CO2.
This was trapped in scintillation fluid and labeled quantification was performed
using a Packard scintillation counter against known standard.
Statistical Analyses
All statistical analyses were conducted with R (R Development Core Team
2012) and used a significance level of P = 0.05. Treatment effects on root TNC
concentration in the field experiment was tested with a linear mixed model, in
which time of herbicide application and mowing were main treatment factors and
site and plot-nested-within-site were considered as random variables.
Herbicide efficacy in the field experiment was tested with a linear model, in
which the response variable was the herbicide efficacy index, and time of
herbicide application, mowing, and month after herbicide application were the
main treatment factors and site and plot-nested-within-site were considered as
73
random variables. Months after herbicide application were considered as
repeated measures.
Analysis of variance (ANOVA) was used to test the response of root TNC
concentration to ‘Light treatment’ and ‘date of herbicide application’ and their
interactions. A Tukey post hoc test was used to identify differences among levels.
Herbicide translocation responses were analyzed separately for the April
and October experiments, due to the differences in individual sizes and harvest
dates. The herbicide movement within the plant was determined by the amount
of labeled 14C-triclopyr herbicide recovered from different organs (treated leaf,
other leaves, meristem, stems and roots) and treated leaf washes (water and
acetone), expressed as the percent of the total initial concentration. From these,
the following were calculated: total recovery (treated leaf + leaf washes + leaves
+ meristems + stems + roots), leaf water-wash, leaf acetone-wash, absorbed
(treated leaf + leaves + meristems + stems + roots), treated-leaf absorption and
translocated (leaves + meristems + stems + roots). The amount of herbicide
translocated from the treated leaf to other parts of the plant was determined for
leaves, meristems, stems, and roots. These variables were analyzed as
response variables in ANOVAs that examined light treatment and days after
treatment (DAT) as main effects, after ensuring the assumption of normality and
equal variance. A Tukey post hoc test was used to identify differences among
levels.
74
Results
Field Experiment
Effects of season and mowing on root sugar and starch concentrations
In the field, mowing shoots of A. crenata significantly reduced root starch
concentration compared to unmowed plants (P < 0.001, Table 4-4), and this
difference was significant at each harvest time (Figure 4-5, Table 4-4). Starch
concentration significantly changed with season in both control and mowed
plants (P < 0.001, Table 4-4); the lowest in October, followed by July, and the
highest in January and April (Figure 4-5, Table 4-4). Simple sugar concentration
was also affected by mowing A. crenata (P < 0.01, Table 4-5), but in the opposite
direction from starch (Figure 4-5); simple sugar concentration significantly
increased by mowing and this difference was maintained over time (Table 4-4).
Simple sugar concentration was higher in July and October (during growing
season) than in January and April.
Herbicide efficacy in the field
Herbicide efficacy index for adult plants did not differ between 6 and 12
months after herbicide application (P = 0.63, Table 4-6). January application date
showed the lowest herbicide efficacy and significantly lower efficacy in mowed
plots than in unmowed plots (P < 0.001, Table 4-6). This month showed few
resprouts and plot cover of adult plants was explained by surviving plants that did
not get killed with herbicide. The other application dates (October, April, and July)
did not differ from each other and between mowed and unmowed treatments
(Figure 4-6).
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At 6 and 12 months after herbicide application, many new seedlings
appeared, all of which were apparently from newly germinated seeds and none
from resprouts. Herbicide efficacy index was lower for seedlings in the 12 months
after application compared to 6 months (P < 0.001, Table 4-7). Overall, January
and April herbicide applications had lower efficacy than October and July
applications (Figure 4-6). Mowing treatment increased efficacy compared to
unmowed plots except for July application date.
Greenhouse Experiments
There was a significant light treatment effect on starch concentration (P <
0.001, Table 4-8); shade treatment decreased starch concentrations in both April
and October experiments (Figure 4-7). Root sugar concentrations on the other
hand showed no difference between light treatments and between April and
October experiments (Table 4-9).
Amount of herbicide absorbed into the plants was estimated as the amount
not accounted in the leaf wash by water and acetone. In the April herbicide
application, there was no significant difference in the total recovery between light
treatments (P = 0.09) and harvest times (P = 0.45; Table 4-10). Total recovery
was 73.8 %. However, there was a significant decrease in leaf water-wash over
time (P = 0.01), from 60.1% at 1 day after treatment (DAT) to 47.4% at 7 DAT.
Accordingly there was a significant increase in amount absorbed (P = 0.04) and
translocated (P = 0.04) over time (Table 4-9 and Table 4-12). There was no
significant treatment or time of harvest effect on percent recovery in leaf acetone-
wash and treated leaf. Overall leaf acetone-wash was 12.7% and treated leaf
3.5%.
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In the October herbicide application, there was a significant decrease in the
total recovery over time (P = 0.04; Table 4-10) from 99.6% at 7 DAT to 84.5% at
21 DAT. There was a significant decrease in amount in leaf wash over time
(Table 4-11 and Table 4-13), decreasing from 78.2% 7 DAT to 45.8% 21 DAT.
There was no significant effect of light treatment or change over time for leaf
washes (acetone), absorbed and translocated. Overall leaf acetone-wash was
6.4%, absorbed 20%, and translocated 12.1%. There was a significant light
treatment effect on the amount absorbed in treated leaf (Table 4-14), lower in
sun (9.7%) than in shade (14.6%).
Radioactivity was detected in all portions of treated plants in both April and
October herbicide application dates and for all treatments (Table 4-15 & 4-18).
For the sun treatment, proportion of radioactivity in the roots (1.4%) was
significantly higher than in other organs (P <0.001). In the shade only the pair-
wise difference between ‘roots’ vs. ‘meristems’ was significant (Table 4-16).
There were no significant differences in other plant tissues. Overall there was a
significant increase in translocation in all plant parts over the 7 days period (P =
0.004; Tables 4-17); overall average from 0.31% to 0.68%. In the October
application date there was only a significant increase of translocation over 21
days for all plant parts (P = 0.002, Table 4-18); overall average from 2.1% to
5.3%.
Discussion
The results of this study confirm that triclopyr, a popular herbicide for
control of woody plants, is effective for the control of A. crenata. Although
relatively small amount of the herbicide enters the plant, there was a
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considerable amount of translocation to the roots. Contrary to what we expected,
there was no difference in efficacy of mowing adult individuals in the field, even
though mowing and subsequent re-growth reduced the root carbohydrate storage
prior to all herbicide application dates 4-13 months later. Foliar application killed
the treated plants regardless of mowing, except for a weak effect in January
when some plants were not killed likely due to cold weather. However, mowing
and shoot-removal was effective for reducing the density of seedlings originated
from seeds that germinated after herbicide applications. The recovery of A.
crenata cover from germinated seeds in unmowed plots were the strongest
following the January herbicide application.
Influence of Herbicide Timing on Efficacy
Seasonal variation of triclopyr herbicide efficacy have been linked to
environmental stress such as drought (Seiler et al. 1993). Other stress such as
temperature could also lead to reduced efficacy. In our study, the January
application date was during the months with below average mean temperatures
(Appendix C, Figure C-1). A. crenata is susceptible to freezing events, which can
lead to stem die back (K. Kitajima unpublished data). Even though efficacy index
was reduced in January application date, they were higher than triclopyr applied
to other species such as blackberry (Rubus spp.) 63% control at 12 MAT (Ferrell
et al. 2009) and Chinese privet (Ligustrum sinense) 70.3% applied in December
at 24 MAT (Harrington & Miller 2005).
Influence of Mowing on Herbicide Efficacy
Mowing increased herbicide efficacy on seedlings, however all seedlings
present in the plots were from germinated seeds rather than resprouts. All
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mowed adult shoots, including fruits, were removed. Therefore, unmowed plots
had more seeds ready to be dropped and germinate in the following year. A new
cohort of fruits mature on plants in December-January, and fruits may remain on
plants for up to one year when the next cohort of fruits mature. High seedling
cover after 12 months following the herbicide application in January can be
attributed to maximum local fruit density in January; when the plants in the
unmowed plots were killed by herbicide in January, they had more fruits than
other times of the year (Meisenburg 2007). Seeds that are dropped to the ground
germinate when temperature and moisture conditions are appropriate, mostly
from April through October (Alison Fox, personal communication). In the mowed
plots, adult-size plants that recovered shoots did not flower until the following
year (June of the following year) and produced no ripe fruit prior to the last
herbicide application date was conducted (July). Hence, a smaller number of
seedlings found in unmowed plots were likely to have originated from seeds that
had been dropped prior to the mowing, or dispersed from adults in the
surrounding area.
Herbicide Translocation
A large proportion of applied triclopyr herbicide (> 60%) did not enter the
leaf of A. crenata, and slow absorption into plants continued over 7-14 days.
Hence, a rain event shortly after herbicide application may wash down and
compromise its efficacy. In other species, such as the honey mesquite tree
(Prosopis juliflora), leaves absorb 66% of applied triclopyr ester herbicide within
24 hours. These differences in absorption by leaves are related to leaf
developmental stage and relative amount of waxy cuticle (Hess 1987). Leaves
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that have incomplete development or reduced waxy cuticle on a leaf surface tend
to show greater absorption of water-soluble herbicides. Leaf wash with acetone
recovered between 6 and 12% of applied herbicide, which was double of what
entered the leaf indicating that the cuticle represents a considerable barrier to the
herbicide entry into leaves of A. crenata. Nevertheless, the small amount of
herbicide that does enter the leaf is likely to be translocated to other plant parts,
most importantly to the roots. Triclopyr is highly mobile in plants, in particular
under warm conditions (Radosevich & Bayer 1979). The greenhouse
environmental conditions were probably optimal for herbicide translocation, while
the colder condition of the field in January may have compromised the herbicide
efficacy by constraining translocation.
In summary, triclopyr is an effective herbicide to control A. crenata, despite
the small amount of the herbicide that enters the plant. A method that will
increase herbicide penetration could yield better results and could lower rates of
herbicide application needed. Mowing was effective for controlling seedlings by
removing seed sources and possibly multiple mowing treatments could further
reduce seed source. Weather can play an important role in efficacy of adult
plants and therefore it is not recommended to apply herbicide during cold
periods. Regardless of method or timing it is recommended that multiple
herbicide treatments be conducted to obtain the desired control to kill both adults
and new seedlings.
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Table 4-1. Study site locations, Alachua County, Florida, USA.
Site Latitude and longitude
Evergreen Cemetery (EC) 29°37'44.18"N, 82°19'05.75"W
Hogtown Creek (HC) 29°41'53.15"N, 82°20'36.23"W
Newnan’s Lake (NL) 29°37'54.62"N, 82°12'14.47"W
Table 4-2. Field experiment biomass and leaf area (means) of harvested Ardisia crenata individuals in the mowed and unmowed fields at subsequent dates when herbicide applications were administered, across the three sites in Alachua County, Florida, USA.
Application date
Treatment Leaf Area (cm2)
Leaf (g)
Stem (g)
Root (g)
Flower & Fruit (g)
October 2009 Cut 981.2 5.6 4.2 22.1 0.0
Not Cut 1836.1 11.7 18.2 37.5 9.3
January 2010 Cut 949.8 5.8 4.6 26.3 0.0
Not Cut 1267.1 8.3 14.5 28.0 6.2
April 2010 Cut 857.4 5.7 5.4 26.3 0.2
Not Cut 1187.9 8.7 18.2 41.9 5.2
July 2010 Cut 887.4 9.7 9.1 38.2 2.1
Not Cut 1108.3 11.6 26.2 46.1 7.7
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Table 4-3. Greenhouse experiment biomass and leaf area (means) of harvested Ardisia crenata individuals grown under sun and shade light treatments in the greenhouse for the April and October 2011 herbicide application experiments across the three sites in Alachua County, Florida, USA.
Application date
Light Leaf Area (cm2)
Leaf (g)
Stem (g)
Root (g)
Flower & Fruit (g)
April Sun 432.3 5.6 1.8 23.6 0.0
Shade 493.8 3.8 1.1 10.6 0.0
October Sun 139.0 1.8 0.5 2.8 0.0
Shade 194.9 1.5 0.3 1.4 0.0
Table 4-4. Linear mixed model results for root starch concentration of Ardisia
crenata as a function of mowing and herbicide application date across the three sites in Alachua County, Florida, USA.
df X2-value P-value
Mowing 1 26.6 P<0.001
Application date 3 135.9 P< 0.001
Mowing*Application date 3 2.8 P= 0.42
Table 4-5. Linear mixed model results for root simple sugar concentration of
Ardisia crenata as a function of mowing and herbicide application date across the three sites in Alachua County, Florida, USA.
df X2-value P-value
Mowing 1 13.3 P<0.001
Application date 3 105.0 P< 0.001
Mowing*Application date 3 0.6 P= 0.90
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Table 4-6. Linear mixed model results for herbicide efficacy index for adult plants after 6 and 12 months following the four herbicide application dates in the mowed and unmowed fields across the three sites in Alachua County, Florida, USA.
df X2-value P-value
Application date 3 115.4 P < 0.001
Mowed 1 16.5 P < 0.001
Month After treatment (MAT) 1 0.2 P = 0.63
Application date * Mowed 3 20.7 P < 0.001
Table 4-7. Linear mixed model results for the herbicide efficacy index for
seedlings after 6 and 12 months after the four herbicide application dates in the mowed and unmowed fields across the three sites in Alachua County, Florida, USA.
df X2-value P-value
Application date 3 65.4 P < 0.001
Mowed 1 99.5 P < 0.001
Month After treatment (MAT) 1 64.5 P < 0.001
Application date * Mowed 3 10.3 P = 0.02
Table 4-8. Analysis of variance results for root starch concentration of Ardisia
crenata plants grown under low and high light treatments in the greenhouse for the April and October 2011 experiments in Alachua County, Florida, USA.
df F-value P-value
Light 1 94.4 P<0.001
Experiments 1 2.1 P= 0.05
Light*Experiments 1 3.4 P= 0.09
83
Table 4-9. Analysis of variance results for root simple sugar concentration of Ardisia crenata plants grown under low and high light treatments in the greenhouse for the April and October 2011 experiments in Alachua County, Florida, USA.
df F-value P-value
Light 1 4.3 P=0.06
Experiments 1 4.8 P= 0.05
Light*Experiments 1 0.01 P= 0.91
84
Table 4-10. Analysis of variance results for radioactivity of 14C triclopyr in Ardisia crenata plants grown under low and high light treatments at 1, 4, and 7 days after herbicide treatment (DAT) for the April 2011 greenhouse experiment in Alachua County, Florida, USA.
Response variable Factors df F-value P-value
Total Light 1 3.1 P=0.09
DAT 2 0.8 P= 0.45
Light*DAT 2 1.6 P= 0.21
Leaf water-wash Light 1 0.1 P=0.82
DAT 2 5.8 P= 0.01
Light*DAT 2 0.8 P= 0.44
Leaf acetone-wash Light 1 3.9 P=0.06
DAT 2 3.1 P= 0.06
Light*DAT 2 1.5 P= 0.25
Absorbed Light 1 0.3 P=0.59
DAT 2 3.5 P= 0.04
Light*DAT 2 1.5 P= 0.24
Treated leaf Light 1 0.04 P=0.84
DAT 2 2.3 P= 0.13
Light*DAT 2 1.3 P= 0.29
Translocated Light 1 1.0 P=0.34
DAT 2 3.7 P= 0.04
Light*DAT 2 1.1 P= 0.36
85
Table 4-11. Analysis of variance results for radioactivity of 14C triclopyr in Ardisia crenata plants grown under low and high light treatments at 7, 14, and 21 days after herbicide treatment (DAT) for the October 2011 greenhouse experiment in Alachua County, Florida, USA.
Response variable Factors df F-value P-value
Total Light 1 0.1 P=0.79
DAT 2 3.9 P= 0.04
Light*DAT 2 0.4 P= 0.68
Leaf water-wash Light 1 0.01 P=0.93
DAT 2 3.4 P= 0.049
Light*DAT 2 0.02 P= 0.98
Leaf acetone-wash Light 1 1.6 P=0.21
DAT 2 0.8 P= 0.45
Light*DAT 2 0.7 P= 0.49
Absorbed by the plant Light 1 0.2 P=0.68
DAT 1 2.8 P= 0.08
Light*DAT 1 0.65 P= 0.53
Absorbed by the treated leaf
Light 1 5.7 P=0.03
DAT 1 0.3 P= 0.77
Light*DAT 1 0.9 P= 0.41
Translocated Light 1 0.7 P=0.40
DAT 2 2.9 P= 0.07
Light*DAT 2 0.4 P= 0.64
86
Table 4-12. Radioactivity (mean and standard errors of the percent of 14C-labeled triclopyr applied) for leaf water-wash, total absorbed, the treated leaf, and translocation, which showed significant effects on days after treatments (DAT) with herbicide in the April 2011 experiment (Table 4-9) in Alachua County, Florida, USA. Different superscript letters indicate significant difference within a column by post-hoc Tukey multiple comparisons.
Days after herbicide application Leaf water-wash Absorbed Translocated
1 day 60.1 (1.9) a 3.4 (0.4) a 1.2 (0.4) a
4 days 56.7 (2.3) ab 6.1 (2.5) ab 2.0 (0.4) ab
7 days 47.4 (3.5) b 7.0 (2.8) b 2.7 (1.0) b
Table 4-13. Radioactivity (mean and standard errors of the percent of 14C-labeled triclopyr applied) for the total recovery and leaf wash, which showed significant effects on days after treatment (DAT) with herbicide in the October 2011 experiment (Table 4-10) in Alachua County, Florida, USA. Different superscript letters indicate significant difference within a column by post-hoc Tukey multiple comparisons.
Days after herbicide application Total recovery Leaf water-wash
7 day 99.6 (4.8) a 78.2 (10.4)
14 days 88.8 (2.5) ab 69.1 (2.4)
21 days 84.5 (3.7) b 45.8 (10.3)
Table 4-14. Radioactivity (mean and standard error of the percent of 14C-labeled triclopyr applied) found in the treated leaf following in the October 2011 experiment in Alachua County, Florida, USA. Overall means for the two light treatments which significantly differed (Table 4-10), across the three days after herbicide treatments (DAT).
Light treatment Treated leaf
Sun 9.7 (3.3) a
Shade 14.6 (5.0) b
87
Table 4-15. The results of analysis of variance for radioactivity of 14C triclopyr translocated to different plant organs (leaves, stems, roots, meristems) of Ardisia crenata plants grown under low and high light treatments at 1, 4, and 7 days after herbicide treatment (DAT) in the April 2011 experiment in Alachua County, Florida, USA.
Factors df F-value P-value
Light 1 1.6 P = 0.22
DAT 2 5.9 P = 0.004
Plant part 3 21.8 P < 0.001
Light * DAT 2 1.7 P = 0.18
Light * Plant part 3 3.6 P =0.02
DAT * Plant part 6 1.7 P = 0.13
Light * DAT * Pant part 6 0.7 P = 0.67
Table 4-16. Radioactivity (mean and standard errors of the percent of 14C-labeled triclopyr applied) found in leaves, meristems, stems and roots in the April 2011 experiment (Table 4-14) in Alachua County, Florida, USA.. Overall means across the three days after herbicide treatments (DAT) for the two light treatments which significantly differed (Table 4-10). Different superscript letters indicate significant difference by post-hoc Tukey multiple comparisons within each light treatment.
Light treatment
Leaves Meristems Stems Roots
Sun 0.35 (0.03) ab 0.14 (0.05) a 1.2 (0.06) ab 1.4 (0.3) c
Shade 0.46 (0.11) ab 0.14 (0.05) a 0.36 (0.1) ab 0.79 (0.13) b
88
Table 4-17. Radioactivity (mean and standard errors of the percent of 14C-labeled triclopyr applied) found across the four plant parts, which differed significantly among days after treatment (DAT) in the April 2011 greenhouse experiment (Table 4-14) in Alachua County, Florida, USA.. Different superscript letters indicate significant difference by post-hoc Tukey multiple comparisons.
Days after herbicide application Overall recovered
1 day 0.31 (0.11) a
4 days 0.50 (0.07) ab
7 days 0.68 (0.12) b
Table 4-18. The results of analysis of variance for radioactivity of 14C triclopyr translocated in Ardisia crenata plants grown under low and high light treatments at 7, 14, and 21 days after herbicide treatment (DAT) in the October 2011 greenhouse experiment in Alachua County, Florida, USA.
Factors df F-value P-value
Light 1 1.7 P = 0.20
DAT 2 6.6 P = 0.002
Plant part 3 2.2 P = 0.09
Light * DAT 2 1.0 P = 0.37
Light * Plant part 3 0.15 P =0.93
DAT * Plant part 6 0.7 P = 0.62
Light * DAT * Pant part 6 0.6 P = 0.74
89
Table 4-19. Radioactivity (mean and standard errors of the percent of 14C-labeled triclopyr applied) found across the four plant parts, which differed significantly among days after treatment (DAT) in the October 2011 greenhouse experiment (Table 4-17) in Alachua County, Florida, USA.. Different superscript letters indicate significant difference by post-hoc Tukey multiple comparisons.
Days after herbicide application All plant parts
7 day 2.1 (0.9) a
14 days 1.5 (0.2) a
21 days 5.4 (1.1) b
90
Figure 4-1. Schematic of proposed mechanism of carbohydrate movement in a forest understory evergreen plant in relation to seasonal light availability. In the Summer – Fall (June – August) period light levels in the understory are reduced (depicted by the size of the open arrow) and movement (black arrows) of stored carbohydrates from the roots to aboveground plant parts. In the Winter – Spring (December – April) fall period light increases with leaf and the plant exploit greater light availability and excess carbohydrate are translocated to the roots.
SUMMER WINTER
Carbohydrate
Light Light
Shoot & leaf production
+shoot
+
Root storage +
91
Figure 4-2. Herbicide field experiment setup: A) Blocks consisted of 11.75 by 4.25 m area, subdivided into 25 plots. B) Each plot was 0.75 by 0.75 m with 0.5 m of separation between plots; Five treatments (four herbicide application times and a control) were randomly applied to each plot with a total of 5 replicates per treatment. Experiment was conducted from April, 2009 to July 2011 in Alachua County, Florida, USA.
A) Block
B) Plot
Plot
92
Figure 4-3. Examples of field experiment plots with herbicide barrier in Alachua County, Florida, USA. A) Unmowed plot with herbicide barrier. B) Mowed plot with herbicide barrier and C) close-up view of mowed plot with barrier.
93
Figure 4-4. Examples of responses to October herbicide application (field experiment) measured as Ardisia crenata cover in plots at Hogtown Creek (A-C; unmowed plot) and Newnan’s Lake (D-F, mowed plot). Plot prior to herbicide application (A and D), 6 months after treatment (B and E), and 12 months after treatment (C and F), Alachua County, Florida, USA.
94
Starch Sugar
200
300
400
500
600
●
●
●
●
●
●●
●
Oct−2009 Jan−2010 Apr−2010 Jul−2010 Oct−2009 Jan−2010 Apr−2010 Jul−2010
Application date (Month−Year)
Glu
co
se
equiv
ale
nt
con
centr
atio
n (
mg
g-1)
Mowed
No
Yes
Figure 4-5. Boxplots of seasonal total non-structural carbohydrates (TNC, starch and simple sugars) at each herbicide application date in the field for mowed and unmowed adult Ardisia crenata plants across the three sites in Alachua County, Florida, USA. Stars are means. The top and the bottom of each box correspond to the first and third quartiles (the 25th and 75th percentiles). The median is indicated by the thick horizontal line. Whiskers indicate the highest/lowest values that is within 1.5 * IQR of the box boarder, where IQR is the inter-quartile range, or distance between the first and third quartiles. Black dots are outliers.
95
6 MAT 12 MAT
0.5
0.6
0.7
0.8
0.9
1.0
0.5
0.6
0.7
0.8
0.9
1.0
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●
●●
●
●●●
●
●
●●●
●
●
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●
●
●
●
●
●
●
●
Adult
Seedlin
g
October January April July October January April July
Application Date
Pro
po
rtio
n r
em
ove
d
Mowed
No
Yes
Figure 4-6. Boxplots of herbicide efficacy after 6 and 12 months after herbicide treatment application date in the field for mowed and unmowed adult A. crenata plants. Herbicide efficacy measured as relative amount of A. crenata seedling or adults removed from each plot and summarized across the three sites in Alachua County, Florida, USA. Stars are means. The top and the bottom of each box correspond to the first and third quartiles (the 25th and 75th percentiles). The median is indicated by the thick horizontal line. Whiskers indicate the highest/lowest values that is within 1.5 * IQR of the box boarder, where IQR is the inter-quartile range, or distance between the first and third quartiles. Black dots are outliers.
96
Starch Sugar
100
200
300
400
500
●
●
●
●
Apr−2011 Oct−2011 Apr−2011 Oct−2011
Application date (Month−Year)
Glu
co
se
equiv
ale
nt
con
centr
atio
n (
mg
g-1)
Light
Shade
Sun
Figure 4-7. Boxplots of seasonal total non-structural carbohydrates (TNC, starch and simple sugars) at each herbicide application for shaded and sun A. crenata plants in the greenhouse experiment in Alachua County, Florida, USA. Star are means and colored box height includes range between first and third quartiles and thick horizontal line is the median. The top and the bottom of each box correspond to the first and third quartiles (the 25th and 75th percentiles). The median is indicated by the thick horizontal line. Whiskers indicate the highest/lowest values that is within 1.5 * IQR of the box boarder, where IQR is the inter-quartile range, or distance between the first and third quartiles. Black dots are outliers.
97
CHAPTER 5 CONCLUSIONS
The main objective of this research is to explore the phenomenon of forest
exotic invasive plants in a more comprehensive and integral fashion through
assessment of their impact on cover and richness of understory species,
description of mechanisms by which exotic plants competitively suppress native
species, and evaluation of efficacy of herbicides as a common method of control.
The results suggest that invasive shrub A. crenata in closed-canopy
hardwood hammock forests of Florida resulted in the reduction of understory
species richness by 25%, while the total understory cover of native species was
lowered by 34% with significant difference found in all growth forms (trees, shrub,
vines, and herbs) compared to areas uninvaded by A. crenata. Shading by A.
crenata is an important mechanism by which it can suppress seedlings overstory
species. Such effect can potentially have a significant effect on the regeneration
of trees. The survival and growth of seedlings of two oak species, Quercus
virginiana and Q. hemisphaerica, in the understory decreased in the presence of
A. crenata after two growth seasons. The reduction in seedling recruitments of
overstory canopy species due to A. crenata invasions can potentially impact
forest structure in a long term. Hence, for rapid recovery of native species
diversity, removal of A. crenata may be complemented with enrichment planting
of seedlings of native species.
Herbicides, such as triclopyr, are a good method for of control of A. crenata,
but efficacy may be compromised by weather conditions, such as cold
temperature and rain events. Recovery of A. crenata population from seed
98
germination is a significant concern in herbicide-treated area; seed germination
post-treatment had a significant contribution to regeneration of A. crenata at 6-12
months after the single herbicide treatment. Thus, a retreatment of sites would be
essential to obtain desired control in highly infested sites.
Further research will be needed to evaluate how seedlings of other
overstory tree species and rare native understory species in hardwood
hammocks are impacted by A. crenata. Development of methods to improve
efficacy of herbicides will be particularly useful for controlling A. crenata, by
enhancing herbicide entry through leaves. Finally, the management decisions
should consider the evaluation of the economic impacts of A. crenata and public
willingness to accept employment of particular methods for controlling A. crenata
and restore impacted ecosystems.
99
APPENDIX A ADDITIONAL TABLES AND FIGURES FOR CHAPTER 2
Table A-1. Percent cover (mean) of native and exotic species for forest understory in the presence and absence of Ardisia crenata and overstory of all plots (n=157), Alachua County, Florida, USA.
Species name Origin Absent Present Overstory
Petiveria alliacea L. Native 3.671 3.560 0.000
Smilax sp. Native 3.119 1.489 0.185
Quercus hemisphaerica W. Bartram ex Willd. Native 1.934 0.258 3.408
Toxicodendron radicans (L.) Kuntze Native 1.364 1.398 0.127
Sabal palmetto (Walter) Lodd. Ex Schult. & Schult. f.
Native 1.247 0.833 2.261
Carex willdenowii Schkuhr ex Willd. Native 1.214 0.433 0.000
Prunus caroliniana (Mill.) Aiton Native 1.138 0.033 0.000
Quercus pumila Walter Native 1.137 0.000 0.000
Parthenocissus quinquefolia (L.) Planch. Native 1.073 0.775 0.064
Carpinus caroliniana Walter Native 1.060 0.207 17.325
Hedera helix L. Exotic 0.973 0.036 0.000
Chasmanthium laxum (L.) Yates Native 0.868 0.095 0.000
Cornus foemina Mill. Native 0.863 0.024 0.127
Vitis rotundifolia Michx. Native 0.832 1.021 3.357
Verbesina virginica L. Native 0.808 1.902 0.000
Oplismenus hirtellus (L.) P. Beauv. Native 0.748 0.232 0.000
Quercus nigra L. Native 0.678 0.274 14.057
Pinus glabra Walter Native 0.638 0.000 3.217
Celtis laevigata Willd. Native 0.595 0.088 14.344
Rumex hastatulus Baldwin Native 0.458 0.173 0.000
Ostrya virginiana (Mill.) K. Koch Native 0.384 0.652 20.911
Dichanthelium spp. Native 0.367 0.119 0.000
Elephantopus elatus Bertol. Native 0.351 0.148 0.000
Bignonia capreolata L. Native 0.342 0.202 0.064
Carya glabra (Mill.) Sweet Native 0.300 0.231 10.401
Arisaema dracontium (L.) Schott Native 0.274 0.140 0.000
Lamium amplexicaule L. Exotic 0.271 0.871 0.000
Ruellia caroliniensis (J.F. Gmel.) Steud. Native 0.182 0.006 0.000
Salvia coccinea Buc'hoz ex Etl. Native 0.173 0.056 0.000
Campsis radicans (L.) Seemann ex Bureau Native 0.164 0.143 0.115
Cinnamomum camphora (L.) J. Presl Exotic 0.151 0.000 3.121
Serenoa repens (W. Bartram) Small Native 0.149 0.083 0.000
Quercus minima (Sarg.) Small Native 0.137 0.000 0.000
100
Table A-1. Continued.
Species name Origin Absent Present Overstory
Mitchella repens L. Native 0.134 0.095 0.000
Tradescantia fluminensis Vell. Exotic 0.123 0.571 0.000
Sanicula canadensis L. Native 0.122 0.110 0.000
Acer rubrum L. Native 0.114 0.019 0.000
Dioscorea floridana Bartlett Native 0.103 0.020 0.000
Gelsemium sempervirens (L.) W.T. Aiton Native 0.101 0.069 0.127
Galium pilosum Aiton Native 0.084 0.010 0.000
Euonymus americanus L. Native 0.080 0.010 0.000
Galactia volubilis (L.) Britton Native 0.075 0.000 0.000
Prunus serotina Ehrh. Native 0.074 0.500 0.159
Viola sororia Willd. Native 0.074 0.085 0.000
Matelea sp. Native 0.068 0.274 0.000
Callicarpa americana L. Native 0.047 0.369 0.465
Asplenium platyneuron (L.) Britton et al. Native 0.045 0.006 0.000
Asclepias sp. 0.041 0.005 0.000
Diospyros virginiana L. Native 0.041 0.024 0.000
Erythrina herbacea L. Native 0.041 0.000 0.000
Oxalis spp. 0.041 0.054 0.000
Rubus argutus Link Native 0.040 0.124 0.000
Entodon sp. {moss} Native 0.034 0.000 0.000
Saururus cernuus L. Native 0.032 0.000 0.000
Quercus michauxii Nutt. Native 0.029 0.000 0.975
Viola walteri House Native 0.029 0.005 0.000
Ampelopsis arborea (L.) Koehne Native 0.027 0.000 0.000
Liquidambar styraciflua L. Native 0.027 0.462 12.567
Ulmus americana L. Native 0.027 0.000 2.611
Pinus palustris Mill. Native 0.026 0.005 0.478
Morus rubra L. Native 0.021 0.018 1.038
Ilex opaca Aiton Native 0.019 0.000 0.318
Sonchus asper (L.) Hill Exotic 0.018 0.030 0.000
Dryopteris ludoviciana (Kunze) Small Native 0.016 0.000 0.000
Viburnum nudum L. Native 0.016 0.018 0.573
Distichum sp. {moss} Native 0.014 0.000 0.000
Gordonia lasianthus (L.) J.Ellis Native 0.014 0.071 0.000
Stellaria media L. Vill. Exotic 0.014 0.012 0.000
Acer saccharum Marshall Native 0.012 0.036 2.898
101
Table A-1. Continued.
Species name Origin Absent Present Overstory
Fern sp. 0.012 0.179 0.000
Arisaema triphyllum (L.) Schott Native 0.007 0.000 0.000
Ilex vomitoria Aiton Native 0.007 0.000 0.000
Mnium sp. {moss} Native 0.007 0.000 0.000
Polygonum spp. 0.007 0.071 0.000
Ambrosia artemisiifolia L. Native 0.005 0.000 0.000
Baccharis glomeruliflora Pers. Native 0.005 0.000 0.000
Botrychium biternatum (Savigny) Underw. Native 0.005 0.000 0.000
Digitaria sp. 0.005 0.000 0.000
Hypericum sp. Native 0.005 0.000 0.000
Magnolia grandiflora L. Native 0.005 0.000 10.255
Pinus elliottii Engelm. Native 0.005 0.000 0.159
Acer negundo L. Native 0.000 0.026 0.000
Bambusa sp. Exotic 0.000 0.238 1.051
Cercis canadensis L. Native 0.000 0.012 0.000
Cornus florida L. Native 0.000 0.238 0.318
Eupatorium sp. 0.000 0.000 0.000
Juglans nigra L. Native 0.000 0.060 0.478
Krigia virginica (L.) Willd. 0.000 0.000 0.000
Lonicera sempervirens L. Native 0.000 0.018 0.000
Lyonia lucida (Lam.) K. Koch Native 0.000 0.143 0.032
Osmunda cinnamomea L. Native 0.000 0.071 0.000
Persea borbonia (L.) Spreng. 0.000 0.000 0.000
Persea palustris (Raf.) Sarg. Native 0.000 0.030 0.318
Physalis sp. 0.000 0.000 0.000
Quercus virginiana Mill. Native 0.000 0.048 14.567
Rhapidophyllum hystrix (Pursh) H. Wendl. & Drude ex Drude
0.000 0.000 0.000
Rhododendron spp. Native 0.000 0.143 0.764
Stachys floridana Shuttlew. ex Benth. Native 0.000 0.043 0.000
Fraxinus caroliniana Mill. Native 0.000 0.000 0.510
Nyssa sylvatica var. biflora (Walter) Sarg. Native 0.000 0.000 0.478
Pinus taeda L. Native 0.000 0.000 1.688
Tilia americana L. Native 0.000 0.000 0.127
Ulmus alata Michx. Native 0.000 0.000 2.197
Osmanthus americanus (L.) Benth. & Hook. f.ex A. Gray
Native 0.000 0.000 0.987
102
Table A-1. Continued.
Species name Origin Absent Present overstory
Vaccinium sp. L.* Native
Native 0.000 0.000 0.064
Ageratina aromatica (L.) Spach* Native 0.000 0.004 0.000
Carex digitalis Willd.* Native 0.222 0.139 0.000
Melothria pendula L.* Native 0.090 0.000 0.000
Poinsettia heterophylla (L.) Klotzsch & Garcke ex Klotzch*
Native 0.017 0.011 0.000
Trichostema dichotomum L.* Native 0.023 0.000 0.000
total number of species 85 spp 41 spp
number of species that occurred only in the fall
(+5 spp) (+2 spp)
number of Exotic species fall or spring (+8 spp) (+2 spp)
* Native species absent in the overstory.
103
CP MC NL PR SF
−80
−60
−40
−20
0
20
●●●●●●● ●●●●●●● ●●●●● ●●●●●●●● ●●●●●●●●
−40 −20 0 20 40 −40 −20 0 20 40 −40 −20 0 20 40 −40 −20 0 20 40 −40 −20 0 20 40
Distance from origin (m)
Dis
tan
ce
fro
m o
rigin
(m
)
Zone
Invaded
Uninvaded
Figure A-1. Experimental setup for each site, showing shape and size of invaded zone calculated polygon created by
distances from the origin (black dot) of the first five plots (A. crenata invaded) of each transect at the five study sites in Alachua County, Florida, USA. Invaded zones areas are: CP = 510.7 m2, MC = 550.3 m2, NL = 3,084.8 m2, PR = 133.2 m2, SF = 989.7 m2.
104
APPENDIX B ADDITIONAL FIGURES FOR CHAPTER 3
Time (Month−Year)
Tem
pera
ture
(C
)
0
10
20
30
Planting Census 1 Census 2
Mar−09 Jun−09 Sep−09 Dec−09 Mar−10 Jun−10 Sep−10 Dec−10 Mar−11
Monthly
Mean
Mean Maximum
Mean Minimum
Extreme
Figure B-1. Monthly temperatures during study period taken from nearest
meteorological station to study sites (29° 40' 59.988” N, 82° 16' 0.012" W) Gainesville, Florida. Black line is monthly mean based on daily means. Red line is mean monthly maximum based on daily maximum. Blue line is the mean monthly minimum temperature based on daily minimum temperature. Open circles are extreme temperatures experienced during the month. Grey vertical lines indicate initial planting or census dates (240 and 600 days).
105
Time (Month−Year)
Pre
cip
ita
tion
(m
m)
0
50
100
150
200
Planting Census 1 Census 2
Mar−09 Jun−09 Sep−09 Dec−09 Mar−10 Jun−10 Sep−10 Dec−10 Mar−11
Year
2009
2010
2011
Figure B-2. Monthly precipitation during study period taken from nearest meteorological
station (29° 40' 59.988” N, 82° 16' 0.012" W) Gainesville, Florida. Grey vertical lines indicate initial planting or census dates (240 and 600 days).
106
Figure B-3. Mean monthly temperatures during 27 years (1984 to 2011) at Gainesville,
Florida (29° 40' 59.988” N, 82° 16' 0.012" W). Red circles are mean monthly maximum temperatures, triangles are mean monthly temperatures, and blue squares are mean monthly minimum temperatures. Lines are linear regressions and shaded areas are 95% confidence intervals for each month. Black filled points are temperatures during oak seedling experiment (April 2009 to December 2011).
107
Figure B-4. Monthly precipitation during 27 years (1984 to 2011) at Gainesville, Florida
(29° 40' 59.988” N, 82° 16' 0.012" W). Lines are linear regressions and shaded areas are 95% confidence intervals for each month. Black filled points are precipitation during oak seedling experiment (April 2009 to December 2010).
108
Q. hemisphaerica Q. virginiana
0
5
10
15
20
25
●
●
●
●
n= 19 n= 35 n= 9 n= 5
●
●
●
n= 20 n= 46 n= 22 n= 5
Initial
Abs
ent
Pull−
down
No
Pull−
down
Initial
Abs
ent
Pull−
down
No
Pull−
down
Bio
ma
ss (
g)
Figure B-5. Box plot of seedling (Quercus hemisphaerica and Q. virginiana) harvest total biomass for initial harvest, plots without Ardisia crenata (Absent), plots with A. crenata canopies pulled down (Pull-down), and plots with A. crenata canopy intact (No Pull-down). Stars are means. Data excludes values of dead individuals. The top and the bottom of each box correspond to the first and third quartiles (the 25th and 75th percentiles). The median is indicated by the thick horizontal line. Whiskers indicate the highest/lowest values that is within 1.5 * IQR of the box boarder, where IQR is the inter-quartile range, or distance between the first and third quartiles. Black dots are outliers.
109
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20
40
60
80
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●●●
●●●
●●●
BA EC HC NL
Site
Lig
ht (%
PA
R)
Ardisia
Absent
Pull−down
No Pull−down
Figure B-6. Light availability for plots without Ardisia crenata (Absent), plots with A. crenata canopies pulled down (Pull-down), and plots with A. crenata canopy intact (No Pull-down). Light measured as percent photosynthetically active radiation (PAR) relative to an open area at 35 cm (average height of seedlings) above the soil surface. Stars are means. Measurements were taken under clear-sky conditions from 11 am to 3pm, across five sites in Alachua County, Florida, USA.
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APPENDIX C ADDITIONAL FIGURES FOR CHAPTER 4
Figure C-1. Mean monthly temperatures during 27 years (1984 to 2011) at Gainesville,
Florida (29° 40' 59.988” N, 82° 16' 0.012" W). Red circles are mean monthly maximum temperatures, triangles are mean monthly temperatures, and blue squares are mean monthly minimum temperatures. Lines are linear regressions and shaded areas are 95% confidence intervals for each month. Black points are temperatures during herbicide field experiment period (April 2009 to December 2011).
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Figure C-2. Monthly precipitation during 27 years (1984 to 2011) at Gainesville, Florida (29° 40' 59.988” N, 82° 16' 0.012" W). Lines are linear regressions and shaded areas are 95% confidence intervals for each month. Black points are precipitation during herbicide field experiment (April 2009 to December 2010).
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BIOGRAPHICAL SKETCH
Gerardo Celis was born in Costa Rica. Upon conclusion of high school, he initiated
a program in environmental studies at the University of British Columbia in Vancouver.
After one year there, he returned to Costa Rica, where he completed his undergraduate
studies in biology at Universidad Latina. His undergraduate research, entitled: “Seed
germination of two sympatric palm species: Chamaedorea tepejilote Liebm. and
Chamaedorea costaricana Oerst (Arecaceae) in natural conditions and in a nursery,”
was the result of a pro bono collaboration with the National Museum of Costa Rica.
After concluding his undergraduate studies, he taught biostatistics at the same
university and was selected by the Organization for Tropical Studies (OTS) to
participate in the Research Experiences for Undergraduates (REU) program at La Selva
biological station. The research conducted was entitled: “Do patterns of seed
germination and seedling biomass allocation reflect a shade tolerance syndrome in
Gnetum leybodii Tul. (Gnetaceae)?” Later on, he became a teaching assistant, under
Professor Luis Diego Gómez, for OTS’ course “Plantains, iguanas and shamans: an
introduction to field ethnobiology.” At this point in his career, he felt the need to develop
a broader understanding of environmental processes by incorporating the
interdisciplinary dimension. Thus, he decided to pursue a master’s in interdisciplinary
ecology with emphasis on tropical conservation and development at the University of
Florida (UF); he graduated in 2007 with a thesis entitled “Restoring abandoned pasture
land with native tree species in Costa Rica: an ecophysiological approach to species
selection.” He continued to enroll at UF to pursue a Ph.D. in interdisciplinary ecology
with emphasis on forest resources and conservation and concluded in the fall of 2012.
