cogongrass (imperata cylindrica (l.) beauv.) in louisiana

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Cogongrass (Imperata cylindrica (L.) Beauv.) inLouisiana: Cause and ConsequenceLorissa A. Radunzel-DavisLouisiana State University and Agricultural and Mechanical College, [email protected]

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COGONGRASS (IMPERATA CYLINDRICA (L.) BEAUV.) IN

LOUISIANA: CAUSE AND CONSEQUENCE

A Thesis

Submitted to the Graduate Faculty of the

Louisiana State University and

Agricultural and Mechanical College

in partial fulfillment of the

requirements for the degree of

Master of Science

in

The Department of Biological Sciences

by

Lorissa Radunzel-Davis

B.S., University of Wisconsin-Madison, 1996

A.A.S., Argosy University, 2005

August 2019

ii

I would like to dedicate this to my husband Jeff and sons William and Matthew Davis. They

have all been very supportive of my endeavor and have taken on additional tasks enabling me to

focus on finishing my degree.

iii

ACKNOWLEDGEMENTS

This research could not have been done without the assistance of many people. I have

had this opportunity because Dr. Erik Aschehoug decided to take me on has his student and Dr.

Jim Cronin took me in when circumstances changed. Thank you also to Dr. Rodrigo Diaz for

serving on my committee. The Department of Biological Sciences support staff also answered

my questions and took care of the many small things that often go unnoticed.

There are many others I would like to thank. Dr. Haley Dozier directed me to Lee

Memorial Forest as a source of cogongrass. Without her, I would never have met the manager

Joe Nehlig who spent 2 days taking me throughout the property to mark cogongrass patches and

opened his files to me. Denise McKinney pointed me to cogongrass patches at Bogue Chitto

State Park. Chaper 2 could not have happened without the help of Caryn Davis collecting,

drying and shipping soil from Japan. I appreciate how responsive the Louisiana State University

greenhouse staff was to all my requests. My labmate Rachel Harman helped me tremendously

by being there to discuss items both related and unrelated to my work. I want to thank Honza

Čuda for being a sounding board and company in the lab for a semester. Finally, thank you to

undergraduates Daniel, Celine and Marshall along with Honza, Rachel and Joe Johnston for their

help during harvest and clean-up times. Many hands made light work.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS …………………………………………………….………. iii

ABSTRACT ……………………………………………………………….………….…. v

CHAPTER 1. INTRODUCTION TO COGONGRASS WITH EMPHASIS ON

LEE MEMORIAL FOREST ………………………………………………….…………. 1

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

1.2. Disturbance ……..……...………………………………………………...….. 3

1.3. Association with soil and land cover ……………..…………………………. 6

1.4. Spatial distribution .……………………………..………………………..….. 9

1.5. Conclusion ……………………………..…………...……………………… 12

1.6. References ...……..……………………………………………...…………. 13

CHAPTER 2. BIOGEOGRAPHIC EFFECTS OF SOIL MICROBES ON GROWTH

OF COGONGRASS ………………………………………………………………….… 16

2.1. Introduction ……………………...……..………………………………….. 16

2.2. Methods ………………………….…………………………...……………. 19

2.3. Results …………………………………………...………..……………….. 25

2.4. Discussion ………………………………………………………………..… 41

2.5. References ……………...………………………………………………..… 46

CHAPTER 3. COGONGRASS SOIL LEGACY EFFECTS ON TWO NATIVE

PLANTS ………………………………………………………………………….…….. 54

3.1. Introduction …………………….…..……………..………………….……. 54

3.2. Methods …………...………………..…………………………..………….. 56

3.3. Results ………….…….…….…..………………………………………….. 58

3.4. Discussion ...…………………...………………………..…………………. 59

3.5. References ……………………………...…………..……………………… 62

CHAPTER 4. CONCLUSION ………………………………………………………… 68

VITA …………………………………………………………………………………… 70

v

ABSTRACT

Cogongrass (Imperata cylindrica (L.) Beauv.), an invasive species from East Asia, is

found worldwide and is problematic in several countries. In the United States, it grows primarily

in the Southeast, reducing biodiversity by growing in dense patches and potentially causing

mortality and reducing value of native and planted pinelands due to a high burning temperature.

Using Lee Memorial Forest, a Louisiana State University AgCenter property in Washington

Parish as a study site, this thesis explores cogongrass in Louisiana with emphasis on soil

microbes and soil legacy effects on native plant species. Cogongrass populations at Lee

Memorial Forest were more likely to occur in management units with a prescribed burn, found

primarily in evergreen forest on both soil types on the property and have a clustered distribution.

Soil microbial community effects on cogongrass growth were compared in soil collected

from the native range in Japan and in soil collected from Louisiana. There was an initial release

from soil microbial pathogens with 1.4 times more aboveground biomass in live versus sterile

soil from the invaded range. In the native-range soil, 1.4 times more aboveground biomass was

produced in sterile soil than in live soil, indicating the presence of pathogens. Live soils were

reused to test how cogongrass induced alterations in microbial community affected subsequent

cogongrass growth. Aboveground biomass production in sterile soil was always greater than in

live soil, 1.2 times in Japanese soil and 2.2 times in Louisiana soil, indicating increased

pathogens. Cogongrass from two Louisiana populations responded similarly in aboveground

biomass production, however there were differences in the allocation of that biomass to leaves or

additional height, indicating variable invasive potential.

The second study investigated the cogongrass legacy remaining in soil after its removal

and the impact on subsequent plants. Two native plants, Schizachyrium scoparium and

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Arnoglossum ovatum, were grown in soil either previously containing cogongrass or having no

previous plant growth. Schizachyrium scoparium accumulated 2.9 times and A. ovatum 1.3

times more total biomass in cogon-free soil compared to cogon-exposed soil. Soil mitigation

techniques due to cogongrass soil legacy may be needed for optimal restoration success.

1

CHAPTER 1. INTRODUCTION TO COGONGRASS WITH EMPHASIS

ON LEE MEMORIAL FOREST

INTRODUCTION

Cogongrass (Imperata cylindrica (L.) Beauv.), is a clonal C4 perennial grass species that

has strong negative effects on native plant communities worldwide and is listed as one of the

world’s top 100 worst invasive species (Estrada and Flory 2015). There are five varieties of

cogongrass, with at least one variety successfully established on all continents except Antarctica

(Hubbard et al. 1944, Dozier et al. 1998). It is found on a wide variety of soil types, from heavy

clay to sandy soils and in a wide range of nutrient conditions (Jose et al. 2002, Bryson et al.

2010). It is native to Southeast Asia and was introduced into the United States in 1912 in

Alabama as packing material and to Mississippi and Florida in 1921 for study as a possible

forage crop (Hubbard et al. 1944, Tabor 1952, Dozier et al. 1998). By 1952, it was recognized as

a concern and, although a study and an eradication plan were suggested, little was done (Dickens

1974). Now cogongrass is found across the Southeast United States (Dozier et al. 1998).

Cogongrass has strong negative ecological and agricultural impacts throughout the world

(Hubbard et al., 1944, Brook, 1989, Chikoye et al., 2005, Dozier et al., 1998). When left

unmanaged, it quickly becomes the dominant species, growing in tall, dense patches, and

outcompeting native species, with the help of allelopathic chemicals (Estrada and Flory 2015,

Hagan et al. 2013, Xuan et al. 2009, Brewer 2008). It has a dense mat of roots and pointed

rhizomes that can penetrate other root systems (Holly and Ervin, 2006). Cogongrass causes

significant decreases in pine root growth, height and biomass compared to areas with native or

no understory vegetation (Daneshgar et al. 2008). Pine seedlings were smaller and had less

nitrogen content in areas where cogongrass was growing than seedlings growing without

2

cogongrass (Daneshgar and Jose 2009a). Cogongrass has significant impacts on phosphorous

and potassium availability and alters pH of the soil (Brewer and Cralle 2003).

In Louisiana, cogongrass has been present since 1990, when a survey and eradication

plan were enacted (Bryson and Carter 1993) and is primarily found in eastern Louisiana

(Loewenstein and Miller 2007). Lee Memorial Forest is an LSU AgCenter property of more

than 1200 acres in Washington Parish (30.874, -89.991). The area is managed for research as

well as timber harvest. There are areas of hardwood bottomlands, longleaf pine restoration and

timber plantations. A patch of cogongrass was first discovered in 2002 and the land manager has

been monitoring and treating it biannually. Despite such intense management, new patches are

discovered every year (Figure 1.1, J. Nehlig personal communication).

Figure 1.1. Number of newly discovered cogongrass patches found annually in Lee Memorial

Forest, Washington Parish, Louisiana. Data collected by Joe Nehlig, land manager of Lee

Memorial Forest.

3

DISTURBANCE

As with many other invasive species, cogongrass is thought to be reliant on disturbance

to be able to establish a population (Catford et al. 2009, Müller et al. 2016), though it is also

found in less disturbed areas (Dozier et al. 1998). Müller et al. (2016) found that for both native

and exotic plants, disturbance was more important for establishment success than pathogens and

herbivores. Cogongrass spreads by seeds and by rhizome production. Anthropogenic dispersal

from moving soil contaminated with rhizomes is thought to be a predominant source of spread

(Dozier et al. 1998). A few studies have investigated disturbance and the spread of cogongrass.

Holzmueller and Jose (2012) studied cogongrass in the Blackwater River State Forest in the

Florida panhandle. They found that it was more than twice as likely to be in burned versus

unburned areas. They also found a positive linear relationship between post-hurricane salvage

biomass and cogongrass presence. Ervin and Holly (2011a) surveyed up to 90 meters from

roadsides in DeSoto National Forest in Mississippi. They found that cogongrass was found in

high proximity to roads, with few areas surveyed having cogongrass more than 30 meters from

the roadside. They also found that human-caused disturbance had a much stronger relationship

with the presence of cogongrass than did natural disturbance.

To investigate this at Lee Memorial Forest, I marked the location of 57 cogongrass

patches using a Trimble GPS unit in December 2016 and January 2017. In addition to location

data, annual maps of cogongrass patches and records of tree harvest and prescribed burning since

1998 were obtained from Lee Memorial Forest records. Management units at Lee Memorial

Forest had not been fully delineated in a digital format. A partially delineated map from the

Louisiana State University AgCenter was used along with paper maps to create a shapefile with

all Lee Memorial Forest management units. Using ArcMap 10.4, information about

4

management activities was joined to the respective units and overlayed with the location of the

cogongrass patches. Association of new cogongrass patches with timing of disturbance was

determined by calculating the time between the last burn or harvest in a management unit and

when the cogongrass patch was first observed. In addition, a chi-square analysis was done to see

if management activities were associated with the presence of cogongrass patches in those units.

It is possible that patches could have arisen from rhizomes moved by mechanical equipment.

Especially areas that are harvested, since the companies hired do not wash off equipment.

Timber harvesting happens periodically at Lee Memorial Forest and sections of

management units were trimmed or clear-cut in 2000, 2001, 2002, 2004, 2005, 2008, 2010, 2013

and 2016. There were 35 cogongrass patches (61%) in management units that had a harvest

prior to the discovery of a patch in that unit (Figure 1.2). Of those patches 55% were found

within 2000 days (5.5 years). Timber harvesting activity was not associated with cogongrass

patch presence (X2=2.87, p=0.09). This would seem to indicate that movement of rhizomes by

mechanical equipment is not driving cogongrass presence in Lee Memorial Forest.

Burning happens annually, though not in the same management units. There were 45

cogongrass patches (79%) found in units managed by prescribed burns. New cogongrass patches

were often found after burning, with 88% being found within 730 days (2 years) of the

prescribed burn (Figure 1.3). Prescribed burning is associated with cogongrass patch presence

(X2=7.86, p=0.01). The frequency of burning, and that it is a primary management tool in areas

with cogongrass may be a reason for this association. Most cogongrass patches (81%) are

located in areas that had some known management activity prior to discovery. However, 11

patches (19%) were in management units that had no prior management activities. Those few

could have germinated from windblown seed.

5

Figure 1.2. The timing of 35 cogongrass patch discoveries since the occupied management unit

had timber harvested using heavy equipment. The mean number of days before discovery was

2032 (5.6 years).

Figure 1.3. The timing of 45 cogongrass patch discoveries since the occupied management unit

had a prescribed burn. The mean number of days before discovery was 526 (1.4 years).

6

There are still unknowns such as the length of time a patch may be growing before it is

large enough to be noticed. It remains to be investigated if management practices, especially

prescribed burning, spread cogongrass, or if it is more likely to be noticed because of the

management activity. The location of these patches does not necessarily indicate a causal

relationship. In addition, there are also research activities that occur in Lee Memorial Forest in

addition to management by harvest and prescribed burning that could potentially spread

cogongrass.

ASSOCIATION WITH SOIL AND LAND COVER

The distribution of invasive species can be useful in predicting their continued spread by

analyzing environmental variables associated with the presence of the species. Analysis of a

distribution model for cogongrass found that in Alabama there was a strong correlation with soil

variables, whereas in Mississippi, there was a stronger correlation with tree canopy cover (Ervin

and Holly 2011b). A study using 3 soil types found in Mississippi (Mississippi Alluvial Plain,

Blackland Prairie and Pontotoc Ridge) found that the soil types were significantly different when

it came to cogongrass growth rate and distribution of biomass (Holly and Ervin 2007). The

nutrient-rich Mississippi Alluvial Plain soil was more productive in all areas of plant growth

measured, indicating that soils with higher nutrient contents are potentially more susceptible to

invasion and establishment (Holly and Ervin 2007). A more comprehensive study using soil

from 53 Mississippi counties covering a wide variety of soil characteristics showed that

cogongrass was able to successfully establish in many different soil types (Bryson et al. 2010).

Using ArcGIS 10.4 soil type and land cover was overlayed with the location of the

cogongrass patches. Soil type data with a 10-meter resolution were obtained from the United

7

States Geological Survey (gdg.sc.egov.usda.gov/) and land use/land cover data with a 30-meter

resolution from the Multi-Resolution Land Characteristics Consortium (www.mrlc.gov/data).

There are 10 different cover types in Lee Memorial Forest, with cogongrass found in 7 of

them (Figure 1.4). Over half (58%) of the cogongrass locations are found in the evergreen

forest, and another 26% are found in shrub/scrub habitat (Table 1.1). A chi-square goodness of

fit analysis of land cover confirmed that cogongrass is not evenly distributed across cover types,

so there could be a cover type association (X2=91.09, p<0.001). Though, this might be due to

differences in use of areas in the forest, since the hardwood area is not actively managed.

Figure 1.4. Land cover type and location of cogongrass patches on Lee Memorial Forest.

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Table 1.1. Cogongrass Patches on Lee Memorial Forest and associated cover type.

Number of Cogongrass Patches Vegetation Cover Type

33 Evergreen Forest

1 Deciduous Forest

3 Developed/Open Space

2 Woody Wetlands

2 Hay/Pasture

1 Herbaceous

15 Shrub/Scrub

There are two soil types found at Lee Memorial Forest (Figure 1.5). Ouachita-Jena-Bibb

soil has 40% of the cogongrass patches. This is an acidic soil characterized by a loamy surface

layer and subsoil, with a sandy substratum and is found on flood plains (Trahan et al 1997).

Tangi-Savannah-Ruston soil, containing the other 60% of cogongrass patches is also acidic, but

better drained and generally on higher slopes (Trahan et al 1997). A goodness of fit chi-square

analysis indicates cogongrass is not evenly distributed between the two soil types (X2=48.57,

p<0.001). This could be due to flooding in the woody wetlands, which covered 35% of the area

of Lee Memorial Forest. They are found predominantly on Ouachita-Jena-Bibb soil (94%). The

soil type and the cover type combination may work together to explain the distribution of

cogongrass in Lee Memorial Forest.

9

Figure 1.5. Soil type and location of cogongrass patches on Lee Memorial Forest.

SPATIAL DISTRIBUTION

I performed a hotspot analysis using a spatial weights matrix calculated using Delaunay

triangulation to visualize infestation intensity (Getis and Ord 1992, De Smith et al. 2018). To

examine the spatial distribution of cogongrass, data points were examined using Ripley’s K to

find any evidence of clustering. The spatial weights matrix was not used in the Ripley’s K

analysis. The 57 patches of cogongrass on Lee Memorial Forest could be divided into five

clusters. The areas to the North are a hotspot, where the cluster of cogongrass patches were all

10

near each other. There is an aggregation toward the South as well, where there is a coldspot

cluster, indicating that the patches are farther away from each other, but still have a clustered

distribution (Figure 1.6). The average number of neighbors used for the Delaunay triangulation

was 5.54 indicating that each patch was connected to an average of 9.73% of the total patches for

the cluster analysis. The Ripley’s K function showed slight but significant clustering when the

nearest neighbor was within 640 meters, but beyond that the distribution is not statistically

different from random. (Figure 1.7). This indicates that either wind is not dispersing seeds far, or

multiple rhizomes are transported to a localized area.

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Figure 1.6. Distribution of cogongrass patches on Lee Memorial Forest. A higher GiZScore

indicates other patches in close proximity. Higher values on the heat map indicate clustering of

patches.

12

Figure 1.7. Results of Ripley's K function of the cogongrass distribution in Lee Memorial

Forest. The vertical axis L(d) is the difference between the pattern observed and a randomly

distributed pattern and the horizontal axis is the distance from one cogongrass plant to the next.

The dashed lines represent a 95% confidence interval. The red line represents the results and the

blue line represents the idealized situation. Clustering occurs when patches are located near each

other, but when the distance to the nearest patch extends beyond 640 meters there is a random

pattern.

CONCLUSION

Cogongrass at Lee Memorial Forest reflects previous findings from other areas. The

cover types of evergreen forest and shrub/scrub appear to be a more important factor in where

new patches are discovered than soil type. This could be a reflection of searching intensity, since

there are no management actions in the woody wetlands, though they are a large portion of the

landcover. Management may be a potential source of spread, but new patches are discovered

within a few years of any activity. Monitoring of areas with management actions should be more

thorough when it is within that timeframe. There also appears to be some periodicity in the

13

discovery of new patches, with peaks approximately every 5 years (Figure 1.1). It is likely there

is some germination by seed occurring because there are patches that are in units that have not

had any active management. Research activities also occur in Lee Memorial Forest, which also

have the potential to transport rhizomes and should also be monitored. Patches appear to be

clustered, especially in the north, indicating that areas within 640 meters of a known patch

should be more intensely monitored because of increased risk of invasion.

REFERENCES

Brewer, J. S., and S. P. Cralle. 2003. Phosphorus addition reduces invasion of a longleaf pine

savanna (Southeastern USA) by a non-indigenous grass (Imperata cylindrica). Plant

Ecology 167:237-245.

Brewer, S. 2008. Declines in plant species richness and endemic plant species in longleaf pine

savannas invaded by Imperata cylindrica. Biological Invasions 10:1257-1264.

Brook, R. 1989. Review of literature on Imperata cylindrica (L.) Raeuschel with particular

reference to South East Asia. International Journal of Pest Management 35:12-25.

Bryson, C. T., and R. Carter. 1993. Cogongrass, Imperata cylindrica, in the United States. Weed

Technology 7:1005-1009.

Bryson, C. T., L. J. Krutz, G. N. Ervin, K. N. Reddy, and J. D. Byrd Jr. 2010. Ecotype variability

and edaphic characteristics for cogongrass (Imperata cylindrica) populations in

Mississippi. Invasive Plant Science and Management 3:199-207.

Chikoye, D., U. E. Udensi, and S. Ogunyemi. 2005. Integrated Management of Cogongrass [(L.)

Rauesch.] in Corn Using Tillage, Glyphosate, Row Spacing, Cultivar, and Cover

Cropping. Agronomy Journal 97:1164-1171.

Daneshgar, P., and S. Jose. 2009. Imperata cylindrica, an alien invasive grass, maintains control

over nitrogen availability in an establishing pine forest. Plant and Soil 320:209-218.

Daneshgar, P., S. Jose, A. Collins, and C. Ramsey. 2008. Cogongrass (Imperata cylindrica), an

alien invasive grass, reduces survival and productivity of an establishing pine forest.

Forest Science 54:579-587.

De Smith, M. J., Goodchild, M. F., & Longley, P. 2018. Geospatial analysis: a comprehensive

guide to principles, techniques and software tools 6th Edition. Troubador Publishing Ltd

Dickens, R. 1974. Cogongrass in Alabama after sixty years. Weed Science 22:177-179.

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Dozier, H., J. F. Gaffney, S. K. McDonald, E. R. Johnson, and D. G. Shilling. 1998. Cogongrass

in the United States: history, ecology, impacts, and management. Weed Technology:737-

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cylindrica). Plant Invasions:60.

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distribution models for invasive plants: an example with cogongrass (Imperata

cylindrica). Invasive Plant Science and Management 4:390-401.

Estrada, J. A., and S. L. Flory. 2015. Cogongrass (Imperata cylindrica) invasions in the US:

Mechanisms, impacts, and threats to biodiversity. Global Ecology and Conservation 3:1-

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Geographical Analysis. 24:189-206.

Hagan, D. L., S. Jose, and C.H. Lin. 2013. Allelopathic exudates of cogongrass (Imperata

cylindrica): implications for the performance of native pine savanna plant species in the

southeastern US. Journal of Chemical Ecology 39:312-322.

Holly, D. C., and G. N. Ervin. 2006. Characterization and quantitative assessment of

interspecific and intraspecific penetration of below-ground vegetation by cogongrass

(Imperata cylindrica (L.) Beauv.) rhizomes. Weed Biology and Management 6:120-123.

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availability upon growth and biomass allocation in cogongrass (Imperata cylindrica).

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Holzmueller, E. J., and S. Jose. 2012. Response of the invasive grass Imperata cylindrica to

disturbance in the southeastern forests, USA. Forests 3:853-863.

Hubbard, C. E., R. Whyte, D. Brown, and A. Gray. 1944. Imperata cylindrica. Taxonomy,

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Jose, S., J. Cox, D. L. Miller, D. G. Shilling, and S. Merritt. 2002. Alien plant invasions: the

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Alabama Cooperative Extension System, Auburn University, AL.

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Müller, G., L. Horstmeyer, T. Rönneburg, M. van Kleunen, and W. Dawson. 2016. Alien and

native plant establishment in grassland communities is more strongly affected by

disturbance than above‐and below‐ground enemies. Journal of Ecology 104:1233-1242.

Tabor, P. 1952. Cogongrass in Mobile County, Alabama. Agronomy Journal 44:50.

Trahan, L. J., J. Bradley, L. Morris & P. Porter. 1997. Soil survey of Washington Parish,

Louisiana. United States Department of Agriculture and Louisiana Agricultural

Experiment Station.

Xuan, T. D., T. Toyama, M. Fukuta, T. D. Khanh, and S. Tawata. 2009. Chemical interaction in

the invasiveness of cogongrass (Imperata cylindrica (L.) Beauv.). Journal of Agricultural

and Food Chemistry 57:9448-9453.

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CHAPTER 2. BIOGEOGRAPHIC EFFECTS OF SOIL MICROBES ON

GROWTH OF COGONGRASS

INTRODUCTION

Invasive species disrupt ecosystem processes, reduce biodiversity and land quality

(Vitousek and Walker 1989, Vitousek et al. 1996, Levine et al. 2003, Brewer 2008, Davies 2011)

and cost billions of dollars in direct and indirect impacts on natural and agricultural systems

(Daneshgar and Jose 2009, Pimentel et al. 2011, Divate et al. 2017). Thus, there is an urgent

need to understand the mechanisms by which plants invade new ecosystems. Research on

mechanisms of invasion has largely focused on the traits of plants (van Kleunen et al. 2010), but

successful plant invasions may also be the result of changes in trophic interactions (Catford et al.

2009). For example, the loss of predators, consumers, or pathogens as species move from their

native range to introduced ranges may result in unchecked population growth (Keane and

Crawley, 2002, Agrawal et al. 2005, Heger and Jeschke 2018). This release from natural

enemies (Keane and Crawley 2002) has mostly been studied in the context of plant-herbivore

interactions, however, there are a myriad of macro-and micro-invertebrates, bacteria, and fungi

that have negative effects on plants, thereby acting as enemies (Mitchell and Power 2003).

Soil microbes can have powerful impacts, both positive and negative, on individual plant

growth and fitness (van der Heijden et al. 2008). Microbes increase production of aboveground

biomass, number of leaves and their chlorophyll content (Lau and Lennon 2011) and alter the

timing of flowering (Wagner et al. 2014). Additionally, they increase tolerance to abiotic

conditions (Rodriguez et al. 2008), control gene expression (Yang et al. 2009) and increase

invasiveness (Aschehoug et al. 2012, Aschehoug et al. 2014). The clonal plant Stachys sylvatica

produced more stolons and fewer flowers when exposed to the soil mycorrhizal community from

a hedgerow as compared to one from the forest interior (de la Peña 2011). There can also be

17

differences in microbial community effects depending on plant genotype. Western genotypes of

Boechera stricta are more sensitive to microbial inoculum than an eastern genotype (Wagner et

al. 2014). Relationships between nitrogen-fixing bacteria and mycorrhizal fungi and plants

provide increased access to nutrients (Delavaux et al. 2017). Microbes can also provide

protection by consuming pathogens in the rhizosphere (van der Heijden et al. 2008, de Deyn

2017). Microbial pathogens and consumers reduce plant growth or cause death (Reinhart and

Callaway 2006). Soil microbial communities also contribute to the community composition of

plants by influencing interspecific interactions between plants (Klironomos 2002, Bever et al.

2010). A study of two dune grasses found that nematodes preferentially reduce root mass of one

of the species, allowing the other to have a competitive advantage in the intake of nutrients (van

der Putten and Peters 1997). An invasive aster was found to cultivate a generalist fungus, which

negatively impacted growth of two native species, with limited effect on itself (Mangla and

Callaway 2008). Klironomos (2002) found that plants that accumulated more pathogens were

less abundant in an old field.

Plants, by way of chemical signals and carbon allocation, can also affect the composition

and relative abundance of microbes in the rhizosphere (Garbeva et al. 2004, Berg and Smalla

2009). Kourtev et al. (2002) found the structure and function of soil communities differed

depending on plant species and whether or not they were invasive. The invasive grass Bromus

inermis increased the abundance of rare species of soil bacteria, which in turn appeared to

increase the abundance of B. inermis, suggesting that some relationships between plants and soil

microbes are highly mutualistic (Piper et al. 2015). However, the effect of plants on soil

microbial communities are not always consistent within a species. A study of four genotypes of

Populus augustifolia showed that 70 % of the variation in microbial community composition was

18

due to genotype (Schweizer et al. 2008). Microbial communities in the rhizosphere of three

lineages of clonal Phragmites australis populations were more similar within lineages, even

when growing in sympatry (Bowen et al. 2017). These results suggest that the effects of plants

on soil microbial communities are complex and context specific.

The positive and negative soil feedbacks generated by individual plant species then can

influence plant competition (Bever 2003) which can result in cascading effects across plant and

soil communities in space and time (Bever 2003, Belnap et al. 2005, van der Heijden et al. 2008,

Bauer et al. 2017). Consequently, the complex relationships between plant and microbial

communities are likely to be disrupted when plant species or genotypes are introduced to new

ranges. For example, two invasive lineages of Phragmites australis reduced biomass production

of the native Spartina alterniflora by 7% when grown concurrently in Phragmites cultivated live

soil, compared to sterile soil. Whereas, a native lineage of P. australis increased biomass

production by 6% (Allen et al. 2018).

A crucial step toward understanding plant-microbe interactions is assessing the way in

which soil microbial communities change over time in response to plants, and how those changes

affect plant performance (Bever 2003, Garbeva et al. 2004, Inderjit and van der Putten 2010,

Maron et al. 2014, Perkins et al. 2015, Bauer et al. 2017). To this end, plant-soil feedback

experiments are commonly used to test the effects of whole soil microbial communities on plant

performance (Klironomos 2002, Beckstead and Parker 2003, Kulmatiski et al. 2008, te Beest et

al. 2009, Bever et al. 2010, Brinkman et al. 2010). Soil microbial communities may shift toward

higher abundances of pathogenic microbes over time resulting in a negative feedback, or plants

may cultivate specific beneficial microbes via chemical signaling or carbon sharing resulting in a

19

positive feedback (Kourtev et al. 2002, Reinhart and Callaway 2006, Berg and Smalla 2009,

Cortois et al. 2016, Delavaux et al. 2017).

Cogongrass (Imperata cylindrica (L.) Beauv.) is an invasive species found throughout the

Southeastern US (Dozier et al. 1998, Lucardi et al. 2014, Burrell et al. 2015). To better

understand if release from pathogenic microbes plays a role in its success, I explored the effects

of soil microbial communities on cogongrass growth. I conducted two parallel plant-soil

feedback experiments utilizing live and sterilized soils from both the native range and the

invaded range of cogongrass. The use of a biogeographic experimental design allows us to assess

whether changes in trophic level interactions contribute to cogongrass invasion success. I

focused on the following questions: 1) Does release from soil microbes in the native range

increase performance upon arrival in a novel environment? 2) Does cogongrass alter the soil

microbial community to affect plant performance and do they differ between native and non-

native ranges? 3) Do separate populations of cogongrass in the invaded range have similar

performance reactions to the microbial community?

MATERIALS AND METHODS

Focal Species

Cogongrass is a clonal C4 perennial grass species native to Southeast Asia that has

invaded across the Southeast United States (Dozier et al. 1998, Burrell et al. 2015). There are

three known clonal lineages in the United States (Burrell et al. 2015). Cogongrass has strong

negative effects worldwide including accelerated decomposition rates (Holly et al. 2009), faster

nitrogen uptake than pine seedlings (Daneshgar and Jose 2009), decreasing species richness

(Brewer 2008) and increasing landscape flammability (Lippincott 2000, Estrada and Flory 2015)

and as an agricultural weed (Chikoye et al. 2005). It grows in a wide variety of soil types, from

20

heavy clay to sandy soils and in a wide range of nutrient conditions (Jose et al. 2002, Bryson et

al. 2010).

In September 2017, rhizomes of cogongrass were collected from two unmanaged

populations in Washington Parish, Louisiana, USA: Bogue Chitto State Park (BC) (30.779, -

90.143) and Lee Memorial Forest (LMF) (30.874, -89.991). A permit was obtained from the

Louisiana Office of State Parks and permission granted from the LMF land manager. Both sites

were mixed pine-hardwood forests. Rhizomes were rinsed in tap water to remove adhering soil

particles, then placed into a 5% bleach solution for 5 minutes for surface sterilization (Maron et

al. 2014). Each rhizome was cut into segments containing at least 0.5 grams of plant material

and 5 nodes. Mass, number of nodes and location of collection were recorded for each segment

prior to planting.

Soils

Soils from the native range of cogongrass were collected from two sites in Okinawa,

Japan (26.363, 127.827) in mid-June and early July 2017. Japanese soils were chosen because

the earliest introduction of cogongrass to the United States was in packing material of a shipment

of satsuma plants from Japan in 1912 (Tabor 1952). All soils were air-dried and had large root

material removed prior to shipping. Soils were imported under USDA permit P330-16-00306

and arrived within 10 days of shipping. Invaded-range soils were collected from two different

locations in Lee Memorial Forest (30.876, -89.99 and 30.873, -89.996), Louisiana, USA on June

13, 2017. All soils were collected from areas known to be free of cogongrass to mimic

unoccupied areas in both native and invaded ranges.

In the laboratory, all soils were processed to remove remaining plant matter and reduce

the size of aggregated soil clumps. The two soil sources from each geographic region were

21

thoroughly homogenized prior to use in experiments. Soil samples were sent to the Soil Testing

and Plant Analysis Lab at Louisiana State University

(www.lsuagcenter.com/portals/our_offices/departments/spess/servicelabs/soil_testing_lab) for

analysis of mineral and organic content. Results of those tests are reported in Table 2.1.

Table 2.1. Comparison of initial soil composition before mixing with pasteurized sand.

Louisiana Soil Japanese Soil

Live Sterile Live Sterile

% Organic

Matter 2.57 2.24 6.13 5.49

Calcium, ppm 316.42 198.77 3009.12 3265.45

Copper, ppm 0.37 0.18 3.12 1.22

Magnesium,

ppm 47.09 37.47 313.67 227.31

pH (1:1 Water) 5.92 5.16 7.48 6.67

Phosphorus,

ppm 4.48 9.59 15.82 35.81

Potassium, ppm 65.2 31.05 139.75 76.63

Sodium, ppm 10.13 13.75 47.03 49.89

Sulfur, ppm 8.87 9.17 16.88 27.05

Zinc, ppm 0.8 0.63 7.31 8.18

Carbon % 1.56 1.46 3.96 2.99

Nitrogen % 0.07 0.07 0.34 0.29

Effects of naïve soil microbial communities on cogongrass

In the first phase of the plant-soil feedback experiment, I grew cogongrass in either live

soils or sterilized soils to assess the effects of naïve soil microbial communities on the

establishment and growth of cogongrass. Naïve soil microbial communities are defined as soil

communities that have not previously had cogongrass growing in them. In July 2018, all soils

were mixed with pasteurized sand (heated to 180o C) 1:1 by volume. Then, 1500 mL of the soil-

sand mixture was placed in a 1.8 L round nursery pot. Fifty-four pots were filled with Japanese

soil and 54 pots were filled with Louisiana soil. To establish a treatment consisting of microbes

22

present or absent, half of the pots of each soil type were sterilized via autoclaving. The efficacy

of sterilization was confirmed using sterilization indicator strips placed in the middle of the pot.

All pots, sterilized or not, were covered with aluminum foil to prevent contamination prior to

planting.

On September 16, 2017, rhizomes were randomly selected and planted into the prepared

pots. Thirteen pots were prepared for each soil source-rhizome source-sterilization treatment

combination for a total of 104 pots. Emergence, maximum height (measured from the soil to the

tip of the longest green leaf in cm) and number of leaves in each pot, were monitored weekly.

Any pots that did not have emergent vegetation were replanted on September 30, 2018 with

previously collected but unused rhizomes from the same location as those that did not emerge.

After 14 weeks, all plants were harvested, separating aboveground and belowground parts. All

biomass was dried at 70o C for 48 hours and weighed (Figure 2.1).

Figure 2.1. Diagram of test of naïve soil microbial communities. Japanese and Louisiana soil

were kept separate but went through the same process of sterilization and planting.

23

Effects of cultivated soil microbe communities on cogongrass

To test the effects of cogongrass soil microbe community cultivation on future growth, on

January 12, 2018 new rhizomes were planted into either “experienced soils” or sterilized soils.

Experienced soils were unsterilized, live soils from the previous experiment in which cogongrass

grew for 14 weeks. Live soils from the first experiment were mixed with pasteurized sand

(heated to 180o C) 1:1 by volume. To test for differences associated with rhizome origin, soils

were constrained to re-use within previous rhizome planting type. Soils with the same soil

origin/rhizome combination were homogenized, mixed with pasteurized sand as described

previously, and 1500 mL were placed into 1.8 L round pots. Twelve pots of each soil/rhizome

combination were sterilized via autoclaving and twelve pots had the soil community left intact.

Due to the short natural photoperiod during the time of the experiment, supplemental

lighting was used to extend the photoperiod to 12 hours. At weekly intervals, emergence,

maximum height and number of stems in each pot were measured. After 14 weeks, all plants

were harvested, and aboveground and belowground biomass was separated. All biomass was

dried at 70o C for 48 hours and weighed (Figure 2.2).

Figure 2.2. Diagram of test of cultivated soil microbial communities. Soils were kept separate

from each other and by previous rhizome source exposure and by soil collection location. All

went through the same process of sterilization and planting.

24

Statistical Analysis

Japanese and Louisiana soil varied greatly from each other in mineral content (Table 2.1).

Japanese soil had a close to neutral pH, where Louisiana soil is acidic. Japanese soil also had

over twice as much % carbon, four times as much % nitrogen and micronutrients ranged from

1.9 to 8.5 times higher than in Louisiana soil. Due to these soil composition differences and to

more clearly assess the impact of both microbes and rhizome source, the data were analyzed

separately for Japanese and Louisiana soil. In addition, response variables were checked for

correlation via linear regression using JMP Pro 14.

A two-way ANCOVA was performed using SAS 9.4 with rhizome source and microbial

presence/absence as fixed factors for each of the response variables. In addition, initial rhizome

weight was included in the model as a covariate, since there were a range of values (0.44 g –

1.86 g in the initial phase and 0.75g – 1.14 g in the feedback phase). Post-hoc Tukey tests were

run to determine pairwise significance (P<0.05) of the least-square means for each combination

of microbe/rhizome effect. Response variables included aboveground biomass, height at 12

weeks, and number of leaves at 12 weeks. Before analysis, all response variables were natural

log transformed due to non-normal distribution and heteroscedasticity.

The influence of microbial presence and rhizome source on emergence of the rhizomes

was analyzed using logistic regression in SAS 9.4. Slope parameters were analyzed by maximum

likelihood to determine if they were significantly different from zero, then plots of predicted

probability were generated for each predictive variable.

To investigate microbial influence and compare results from initial and feedback phases

in both locations, a relative interaction (RI) index (Armas et al. 2004) was calculated as

25

RI Index = with variance =

where and BW is the mean measured effect in live soil, BO is the mean

measured effect in sterile soil, is the variance in the live soil, is the variance in the sterile

soil, n is the sample size of live soil and m is the sample size of sterile soil. Responses range

from -1 to 1, with negative values indicating the measured effect was greater in sterile soil and

positive values indicating the measured effect was greater in live soil. The index is symmetric

around zero, which means the RI index can be used to compare the response of cogongrass to

soil microbes across soil and treatment types. Data used to calculate RI index was not

transformed and values were compared using two-sample t-tests assuming unequal variances to

test for differences both within and between treatments.

RESULTS

Correlation of response variables

Aboveground biomass was not correlated with height of the plants in either Japanese (F1,

36=1.87, p=0.18, r2=0.05) or Louisiana soil (F1,43=2.28, p=0.14, r2=0.05). There was a correlation

between aboveground biomass and number of leaves in both Japanese (F1,36=14.70, p<0.001,

r2=0.29, Y=-1.41+0.35X) and Louisiana soil (F1,43=40.29, p<0.001, r2=0.48, Y=-2.67+0.64X).

Number of leaves and height were chosen as response variables because they appeared to

represent differences between the two populations. Height and leaf number were correlated in

Japanese soil when all samples were combined (F1,36=28.12, p<0.001, r2=0.44, Y=6.51-1.05X)

but when rhizome sources were considered separately, no correlation was evident (LMF:

26

F1,16=2.88, p=0.11, r2=0.15, BC: F1,18=0.002, p=0.97, r2<0.001). Similarly, in Louisiana soil

there was a correlation when all samples were included (F1,43=9.90, p=0.003, r2=0.19, Y=4.79-

0.72X), but the LMF rhizome had a positive correlation (F1,19=8.17, p=0.01, r2=0.30, Y=-

0.42+0.82X) and the BC rhizome had a negative relationship (F1,22=5.45, p=0.029, r2=0.16,

Y=5.33-0.84X). Because of these differences, all response variables were included in

subsequent analysis.

Effects of naïve soil microbial communities on cogongrass

There were no significant effects of microbe presence/absence on cogongrass emergence,

regardless of rhizome or soil source (Tables 2.2 & 2.3). In sterile Japanese soil, 46% of the Lee

Memorial Forest (LMF) rhizomes and 62% of the Bogue Chitto State Park (BC) rhizomes

emerged, while in sterile Louisiana soil both rhizomes had 85% emergence. Emergence in

Japanese live soil was 86% for both rhizomes. In Louisiana soil it was 71% for the LMF

rhizome and 93% for the BC rhizome.

In Japanese soil, rhizome source, presence/absence of microbes or their interaction had

no significant effect on plant aboveground biomass (Table 2.4). However, the mean height of

the LMF plants was 106% greater than the mean height of the BC plants (Table 2.5). Rhizome

source also affected the number of leaves with individual BC plants producing 325% more

leaves than LMF plants (Tables 2.4 & 2.5). There was also an interaction between rhizome and

the presence/absence of microbes. In sterile soil, LMF plants produced 104% more leaves, but

microbes had no influence on the number of leaves produced by BC plants (Table 2.5).

27

Table 2.2. Maximum likelihood estimates of emergence with multiple predictor variables from

logistic regression model for cogongrass exposure to naïve soil, N= 54 for each soil source. The

dependent variable was categorized as 0=no emergence and 1=emergence. Emergence was any

observable aboveground growth even if the plant later died.

Soil Source Factor Estimate SE Wald P-value

Japan Microbe Presence 1.36 1.23 1.23 0.268

Rhizome Source -0.393 0.891 0.195 0.659

Microbe*Rhizome -0.380 1.567 0.059 0.808

Louisiana Microbe Presence 0.080 1.470 0.003 0.957

Rhizome Source -0.780 1.294 0.364 0.547

Microbe*Rhizome -0.869 1.761 0.243 0.622

Table 2.3. Influence of factors on the probability of rhizome survival from the logistic

regression model. Results for naïve soil exposure and after cogongrass cultivation of microbial

communities are included.

Soil Source Factor Probability Range

Naïve Soil

Probability Range After

Microbe Cultivation

Japan LMF Rhizome 0.70 – 0.86 0.96

BC Rhizome 0.77 – 0.93 0.63

Microbes Present 0.85 - 0.93 0.63 – 0.96

Microbes Absent 0.69 - 0.77 0.63 – 0.96

Louisiana LMF Rhizome 0.72 – 0.85 0.34 – 0.79

BC Rhizome 0.93 0.33 – 0.79

Microbes Present 0.72 – 0.93 0.78

Microbes Absent 0.85 – 0.93 0.33

28

Table 2.4. Results of the ANCOVAs for cogongrass growth in naïve soil. Soil sources and

response variables were analyzed separately.

Soil Source Response Variable Factor DF F-Value P-Value

Japan Aboveground Biomass Microbe Presence 1, 33 2.93 0.096

Rhizome Source 1, 33 0.48 0.496

Microbe*Rhizome 1, 33 1.81 0.188

Height at 12 Weeks Microbe Presence 1,33 0.65 0.427

Rhizome Source 1, 33 59.24 <0.001


Leaves at 12 Weeks Microbe Presence 1, 33 3.77 0.061

Rhizome Source 1, 33 19.27 <0.001


Louisiana Aboveground Biomass Microbe Presence 1, 40 8.18 0.007



Height at 12 Weeks Microbe Presence 1, 40 0.00 0.998

Rhizome Source 1, 40 32.10 <0.001


Leaves at 12 Weeks Microbe Presence 1, 40 17.12 <0.001

Rhizome Source 1, 40 41.78 <0.001


29

Table 2.5. Means and standard errors of response variables for cogongrass exposure to naïve

soils. Means subdivided by rhizome and soil treatment are provided only if it was a statistically

significant effect.

Soil Source Response Treatment Mean St. Error N

Japan Aboveground Biomass (g) Live Soil 0.65 0.04 24

Sterile Soil 0.92 0.11 14

LMF Rhizome 0.71 0.07 18

BC Rhizome 0.78 0.07 20

Height (cm) Live Soil 32.24 2.83 24



BC Rhizome 21.46 1.38 20

Leaf Number Live Soil 22.33 3.26 24



BC Rhizome 33.85 2.84 20

Live, LMF 9.58 1.26 12

Sterile, LMF 19.50 3.97 6

Live, BC 35.08 3.64 12

Sterile, BC 32.00 4.77 8

Louisiana Aboveground Biomass (g) Live Soil 0.48 0.03 23



BC Rhizome 0.48 0.03 24




BC Rhizome 17.08 1.01 24

Live, LMF 30.72 1.82 10

Live, BC 15.28 0.79 13

Sterile, LMF 25.63 3.35 11

Sterile, BC 19.20 1.84 11




BC Rhizome 21.75 1.74 24

30

In Louisiana soils, live soil resulted in a 37% increase in mean aboveground biomass

compared to sterilized soil (Table 2.5). The BC plants had a 41% increase in mean aboveground

biomass compared to the LMF plants (Table 2.5). Rhizome source and the interaction between

rhizome and microbe presence affected height (Table 2.4). The mean height of LMF plants was

64% taller than the mean height of BC plants (Table 2.5). This was more pronounced in live

soil, where the mean height of the LMF plants was 101% taller than the mean height of BC

plants (Table 2.5). In sterile soil, LMF plants were 33% taller than BC plants. Live soil resulted

in a 63% increase in the number of leaves produced overall and the BC plants produced 129%

more leaves than the LMF plants (Table 2.5).

Microbes influenced cogongrass aboveground growth and leaf production in opposite

ways depending on the source of the soil. The RI index for aboveground biomass and number of

leaves were negative in Japanese soil, indicating a negative effect of microbes on these two

traits. In contrast, there was a positive effect of Louisiana soil on the same traits (biomass: t78.1=-

25.29, p<0. 001, leaves: t70.7=-16.52, p<0.001) (Figures 2.3 & 2.5). Microbial presence/absence

did not appear to influence height (Figure 2.4), until separated by rhizome source. Microbes

positively influenced height for LMF plants in both Japanese and Louisiana soil, but negatively

influenced BC plant heights in both soil types (Japan: t36=-5.64, p<0.001, Louisiana: t37=-10.73,

p<0.001) (Figure 2.6). Microbes negatively influenced the number of leaves for LMF plants in

Japanese soil, but positively influenced it in Louisiana soil. In Louisiana soil, the rhizome source

did not differ in leaf production (t41.7=-0.95, p=0.35) (Figure 2.7). Microbes affected both

rhizomes similarly in aboveground biomass production, but LMF plants were more strongly

affected (Japan: t35.1=3.67, p<0.001, Louisiana: t32.7=-4.12, p<0.001) (Figure 2.8).

31

Figure 2.3. Means (+/- standard error) of RI Index for aboveground biomass in soil containing

microbes compared to sterile soil. An asterisk indicates there was a significant difference

(p<0.05) between the RI Indices of naïve and cogongrass cultivated microbes.

32

Figure 2.4. Means (+/- standard error) of RI Index for height at 12 weeks in soil containing

microbes compared to sterile soil. An asterisk indicates there was a significant difference

(p<0.05) between the RI Indices of naïve and cogongrass cultivated microbes. Japan and

Louisiana values for naïve soil microbes were not significantly different from zero.

33

Figure 2.5. Means (+/- standard error) of RI Index for number of leaves at 12 weeks in soil

containing microbes compared to sterile soil for rhizomes. An asterisk indicates there was a

significant difference (p<0.05) between the RI Indices of naïve and cogongrass cultivated

microbes. An asterisk indicates there was a significant difference (p<0.05) between the phases.

34

Figure 2.6. Means (+/- standard error) of RI Index for height at 12 weeks in soil containing

microbes compared to sterile soil divided by rhizomes from Lee Memorial Forest (LMF) and

Bogue Chitto State Park (BC). An asterisk indicates there was a significant difference (p<0.05)

between the RI Indices of plants grown from the two rhizome sources. The index value for BC

rhizomes grown in Louisiana soil with microbes cultivated by cogongrass is not statistically

different from zero.

35

Figure 2.7. Means (+/- standard error) of RI Index for number of leaves at 12 weeks in soil

containing microbes compared to sterile soil for rhizomes divided by rhizomes from Lee

Memorial Forest (LMF) and Bogue Chitto State Park (BC). An asterisk indicates there was a

significant difference (p<0.05) between the RI Indices of plants grown from the two rhizome

sources. The index value for BC rhizomes grown in Louisiana soil with microbes cultivated by

cogongrass is not statistically different from zero.

36

Figure 2.8. Means (+/- standard error) of RI Index for aboveground biomass in soil containing

microbes compared to sterile soil divided by rhizomes from Lee Memorial Forest (LMF) and

Bogue Chitto State Park (BC). An asterisk indicates there was a significant difference (p<0.05)

between the RI Indices of plants grown from the two rhizome sources.

Effects of cultivated soil microbe communities on cogongrass

Rhizome source predicted emergence in Japanese soil that had previously been

conditioned by growing cogongrass in those soils (Wald=5.64, P=0.018) with the BC rhizome

having a predicted 63% chance of emergence and the LMF rhizome having a 96% chance of

emergence. In Louisiana soil microbial presence (Wald=9.35, P=0.002) had a large influence on

emergence with a predicted 78% probability of emergence in the presence of microbes and only

a 33% chance in sterile soil (Tables 2.3 & 2.6).

37

Table 2.6. Maximum likelihood estimates of emergence with multiple predictor variables in

feedback with cogongrass cultivated soil microbial communities, N= 48 for each soil source.

The dependent variable was categorized as 0=no emergence and 1=emergence. Emergence was

any observable aboveground growth even if the plant later died.

First Iteration Second Iteration

Soil

Source

Effect Estimate SE Wald P-

value

Estimate SE Wald P-

value

Japan Microbe Presence 1.099 0.882 1.552 0.213

Rhizome Source 2.398 1.193 4.037 0.045 2.625 1.105 5.641 0.018

Microbe*Rhizome 9.216 166.3 0.003 0.956

Louisiana Microbe Presence 4.007 1.300 9.497 0.002 2.028 0.663 9.346 0.002

Rhizome Source 1.609 0.966 2.775 0.096

Microbe*Rhizome -3.314 1.549 4.578 0.032

In Japanese soil, rhizome source, presence/absence of microbes or their interaction had

no statistically significant effect on plant aboveground biomass or 12-week leaf count. Rhizome

source influenced height, with a 65% increase in mean height of LMF plants compared to BC

plants (Tables 2.7 & 2.8).

In Louisiana soil, microbe presence affected aboveground biomass produced, with a

117% increase in mean aboveground biomass in sterile soil compared to live soil. Neither

rhizome source nor presence/absence of microbes influenced the height or number of leaves

(Tables 2.7 & 2.8).

38

Table 2.7. Means and standard deviations of response variables after exposure to a microbial

community cultivated by cogongrass.

Soil Source Response Treatment Mean St. Error N

Japan Aboveground Biomass (g) Live Soil 1.34 0.10 21



BC Rhizome 1.20 0.06 14




BC Rhizome 32.76 1.60 14




BC Rhizome 19.93 1.73 14

Louisiana Aboveground Biomass (g) Live Soil 0.06 0.01 19



BC Rhizome 0.08 0.02 13




BC Rhizome 6.01 0.93 8




BC Rhizome 4.38 0.92 8

39

Table 2.8. Results of the ANCOVAs of the Feedback Growth Phase. Soil Sources and

Response variables were analyzed separately.

Soil Source Response Variable Factor DF F-Value P-Value

Japan Aboveground Biomass Microbe Presence 1, 32 1.96 0.171




Rhizome Source 1, 32 42.39 <0.0001





Louisiana Aboveground Biomass Microbe Presence 1, 22 9.29 0.006









40

Microbial impact was negative in all cases for aboveground biomass production (Figure

2.3), height (Figure 2.4) and for number of leaves (Figure 2.5). Although the effect was much

greater in Louisiana for all plant traits (biomass: t30.4=8.90, p<0.001, height: t23.5=6.48, p<0. 001,

leaves: t23=2.8, p=0.01). When divided by rhizome source, microbial presence negatively

affected the height and aboveground biomass production in all soils and rhizomes, except height

in Louisiana soil, which was not significantly different from zero (Figures 2.8 & 2.6). There was

a stronger microbial effect on LMF rhizomes in Louisiana soil for aboveground biomass

production (t23=2.63, p=0.015) and a stronger effect in Japanese soil on the BC rhizome for

height (t26.3=-4.03, p<0.001). Microbes had a negative effect in both soils on leaf production for

LMF rhizomes, and a neutral to positive effect for the BC rhizomes (Figure 2.7).

Comparing Naïve Soil Effects to Cogongrass Cultivated Soil Microbial Effects

Soil microbes had neutral or negative effects on cogongrass growth in Japanese soil for

all response variables in the initial and feedback phases. After microbial communities were

cultivated, the strength of the reaction weakened for aboveground biomass production and

number of leaves (biomass: t73=-4.69, p<0.001, leaves: t55.2=-3.12, p=0.003). The response

became less negative after conditioning to microbes for 14 weeks (Figures 2.3 & 2.4). Microbial

cultivation had the opposite effect for height and moved in a negative direction (t55.5=4.19,

p<0.001) (Figure 2.4). In Louisiana soil after cultivation, soil microbes caused all response

variables to move from neutral or positive values to negative (biomass: t29.8=16.84, p<0.001,

height: t53.3=8.16, p<0.001, leaves: t51.8=9.51, p<0.001) (Figures 2.3-2.5).

When separated by rhizome source, microbes had no real effect on the BC rhizome for

any of the response variables, with the exception of aboveground biomass production in

Louisiana soil, which went from positive to negative (t13=6.91, p<0.001) (Figure 2.4). The LMF

41

rhizome was different in every response variable after microbe cultivation in all soil types. In

Japanese soil the response moved in a positive direction, though still staying negative, for

aboveground biomass production (t33=-4.9, p<0.001) and height (t28.4=2.13, p=0.042). While the

movement was from positive to negative for number of leaves (t32=-6.79, p<0.001) (Tables 2.4-

2.6). In Louisiana all response variables moved from positive to negative after cogongrass

cultivated soil microbes (biomass: t18.1=14.26, p<0.001, height: t16.3=6.90, p<0.001, leaves:

t15=10.42, p<0.001) (Figures 2.4-2.6).

DISCUSSION

There was a strong positive effect of naïve soil microbe communities on the growth of

cogongrass in the invaded range. This pattern of facilitation during colonization may improve

cogongrass establishment and drive patterns of invasiveness in new ranges (Inderjit and van der

Putten 2010, Maron et al. 2014, Reinhart and Callaway 2006). This contrasts with the native

range where naïve soil microbial communities had a negative effect on cogongrass growth. Soil

microbial communities in the native range may act as a top-down control on cogongrass

abundance and distribution (Klironomos et al. 2002, Bever 2003, Bever et al. 2010, Inderjit and

van der Putten 2010) and by moving to new ranges, cogongrass may be released from natural

enemies (Keane and Crawley 2002).

Although there are positive effects of naïve soil microbial communities on cogongrass

growth, cultivated soil microbial communities had strong negative effects on cogongrass growth

in both the invaded and native range. The reversal of soil microbial community effects in the

introduced range is likely the result of the rapid accumulation and cultivation of soil pathogens.

Negative soil feedbacks typically result in low growth and reduced population size within

communities (Klironomos et al. 2002). However, field observations show persistent robust

42

populations of cogongrass that expand rapidly after colonization (Wilcut et al. 1988) This

disconnect between negative soil feedback and rapid expansion and growth may be due to

changes in plant behavior in response to soil microbial community changes over time. For

example, invasive species with negative soil feedbacks may escape negative soil conditions

through higher investment in root growth (Cortois et al. 2016). Additionally, invasive species

exhibit more intensive root foraging behavior than non-invasive introduced species (Keser et al.

2014) and negative soil conditions may further promote root elongation in an attempt to escape

soil pathogens.

Grasses, such as cogongrass, are more likely to have negative soil feedbacks due to the

large amount of biomass devoted to underground growth (Kulmatiski et al. 2008, Cortois et al.

2016). For example, two invasive grasses Ammophila arenaria and Bromus inermis had lower

biomass in field-collected, conspecific cultivated live soils when compared to sterilized soils

(Beckstead and Parker 2003, Otfinowski et al. 2016). Bromus inermis experienced strong

negative feedbacks in both native and invaded range soils, but the effect was much stronger in

the invaded range. Despite strong negative feedbacks, B. inermis is an aggressive invaded or the

northern prairies of North America (Otfinowski et al. 2016).

An estimated 70% of monocot families are rhizomatous (Pan and Clay 2002) and

perennial, such as cogongrass, are likely to be successful invaders since invasive plants have

high frequencies of clonal growth (Thompson et al. 1995, Lake and Leishman 2004, Lloret et al.

2005, Pyšek and Richardson 2008). Clonal plants are physiologically integrated so that risk is

spread out among many ramets (Caraco and Kelly 1991, Fischer and van Kleunen 2001, Pan and

Price 2002). In this way, the plant can minimize the impact of parasitic or pathogenic organisms

with an increase in the number of ramets, resulting in a decreased chance of mortality for the

43

genet as a whole (Pan and Price 2002). Ramets are preferentially placed in areas with the best

soil conditions (de Kroon and Hutchings 1995, Fischer and van Kleunen 2001) and can

potentially grow away from negative conditions. For example, in a sedge species, Carex

arenaria, the soil condition of the parent plant was a larger determinant of growth than the ramet

soil condition (D’Hertefeldt and van der Putten 1998). The aster Lactuca sibirica was infected

with a systemic rust fungus and ramets were uninfected if more than 45 cm away from parent

plants, even when still physiologically connected (Wennstrom and Ericson 1992). Similarly,

cogongrass could increase growth in underground structures in invaded areas to grow to

uncolonized areas to take advantage of a naïve microbial community.

Cogongrass is thought to have been introduced to the United States from two locations,

first accidentally from Japan and a second time from the Philippines for research purposes

(Patterson et al. 1980, Estrada et al. 2017). The second time, samples were brought to Florida,

where they thrived, Mississippi, where they were killed in a hard frost within a few years and

Texas, where they survived less than a year (Hubbard et al. 1944). A few previous studies have

tried to differentiate plants from two locations under the assumption that Mississippi accessions

were descended from the Philippine plants. Plants from Mississippi were found to have a

consistently shorter height and less biomass production than plants from Alabama near the

Japanese introduction (Patterson et al. 1980). In this study there was no observable difference

between plants at either location in the field, however in the greenhouse difference in growth

form were evident and consistent. In the greenhouse, the LMF plants grew taller and the BC

plants sent up many more stems and leaves. Later genetic studies showed that the clonal lineage

with the widest geographical variation is most similar to Japanese accessions and is predominant

in the northern Gulf Coast region (Burrell et al. 2015). Burrell et al. (2015) found that there was

44

equal or greater genetic variation within lineages than between lineages. There is enough

variation that samples collected from the northern Gulf Coast can be divided into Mississippi and

Alabama subtypes (Lucardi et al 2014). There was no evidence of hybridization between clonal

lineages (Burrell et al 2015), so it is possible that the growth differences observed are due to

phenotypic plasticity rather than genetic contribution from different lineages. While the overall

trend of microbes on aboveground biomass was the same for both rhizome sources, the BC

source population was less affected by the presence of microbes. Without genetic analysis it is

not possible to tell if the different rhizome sources represent the same clonal lineage and the

same introduction source or if the phenotypic differences are due to genetic differences.

The phenotypic expression of plants from the BC population mirrored populations

described in Patterson et al. (1980), with the BC plants consistently short with a large number of

leaves. The two populations responded differently to microbial assemblage modification, with

the BC plants not altering aboveground biomass allocation between leaves and height. The LMF

plants, however, exhibited large changes in plant traits between the microbe presence-absence

treatments. While it had increased leaf production initially, after microbial cultivation there was

a decrease in leaf production due to microbes. The same pattern held for height. LMF plants

may be less suited to persist in shaded areas or may expand quickly to find less shaded areas.

However, the BC plants could be more likely to persist in shaded areas because increased leaf

production enables a plant to capture more light and produce more photosynthate (Chapin et al.

1987). The decreased height of the BC plants in response to microbes may be of little concern

when growing in an area shaded by trees. The BC plants, therefore, may be well suited to invade

shaded areas. Patterson (1980) and Holly and Ervin (2007) found cogongrass allocated more

biomass to aboveground structures when shaded, and Estrada et al. (2017) found that Florida

45

populations grown in shade did not persist into a second year. The source populations for my

study were in shaded areas and had been persisting for years (J. Nehlig personal

communication). Further investigations could explore context dependence in invasive ability

depending on habitat and population source.

Invasive capacities and strategies may vary by phenotype (te Beest et al. 2009, Andonian

et al. 2012). Centaurea solstitialis seeds from Argentina, an invaded region where there is rapid

spread, germinated later than other seeds, but had similar biomass at the end of the study

(Andonian et al. 2012). Though not directly measured, this would indicate that these plants were

growing much faster and had a higher relative growth rate than those from other regions,

potentially contributing to the rapid spread. In Argentina, there was a greater production of

shoots when grown in live soil compared to sterilized soil, differing from other regions tested,

including California, which also has a rapidly spreading population (Andonian et al. 2012).

These might represent phenotypically adaptive strategies facilitated by the microbial community.

The genetic component of both plant and microbial communities has the potential to

reveal which plant populations are more likely to become invasive and what causes an

introduced plant to become invasive. Increased sampling of plants from native areas combined

with genetic sequencing could uncover variation in invasive potential. Genomic areas that differ

between populations can be evaluated for interactions with soil microbes. While many studies

have looked at soil microbial community responses to plant chemical compounds (Lorenzo et al.

2013, Zhu et al. 2017) and changes along invasion gradients (Sun et al. 2013, Piper et al. 2015,

Collins et al. 2016), a literature search did not reveal any studies assessing before and after

microbial assemblages in the same location. This could be done in both invaded and native

ranges, to see if soil microbial changes are consistent.

46

The enemy release hypothesis is conceptually straightforward, but simply escaping old

enemies does not mean a species will become invasive. It cannot be directly determined if

release from soilborne enemies through rapid expansion or positive feedbacks is a determining

factor in invasive ability. The effect of changes in the microbial community on native plants and

the community needs to be explored as well (Keane and Crawley 2002, Aschehoug et al. 2014,

Heger and Jeschke 2014, Bauer et al. 2017). Differences between populations could indicate

underlying reasons for invasion success and could depend on biogeographic area, even within the

same species. Part of this could be the way the phenotype/genotype, soil microbial community

and nutrient content of the soil interact. Teasing apart these interactions has the potential to give

more insight into differences in degree of invasiveness. In addition, it is possible there is not a

single causal mechanism for invasion success (Catford et al. 2009). It is possible that after a

certain time lag where plants with negative feedback rapidly expand their range, soil microbiota

will no longer be naïve. It will be only marginally beneficial to grown into new areas and rapid

expansion will cease. At that point, negative feedback will serve as a mechanism for coexistence

as may be the case in the current home range.

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54

CHAPTER 3. COGONGRASS SOIL LEGACY EFFECTS ON TWO

NATIVE PLANTS

INTRODUCTION

A major cause of anthropogenic global change is the accidental and purposeful

introduction of species into new ranges (Crosby 1986, Kowarik and von der Lippe 2008).

Invasive species have strong negative effects on ecosystem function (Tilman 1999, Levine et al.

2003), population persistence (Brewer 2008, Davies 2011), and can lead to the extinction of rare

species (Baider and Florens 2011, Akasaka et al. 2017). For example, invasive species may have

cascading effects across multiple trophic levels resulting in potentially irreversible changes to

food web dynamics (Belnap et al. 2005, Perkins and Nowak 2012). These problems have led

ecologists to seek ways to restore ecosystems via the removal of non-native species. However,

success of these actions has been limited, potentially because of the legacy effects that species

may exert on systems long after their removal (Maron and Jeffries 2001, Cuddington 2011,

Elgersma et al. 2011, Corbin and D’Antonio 2012).

The term ‘legacy effects’ refers to the changes to abiotic or biotic system properties of

soil that persist after a plant species has been removed from the soil (Corbin and D’Antonio

2012). Abiotic changes can include persistent chemical exudates from microbes or roots in the

soil (Kuusipalo et al. 1995, Hierro and Callaway 2003, Hagan et al. 2013), degradation of leaf

litter (Liao et al. 2008, Holly et al. 2009), alteration of nutrient loads and cycling (Liao et al.

2008, Daneshgar and Jose 2009) and changes to soil pH (Corbin and D’Antonio 2012). Biotic

changes occur via alterations in microbial community assemblages, including fungal and

bacterial species, nematodes, and soil microinvertebrate abundance and composition (Kourtev et

al. 2002, Mangla and Callaway 2008).

55

These abiotic and biotic changes are not necessarily independent but may be the result of

interactions among factors (Reinhart and Callaway 2006, Perkins and Nowak 2012, van der

Putten et al. 2013,). As such, the resulting legacy effects can be highly variable, temporal and

context dependent (Klironomos 2002, Stinson et al. 2006, Kardol et al. 2007, Meisner et al.

2014). Some recent examples include staged removal of invasive Cytisus scoparius, which

showed an initial increase of nitrogen one month after removal, but a decrease of 70% within 10

months with no further decrease over time (Grove et al. 2015). Additionally, Douglas-fir planted

in removal areas were 37% smaller 22-months post removal of C. scoparius and there was an

increase in other exotics the longer the time since removal (Grove et al. 2015). Another study

involving restoration of pine areas in Hungary showed no legacy of the pine, but when these

areas had invasive Asclepias syriaca there were transient negative effects on grass, but positive

effects on the species richness of other native species (Szitár et al. 2016). Following invasive

Rhododendron ponticum removal, the total cover of grass and forbs did not change over the

ensuing 20-years (Maclean et al. 2018b). There was a significant increase in bryophyte cover,

returning to a near uninvaded level causing a different community composition than in

uninvaded areas with no change in nutrients or pH, though microbial changes were not

examined. A last example with invasive Phalaris aquatica showed that after 11 years of growth,

a microbial legacy was created following removal of this weed. However, the legacy affected

only 1 of 3 native plants tested (Pickett et al. 2019). Hence, legacy effects are likely to be

species specific and studies should be targeted to understand specific problematic species effect

on predominant native species when undertaking restoration in an area previously dominated by

an invasive species. Nurturing survival and expansion of rare species in such areas may be more

successful as well with an understanding of legacy effects.

56

Cogongrass (Imperata cylindrica (L.) Beauv.) is an invasive species in the southern

United States that has strong negative effects on both natural and managed pinelands.

Cogongrass reduces pine seedling survival (Daneshgar et al. 2008, Daneshgar and Jose 2009),

increases the intensity of prescribed burns (Lippincott 2000), and is a low-quality forage for

grazers (Hubbard et al. 1944, Dozier et al. 1998). It also has negative effects on native plant

growth via chemically mediated interactions (Koger and Bryson 2004, Xuan et al. 2009, Hagan

et al. 2013, Javaid et al. 2015) and can cause changes to soil microbial communities (see

previous chapter). The varied mechanisms by which cogongrass affects communities of native

plants may also lead to persistent legacy effects after management actions, but the only studies

investigating these have used manufactured leachate, which may not accurately reflect conditions

in the field (Maclean et al. 2018a). Here, I evaluate the legacy effects of cogongrass on native

species establishment and growth in the Southeastern United States.

METHODS

Two native species commonly found in Louisiana pinelands were used to estimate the

legacy effects of cogongrass. Arnoglossum ovatum (Walt) H.E. Robins, is an aster commonly

found in the southeastern United States in a wide variety of habitats (Smith 1996, Anderson

1998). Little bluestem, Schizachyrium scoparium (Michx) Nash, is a dominant grass throughout

North America (Gaines et al. 1954, Platt 1999, USDA 2019) and is often used in restoration of

prairies and pinelands (Aschenbach 2010, Tober and Jensen 2013). Seed of both species were

purchased from The Ecology Center at University of Louisiana-Lafayette

(ecology.louisiana.edu) in February 2018.

To mimic management actions on cogongrass, in March 2018, I first established

cogongrass “populations” in the greenhouse. Three rhizome segments weighing 1 gram each

57

were planted in ten 3L round pots containing Sta-Green Moisture Max potting mix plus fertilizer

(Pursell Industries, Sylacauga, Alabama) in a greenhouse at Louisiana State University and

watered as needed. To establish a source of control soil that did not have previous cogongrass

growth, on June 2018, I filled ten additional 3L pots with potting soil and watered at the same

frequency and kept in the same conditions as pots containing cogongrass.

In July 2018, cogongrass was harvested and all potting soil from pots containing

cogongrass was combined. Cogongrass-exposed soil was mixed in a 1:5 ratio by volume with

the same, but unused, potting soil. Though this may have diluted the effects, it was done to

ensure a sufficient number of replicates for the experiment described below. After mixing, 1200

mL of the substrate was placed in a 1.5 mL pot. A total of 36 pots were prepared with this

cogongrass-exposed soil. The same procedure was followed to make 36 pots of cogon-free soil

using the control potting soil from the greenhouse. Pots were randomly placed in three

incubators set for 14 hrs light (28oC)/10 hrs dark (25oC) and watered as needed.

Two flats containing the two native plant species were started, one with the unmixed

cogongrass-exposed soil and the other with unmixed cogongrass-free soil. The potting soil used

to start the seeds was not mixed with any other material. Seeds of both A. ovatum and S.

scoparium were direct seeded onto the soil and placed in incubators at the same settings

mentioned previously. In September 2018, one-month-old seedlings were transplanted into pots,

18 of each species were planted in cogongrass-exposed soil and 18 were planted in cogongrass-

free soil for a total of 72 pots. After growing for 11 weeks they were harvested. Aboveground

and belowground mass was separated and weighed after drying for 48 hours at 70o C.

To determine if there were any legacy effects in the soil after cogongrass growth total

biomass was analyzed using the Friedman’s Test in SAS 9.4. This test was chosen because the

58

distribution of the data was not normal, even if natural log transformed, and blocking was

required due to the use of three incubators. Each species was analyzed separately using

untransformed values. Survival data were analyzed via a chi-square test. In addition, data were

standardized using the relative interaction (RI) index (Armas et al. 2004).

RI Index = with variance =

where and BW is the mean measured effect in cogongrass-exposed soil, BO is

the mean measured effect in cogongrass-free soil, is the variance in the cogongrass-exposed

soil, is the variance in the cogongrass-free soil, n is the sample size of cogongrass-exposed

soil and m is the sample size of cogongrass-free soil. Responses range from -1 to 1, with

negative values indicating the measured effect was greater in cogongrass-free soil and positive

values indicating the measured effect was greater in cogongrass-exposed soil. The index is

symmetric around zero.

RESULTS

Overall, both species were able to survive in both soil types. The survival rate was the

same for both species with 94% surviving in the cogon-free soil and 83% surviving in the cogon-

exposed soil. The soil legacy of cogongrass did not affect the ability of S. scoparium (X2=1.13,

p=0.29) or A. ovatum (X2=0.07, p=0.29) to emerge and persist for the 3 month duration of the

experiment.

The cogon-exposed soil negatively impacted total biomass production of S. scoparium

(Q=7.22, p=0.007). Total biomass was reduced by 65% for plants that grew in cogon-exposed

59

potting soil. Cogon-exposed soil elicited less of a response for A. ovatum. Biomass was reduced

by 26% (Table 3.1), however the Friedman’s test showed that there was no significant

differences (Q=0.68, p=0.41) when accounting for the incubator used.

Table 3.1. Results of 11 weeks of growth of two native plant species. Potting soil either had

cogongrass growing in it for 3 months (cogon-exposed) or did not (cogon-free).

Species Response Variable Soil Type Mean (g) Standard Error N

S. scoparium Total Biomass Cogon-free 0.961 0.24 19

Cogon-exposed 0.336 0.10 15

A. ovatum Total Biomass Cogon-free 0.700 0.14 17

Cogon-exposed 0.520 0.09 15

Figure 3.1. Index value (+/- standard error) showing effect of cogongrass precultivating soil on

total biomass production of two native species. Negative values indicate that the growth

response variable was lower in cogon-exposed potting soil. Both values are significantly

different from zero.

DISCUSSION

These results show that legacy effects are a potential barrier to restoration of cogongrass

infested areas. After 11 weeks growing in cogon-exposed soil both S. scoparium and A. ovatum

60

had reduced biomass with a stronger effect on the grass species (Figure 3.1). Cogongrass alters

the soil microbial community (see previous chapter). Potting soil has an unknown microbial

composition, potentially containing a variety of bacteria and fungi via the manufacturing,

packaging and transportation processes. In addition, microbes may be added through watering

and air exposure. A variety of fungi and algae were observed to be growing in the potting soil

(L. Radunzel-Davis, personal observation) while in the incubators. Previous research on S.

scoparium showed that when exposed to fungal spores from multiple prairie soils, growth was

not affected (Ji et al. 2010) and that growth responses in live soil was positive compared to

sterile soil (Bauer et al. 2017). But, this contrasts to an earlier study showing that S. scoparium

had reduced growth in live soil compared to sterilized soil, indicating a sensitivity to soil

microbial communities (Anderson and Roberts 1993). There may be specific microbes that S.

scoparium is sensitive to that are not found in all areas. Specific impacts of microbial changes

on native plants due to cogongrass should be investigated in future experiments due to strong

linkages between microbial community and plant species and community (Belnap et al. 2005,

Mangla and Callaway 2008, de Kroon et al. 2012, Fitzpatrick et al. 2017, Reese et al. 2018).

Cogongrass has already been shown to produce allelopathic compounds (Koger and

Bryson 2004, Xuan et al. 2009, Hagan et al. 2013, Javaid et al. 2015). In vitro studies have

shown that cogongrass root extract decreases germination (Koger and Bryson 2004) and acts as a

fungicide (Javaid et al. 2015). A study using cogongrass soil leachate applied to native plants in

pots found only one plant, the grass Aristida stricta, to be negatively affected (Hagan et al.

2013). They theorized that bluestem grasses, such as S. scoparium may also be resistant,

however S. scoparium was shown previously to have reduced seedling growth when exposed to

root extracts of Andropogon virginicus, another native grass (Rice 1972). Another study

61

exposed S. scoparium to hydrocinnamic acid, a known allelochemical, and found it caused

reduced shoot and root biomass (Williamson et al. 1992). Hydrocinnamic acid was one of 36

compounds identified in rhizome and root extracts of cogongrass (Xuan et al. 2009). This

compound should be investigated further to explore if it is common and if it is a main causative

agent of reduced biomass. Interestingly, another study found that A. virginicus performed better

than other natives when in competition with cogongrass (Daneshgar and Jose 2009b). This could

be due to similar root chemical exudates, which would make A. virginicus a good native

candidate to use in comparison studies with cogongrass since often plants have evolved tolerance

to root exudate chemicals in the home range (Callaway and Aschehoug 2000).

It has already been documented that cogongrass alters the communities it invades,

decreasing plant species richness and increasing ground shading (Brewer 2008). And areas have

been difficult to rehabilitate (Kuusipalo et al. 1995). A meta-analysis of plant-soil feedback

studies showed that, overall, native species were not inhibited by soil legacies of exotic species

and that native grasses in general had positive feedbacks in soil conditioned by exotics (Meisner

et al. 2014). These conflicting results show the importance of exploring specific interactions,

especially in cases of a dominant exotic species on a dominant restoration species. The success

of S. scoparium in a restoration effort may be important, since it allows other species to grow

and produce more biomass than other grasses (Wilsey 2010). However, in areas dominated by

cogongrass there may be a tradeoff between successful establishment and restoration diversity.

One unanswered question is how long soil legacies persist. A study of multiple iterations

of growth showed that there is the potential for some species to have a longer lasting legacy,

even when a second species conditions the soil after the first is removed (Wubs and Bezemer

2017), but this did not hold true for all species, and there may be only transient effects (Corbin

62

and D’Antonio 2012). Use of a native cover crop that modifies the soil in a way that will benefit

native plants could enhance restoration success (Krueger-Mangold et al. 2006, Sheley et al.

2006, Perry et al. 2009). The use of activated carbon to counteract the effect of allelochemicals

during restoration has also been explored (Callaway and Aschehoug 2000, Kulmatiski 2011). A

study of activated carbon had mixed results, with more efficacy against the legacy of some plants

than others (Kulmatiski 2011). It was only effective in increasing native plant abundance when

used along with native seed addition. It also had the effect of decreasing soil microbial lipids, so

the benefits may be not only neutralizing chemicals, but also biotic alterations (Kulmatiski

2011).

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CHAPTER 4. CONCLUSION

Cogongrass has shown that its reputation as one of the world’s worst weeds is well

deserved. It has been able to persist and expand into new patches despite continuous monitoring

and chemical control at Lee Memorial Forest. Patches there are clustered and occur in areas that

are less managed. A greater number of patches are found every five years, indicating some

unexplored potential cyclical pattern. Monitoring intensity should be concentrated near known

clusters and after prescribed burning and timber harvesting activities.

Cogongrass modifies the soil that in occupies. In Louisiana, it initially benefits from

growth in soil with a naïve microbial community. These microbes act on the plant to greatly

increase aboveground biomass production. In native Japanese soil, the soil microbes decrease

aboveground biomass production. Sampling other invaded, as well as other native areas would

show if this trend can be generalized and if there are some interaction effects with nutrient

quality or other conditions. The two populations of cogongrass used in this study did not

respond in the same way. Some populations of cogongrass may have more invasive potential in

some areas than others. This could be explored further by growing rhizomes from populations

with different phenotypic growth forms in the greenhouse in a range of shaded conditions to see

if some are more likely to grow and persist. In some cases, there may be a low probability of

cogongrass expanding into an area if it is less likely to become abundant.

The changes in microbial composition are another area that can be explored in greater

depth. Genetic sequencing of the microbes in the soil can reveal more details on the microbial

changes that occur in the soil and give insight into what is being favored and what is being

disfavored by cogongrass. Microbial composition can be compared before and after cogongrass

growth and with different soil types and locations to determine if a pattern can be established.

69

The microbial and chemical alterations in soil by cogongrass have the potential to

influence growth by subsequent occupiers. This should be more fully explored for areas that are

going to be restored. The use of seed addition, activated carbon or even a temporary cover crop

all may have an impact on the success of restoration. Legacy effects are not the same for all

species. Effects on the most abundant native plants or desired rare native species should be

determined to increase chances of restoration success. If there are effects on either these, then

extra steps can be taken to minimize the soil legacy.

Cogongrass remains problematic throughout the Gulf Coast states. Increased study of

soil modifications that lead to its success have already given some insights into what makes it

invasive. The naïve soil microbial community enhances biomass production, and the alterations

to that community by cogongrass encourage rapid lateral growth. These alterations to either the

soil microbial community and/or the chemicals released into the soil by cogongrass can have

negative consequences for native plant biomass production. Continuing these lines of research

have the potential to yield information that can be used to stop the spread of cogongrass and

restore high priority areas.

70

VITA

Lori considers herself from Wisconsin but spent more than half her life far from the

Midwest, including 2 years in the Philippines as a Peace Corps Volunteer. She earned her

bachelor’s degree in wildlife ecology with a focus on international agriculture and natural

resources and an Environmental Studies Certificate from the University of Wisconsin-Madison.

She developed an interest in invasive species after working for the Departments of Natural

Resources in Wisconsin and Minnesota, where her primary objective was to eliminate invasive

plants from State Natural Areas. She temporarily changed careers, becoming a Certified

Veterinary Technician and worked in small animal hospitals as well as a wildlife rehabilitation

center. Earning this degree will represent a return to her original career path.

cogongrass (imperata cylindrica (l.) beauv.) in louisiana

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