Department of Botany Faculty of Sciences Pir Mehr Ali Shah ...

i

ECOBIOLOGICAL AND ALLELOCHEMICAL CHARACTERIZATION

OF SELECTED INVASIVE PLANTS OF POTHWAR REGION OF

PAKISTAN

HUMA QURESHI

10-arid-1788

Department of Botany

Faculty of Sciences

Pir Mehr Ali Shah

Arid Agriculture University Rawalpindi

Pakistan

2018

ii

ECOBIOLOGICAL AND ALLELOCHEMICAL CHARACTERIZATION

OF SELECTED INVASIVE PLANTS OF POTHWAR REGION OF

PAKISTAN

by

HUMA QURESHI

(10-arid-1788)

A thesis submitted in the partial fulfillment of

the requirements for the degree of

Doctor of Philosophy

in

Botany

Department of Botany

Faculty of Sciences

Pir Mehr Ali Shah

Arid Agriculture University Rawalpindi

Pakistan

2018

iv

CERTIFICATE OF APPROVAL

vi

This Thesis is dedicated

to

My Friends and Family

vii

CONTENTS

Page

List of Tables x

List of Figures xii

List of Annexures xiv

Acknowledgements xv

ABSTRACT xvi

1. INTRODUCTION 1

1.1. BIOLOGICAL INVASIONS: AN ISSUE OF GLOBAL

CONCERN

1

1.2. INVASIVE ALIEN PLANTS (IAP): DEFINITION AND

CONCEPTS

2

1.3. ALLELOPATHY: A NOVEL WEAPON FOR INVASION

SUCCESS

2

1.4. INVASIVE PLANT SPECIES IN PAKISTAN 6

1.5. ALLELOPATHY: DEFINITION AND CONCEPTS 6

1.6. ROLE OF ALLELOPATHY IN AGRICULTURAL

IMPROVEMENT

7

1.7. ROLE OF ALLELOPATHY IN WEED MANAGEMENT 11

1.8. WEEDS OF WHEAT CROP 12

1.9. SELECTED INVASIVE SPECIES 16

1.9.1. Broussonetia papyrifera (L.) L’Herit. ex Vent (Paper

Mulberry)

16

1.9.2. Lantana camara L. (Sage plant) 16

1.9.3. Parthenium hysterophorus L. (Congress weed) 17

1.9.4. Xanthium strumarium L. (Common Cocklebur) 18

1.10. OBJECTIVES 18

2. REVIEW OF LITERATURE 21

2.1. PLANT INVASIONS-AN ECOLOGICAL EXPLOSION 21

2.2. EVIDENCE OF ALLELOPATHIC ADVANTAGE TO

PLANT INVASIONS

33

viii

2.3. NATURAL PRODUCTS FOR PEST MANAGEMENT 35

2.4. ALLELOPATHY IN WEED MANAGEMENT PRACTICES 40

2.4.1. Allelopathic Cover and Smother Crops 42

2.4.2. Allelopathic Green Manure Crops 42

2.4.3. Crop Residues and Allelopathy 43

2.4.4. Searching for Allelopathic Traits Among Wild Varieties

of Crop Plants

43

2.4.5. Allelochemicals as Nature’s own Herbicides 44

3. MATERIALS AND METHODS 49

3.1. ECOLOGICAL IMPACT ANALYSIS 49

3.1.1. Study Area 49

3.1.2. Experimental Design 50

3.1.3. Data Analysis 50

3.2. ALLELOPATHY SCREENING BIOASSAY 56

3.2.1. Collection and Drying of Plant Material 56

3.2.2 Toxicity Assessment of Different Plant Parts 56

3.2.3. Statistical Analysis 57

3.3. HERBICIDAL ACTIVITY ANALYSIS 57

3.3.1. Fractionation of Methanol Plant Extracts 57

3.3.2. Herbicidal Activity Analysis Through Germination

Bioassay

57

3.3.3. Statistical Analysis 58

3.4. ALLELOCHEMICAL ANALYSIS 58

4. RESULTS 60


4.2. ALLELOPATHY BIOASSAYS AND HERBICIDAL

ACTIVITY

105

4.3. ALLELOCHEMICAL CHARACTERIZATION 118

5. DISCUSSION 122


5.2. ALLELOPATHY BIOASSAYS AND HERBICIDAL

ACTIVITY

131

ix

Recommendations 134

SUMMARY 135

LITERATURE CITED 136

x

LIST OF TABLES

Table No. Page

1. Modes of entry of alien species into new habitats and likely

vectors

3

2. Hypotheses for invasiveness of alien species 4

3. Common allelopathic compounds and representing plants 9

4. Projected wheat requirements, area and yield in Pakistan (2010-

2030)

14

5. Recommended wheat varieties in Pakistan 15

6. Literature review for impact analysis of invasive plants around

the globe

24

7. Allelochemicals isolated from Invasive plants 36

8. Plant species found in studied plots, family and life form 62

9. Analysis of variance (ANOVA) of invasion impacts and district

on diversity indices of local plant community

65

10. Student’s t-test for significance of differences between control

and invaded plots at different sites

67

11. SIMPER analysis of Parthenium invaded and control sites in

Pothwar region, Pakistan. Data have been pooled prior to

analyses across districts

69


13. Summary ANOVA of invasion impacts and site on diversity

indices of local plant community

76



78

15. SIMPER analysis of Broussonetia invaded and control sites in

Pothwar region, Pakistan

80




87



89

xi

19. SIMPER analysis of Lantana invaded and control sites in


92




99



101

23. SIMPER analysis of Xanthium invaded and control sites in


103

24. Seedling growth inhibition of Radish seeds by different plant

parts of Lantana camara L., Parthenium hysterophorus L.,

Xanthium strumarium L. and Broussonetia papyrifera (L.)

L’Herit. ex Vent at 0.05gmL−1 aqueous extract

106

25. Seedling growth inhibition (%) of weed test species by Lantana

camara leaves and Xanthium strumarium fruits solvent fractions

at 500 ppm

108



at 1,000 ppm

109



at 10,000 ppm

110

28. IC50 values of seedling growth for weed test species by X.

strumarium fruits solvent extracts

116

29. IC50 values of seedling growth for weed test species by L.

camara leaves solvent extracts

117

xii

LIST OF FIGURES

Figure No. Page

1. Mechanism of plant interference involving competition and

allelopathy

8

2. World Wheat Production (million tons) 13

3. Studied Invader Species in Pothwar region of Pakistan 19

4. Distribution map and invasion status of selected invaders around

the globe

20

5. Natural product based herbicides 41

6. Mean monthly climate data of Pothwar region, Pakistan 54

7. Distribution of plots for impact analysis of invaders in Pothwar

region, Pakistan

55

8. Modified Kupchan Method of solvent-solvent partitioning 59

9. Rarefaction curve showing cumulative number of species recorded

as a function of sampling effort

61

10. Mean values/10m2 for ecological indices of invaded vs. control

plots in different sites

66

11. Multidimensional scaling (MDS) ordination and analyses of

similarity (ANOSIM) results of invasion status data for Pothwar

region, Pakistan

68



72

13. Mean values/10m2 for ecological indices of invaded vs control


77



region, Pakistan

79



83



88

17. Multidimensional scaling (MDS) ordination and analyses of 90

xiii


region, Pakistan



95



100



region, Pakistan

102

21. Growth of Triticum aestivum, Avena fatua, Phalaris minor,

Chenopodium album and Rumex dentatus under 1000ppm

chloroform extract of L. camara leaves extract

111

22. IC50 seedling growth curves for weed test species by X.

strumarium fruits solvent extracts

113

23. IC50 seedling growth curves for weed test species by L. camara

leaves solvent extracts

115

24. GC spectrum of compound isolated from chloroform fraction. A

single peak eluted after 14.3min showing an isolated compound

with an abundance of 52500 in the sample

119

25. Structure of isolated compound from chloroform fraction of

Lantana camara leaves (Vitexin)

120

xiv

List of Annexures

Annexure No. Page

1. Distribution of plots for impact analysis of Lantana

camara L. in Pothwar region, Pakistan

xviii

2. Distribution of plots for impact analysis of Xanthium

strumarium L. in Pothwar region, Pakistan

xiv

3. Distribution of plots for impact analysis of Parthenium

hysterophorus L. in Pothwar region, Pakistan

xv

4. Distribution of plots for impact analysis of Broussonetia

papyrifera (L.) L’Herit. ex Vent. in Pothwar region,

Pakistan

xvi

xv

ACKNOWLEDGEMENTS

As I reach the end of my thesis, I would like to thank Almighty Allah for

the virtues of this blessing for implanting the soul of endurance and faith in myself

to complete this study.

I would like to express my deep gratitude and sincere thanks to my

supervisor Prof. Dr. Muhammad Arshad, Chairman, Department of Botany,

PMAS- Arid Agriculture University, Rawalpindi for his encouragement, guidance,

professional suggestions and much useful advice through the period of this study. A

special debt of gratitude to Prof. Dr. Riaz Ahmad and Dr. Yamin Bibi, my

committee members for their help and advice during research work. My great

thanks and deep gratefulness goes to all teachers in the Department of Botany for

their honest advice in laboratory work and to all my colleagues and friends

(especially Tahira Shamshad, Nagina Gillani, Nabeela Hanif, Wajiha Seerat) for

their cooperation.

I can’t find any words to express my sincere appreciation and gratitude to

my parents and brother Irfan-ul-Haq Qureshi for their endless support,

encouragement and care.

Financial support of this study by the Higher Education Commission of

Pakistan through International Research Support Initiative Programme (IRSIP) is

highly acknowledged as a result of which I was able to work without financial

constraints and visited The University of Queensland, Australia for a period of six

months where I performed crucial part of my work. I am also thankful to my

foreign supervisor Prof. Dr. Steve William Adkins providing me every possible

benefit and support during my stay there.

Finally, thanks to whoever helped me whose name is not mentioned here.

Thank you to all…

HUMA QURESHI

xvi

ABSTRACT

Pothwar region, Pakistan is a hot spot for biodiversity, but the vegetation is

constantly under pressure of exotic invasive plants. Phytosociological studies help

to understand extent of biological invasion. Multiple analyses of ecological

parameters at different locations derive general explanations of impact on species

diversity in plant communities. The current study assessed impact of selected

invaders viz. Parthenium hysterophorus L., Lantana camara L., Xanthium

strumarium L. and Broussonetia papyrifera (L.) L’Herit. ex Vent. invasion on

native flora in Pothwar region of Pakistan. Paired plot experimental design with

two categorical factors; invaded and non-invaded (control) under same habitat

conditions was used for sampling. Differences in number of species (S), abundance

(N), species richness (R), evenness (Jꞌ), Shannon index of diversity (Hꞌ) and

Simpson index of dominance (λ) were calculated using PRIMER-7 software

package. Ecological indices were compared between invaded and control plots by

t-test series using IBM SPSS v. 21 software. Control plots harbored by an average

of 0.9, 1.74, 1.28 and 1.3 more individuals per 10m2 respectively. The control

category was diverse (Hꞌ=1.73, 2.56, 2.15, 2.00) than invaded category (Hꞌ=1.53,

1.56, 1.65, 1.82) for four studied invaders. Similarly, control plots showed higher

value of Jꞌ and λ for all the studied sites. The higher value of species richness in

control plots shows heterogeneous nature of communities and vice versa in invaded

plots. The lower value of index of dominance in invaded plots shows less sample

diversity than control ones. This decrease in number of species directly affects α-

diversity in invaded plots. At multivariate scale, ordination (nMDS) and ANOSIM

showed significant magnitude of differences between invaded and control plots in

xvii

all sites. The decrease in diversity indices in invaded over control sites indicated

that plant communities become less productive due to invasion; hence a threat to

plant diversity. Invasion impact was observed as Lantana camara > Xanthium

strumarium > Parthenium hysterophorus > Broussonetia papyrifera. Results

suggested appropriate control measures for studied invaders.

Radish seed germination bioassay with methanol extracts harboring 0.05

gmL-1 of root, leaves, flowers and stem of selected invaders indicated L. camara

leaves and X. strumarium fruits as most phytotoxic plant parts. Fractionation and

bioassay guided isolation of allelochmicals from L. camara leaves against monocot

(Phalaris minor Retz. & Avena fatua L.) and dicot (Rumex dentatus L. &

Chenopodium album L.) weed test species provided evidence about herbicidal

potential of test plant species. Among ethyl acetate, hexane, chloroform and

aqueous methanolic extract fractions, ethyl acetate fraction was shown to be most

inhibitory to selected weed test species. Through flash column chromatography

using mobile phase of Hexane : Ethyl acetate (60:40), 31 fractions were collected

in small vials and tested for inhibition activity against radish seeds. Fraction with

highest inhibition activity was subjected to GC-MS analysis that shows compound

as ‘Vitexin’. To the best of our knowledge Lantana camara leaves have not been

previously reported to possess flavonoid compound ‘vitexin’ and tested against

weeds of wheat crop. So this investigation has provided a clue about its herbicidal

importance for further research.

1

Chapter 1

INTRODUCTION

1.1. BIOLOGICAL INVASION: AN ISSUE OF GLOBAL CONCERN

Biological invasion is defined as ‘dispersal of species to habitats where it

was absent previously followed by its proliferation, spread, persistence and

negative effects on biodiversity, health and/or economy’ (International Union for

Conservation of Nature, 2015). Biological invasion is a form of biological

pollution. Biopollution is impact of alien species on ecological quality. It includes

modifications/deterioration of habitats, competition with and replacement of

native species, spread of pathogens and genetic alteration within population

(Holm et al., 1991; Alpert, 2006). Exotic plants, animals, insects and other living

organisms are biological pollutants, among which plants are probably the worst,

attributed to their huge biomass (Florece and Baguinon, 2011). According to

‘Invasive Species Specialist Group (ISSG)’, out of 100 worst invaders in the

world, 32 are plants (Holzmueller and Jose, 2009).

Historically, alien plant species spread through exploration and colonization

to new habitats. Today, ‘natural entry’ and ‘introduction’ are dispersal modes of

invaders (Table 1). The pace of spread of invasive plants is increasing with

economic activities including trade, travel and technology worldwide (Pysek and

Hulme, 2005). Invasive plants contribute to soil erosion, alteration in native flora,

human/animal health risk, modification of ecosystem processes (hydrology, soil

nutrient composition), reduction in agricultural yield and spread of vector borne

diseases (Marwat et al., 2010; Dogra et al., 2010; Etana, 2013; Qureshi et al.,

2014).

1

2

1.2. INVASIVE ALIEN PLANTS (IAP): DEFINITION AND CONCEPTS

A species introduced to areas beyond its native range, established in wild

and spread substantially from its point of introduction is referred as invasive

species. Invasive species cause ecological problems, threaten global biodiversity,

introduce diseases or incur economic costs (Jeschke et al., 2012). Due to their

potential to outcompete and replace native species, invasive plant species are 2nd

leading cause of biodiversity loss after habitat destruction (Malik and Husain,

2007; Gaertner et al., 2009). Researches and publications on topic of biological

invasions have been numerous since 1990s. From this perception, invasion biology

is a young discipline (Jeschke et al., 2012).

Not all but a few introductions tend to harm native flora in introduced

range. Successful invasions are generally influenced by adequacy of seeds and

subsequent ability of dispersal (Rejmanek and Richardson, 1996), suitability of

habitat and ecological niche (Sax and Brown, 2000), environmental adaptability

(Sage, 2004), ability to escape diseases, parasites and predators (Davis et al., 2000)

and ability to overcome biological, physical & environmental thresholds (Malik

and Husain, 2007). Hypotheses for success of invasive plants in new habitats are

summarized in Table 2.

1.3. ALLELOPATHY: A NOVEL WEAPON FOR INVASION SUCCESS

Allelopathy is defined as biochemical interaction of promotion/inhibition

within plants. Typically, it is negative in character where donor plant disrupts

physiological processes of recipient plant by inhibition of enzyme activity (Soltys

et al., 2013). Allelopathy is important factor influencing invasion of exotic plants.

Many of allelochemicals have anti-microbial, anti-fungal and anti-herbivore effects

3

Table 1: Modes of entry of alien species into new habitats and likely vectors

(Source: Alpert, 2006)

4

Table 2: Hypotheses for invasiveness of alien species

6

which enhances competitive advantage of invaders in introduced range (Meiners et

al., 2012).

These compounds have been observed in many invasive plants and

involved in their invasion success. Allelopathic effects of important invaders e.g.

Centaurea maculosa Lam. (Ridenour and Callaway, 2001), Alliaria petiolata (M.

Bieb.) Cavara & Grande, (Callaway et al., 2008), Solidago canadensis L.

(Abhilasha et al., 2008) and Lantana camara L. (Sharma et al., 2005) contribute to

their invasion success.

1.4. INVASIVE PLANT SPECIES IN PAKISTAN

Invasion of newly colonized areas by alien plants is problem of global

significance. In Pakistan, among 700 alien species of vascular plants, 73 are listed

as invasive (Qureshi et al., 2014). Parthenium hysterophorus L., Lantana camara

L., Broussonetia papyrifera (L.) L'Her. ex Vent., Eucalyptus camaldulensis

Dehnh., Xanthium strumarium L., Prosopis juliflora Sw. (DC.), Eicchornia

crassipes Mart. (Solms), Leucanea leucosephala Tant. De wit., Salvinia molesta

Mitch., Bromus unioloides Kunth are top invaders of economic importance in

Pakistan (Qureshi et al., 2014).

1.5. ALLELOPATHY: DEFINITION AND CONCEPTS

The term ‘allelopathy' is derived from two Greek words: `allelon' meaning

`each other' and `pathos' meaning `suffering', coined by Hans Molisch (Austrian

plant physiologist), in 1937. The International Allelopathy Society defines

allelopathy as “Biochemical sympathetic (positive) or pathetic (negative)

interaction within plants and microorganisms”. However, ecologists favor negative

effects in allelopathy (Gross, 1999).

7

In allelopathic interactions, there is production and release of chemical

substances by plants into environment by root exudation, volatilization and

leaching (Fig. 1) from aboveground parts or by decomposition of plant material

(Cheema et al., 2013). Chemicals involved in this process are called allelopathins,

allelochemics or allelochemicals. Allelochemicals are mostly secondary

metabolites with a few exceptions of primary metabolites (Table 3). Even with this

diversity, allelochemicals have basically four precursors: shikimic acid, acetyl

coenzyme A, deoxyxylulose phosphate and mevalonic acid synthesized during

shikimate or soprenoid pathway (Weir et al., 2004; Gantayet et al., 2011). Phenolic

acids and terpenoids are common types of allelochemicals (Shankar et al., 2009).

Allelochemicals from donor plant disrupt physiological processes e.g.

photosynthesis, respiration, water and hormonal balance of recipient plant by

enzyme activity inhibition (Soltys et al., 2013).

1.6. ROLE OF ALLELOPATHY IN AGRICULTURAL IMPROVEMENT

‘Yield maximization’ is the last word of modern agriculture for food

security of ever increasing population of the world. Maximizing world’s

agricultural efficiency depends largely on controlling pests and diseases. Although,

demand for insecticides and fungicides by successful breeding for resistant

cultivars has reduced, use of herbicides is still increasing globally. Application of

heavy doses of herbicides is directly/indirectly causing negative impact on quality

of produce, human health and environment (Bhadoria, 2011). Weeds are ‘plants

growing in an undesired location’. Weeds compete with crops for resources thus

lower crop yields and contaminate the crop with their seeds thereby extending the

problem into succeeding growing seasons.

8

Fig. 1: Mechanism of plant interference involving competition and allelopathy

Volatilisation

Leaching Decaying plant material

Root exudation

③

④

②

①

9

Table 3: Common allelopathic compounds and representing plants

Compound Representative species

Phenolic

compounds

3,4-Dihydroxybenzoic acid Delonix regia

3,4-Dihydroxybezaldehyde Delonix regia

3,4-Dimethoxyacetophenone Asparagus officinalis

3,4-Dihydroxycinamic acid Delonix regia

3,5-Dinitrobenzoic acid Delonix regia

Caffeic acid Helianthus annuus

Chlorogenic acid Helianthus annuus

Ferulic acid Grass and fern species

Fusaric acid Fusarium oxysporum

Gallic acid Celtis laevigata

Gentisic acid Celtis laevigata

Hydroquinone Arctostaphylos

glandulosa

Isochlorogenic acid Helianthus annus

Medicagenic acid Medicago spp.

Neochlorogenic acid Helianthus annus

o-Hydroxyphenylacetic acid Oryza sativa

p-Coumaric acid Grass and fern species

Phloroglucinol Pluchea lanceolata

p-hydroxybenzaldehyde Sorghum bicolor

p-Hydroxybenzoic acid Miscanthus species

Polyacetylenic methyester Solidago altissima

Quercetin Salsola kaki

Scopoletin Celtis laevigata

Scopoline Celtis laevigata

Syringic acid Grass and fern species

Vanillic acid Grass and fern species

10

Alkaloids 6,6’-Dihydroxythiobinupharidine Nuphar lutea

Caffiene Coffea Arabica

L-Azetidine3carboxylic acid Delonix regia

Nupharolutine Nuphar lutea

11

Among 7,000 identified weed species, fewer (~200-300) are particularly

troubling to world’s farmers (Vyvyan, 2002). There are many strategies used to

control weeds (physical, mechanical, chemical and biological). The chemical

method is most popular for decreasing negative effects of weeds in crops. But,

herbicide-resistant weeds have resulted due to extensive use of synthetic chemical

herbicides, and public concerns over impact of synthetic herbicidal chemicals on

environment and human health are increasing. These concerns are shifting attention

to natural products based weed control technologies (Ferreira and Reinhardt, 2016).

1.7. ROLE OF ALLELOPATHY IN WEED MANAGEMENT

Weeds have co-evolved with crops and hence are an integral component of

agroecosystem. They compete with crops for resources and cause economic losses

globally (Sayili et al., 2006). Among pests, weeds have the greatest negative

impact on crop productivity (Dayan and Duke, 2014). Huge amounts of synthetic

chemical herbicides are used to manage weeds. Heavy doses of synthetic chemicals

for weed control have encouraged herbicidal resistance in weeds, risking human

health and the environment. Natural compounds, known as “bioherbicides” pose a

big area for environmentally safe herbicides, based on compounds produced by

living organisms (Soltys et al., 2013).

Allelopathic weeds and their allelochemicals have wide application

prospects in increasing crop production, plant protection and biological control

(Yan et al., 2000). Putnam (1988) listed 6 classes of allelochemicals viz. alkaloids,

cinnamic acid derivatives, benzoxazinones, cyanogenic compounds, ethylene and

flavonoids as natural herbicides. Allelochemical features that make them potential

bioherbicides are: similar mode of action to synthetic herbicides, total/partial

12

solubility in water for their easy application without surfactants, environment

friendly chemical structures with higher oxygen and nitrogen contents and non-

halogenated molecules decreasing environmental half-life thus preventing

accumulation in soil (Dayan et al., 2009; Soltys et al., 2013). Allelopathic

compounds having role in weed control are: allyl isothiocyanate (black mustard),

isoflavonoids and phenolics (Trifolium spp.; Melilotus spp.), fatty acids (buck

wheat), scopoletin and phenolic acids (Avena sativa), dhurrin, sorgoleone

(sorghum, sudangrass), hydroxamic acids (cereals) (Bhowmik and Inderjit, 2003).

1.8. WEEDS OF WHEAT CROP

Wheat is staple food in Pakistan; supplying average of 72% caloric energy

in daily diet. In Pakistan wheat consumption per capita is 124 kg/annum. Variation

in annual yield of wheat due to several factors affects social balance and economy

of the country (Rashid et al., 2016). Despite, ranked among top ten country in

production, average grain yield is far below than other wheat producing countries

of the world (Fig. 2). Average world wheat yield is 3210 kgha-1

while in Pakistan;

it is 2787 kgha-1

(Mehmood et al., 2014). Though a bumper crop of 21 million-tons

was harvested during 1999-2000 (Khattak et al., 2001), the average ha-1

yield of

wheat is 2.06 tons that is far behind average yield of 2.71 tons/ha (Table 4). Loss in

wheat yield due to weed competition is greater than combined effect of diseases

and insects in Pakistan (Shehzad et al., 2012). Weeds compete with crop for

nutrients, moisture, light and space. Weeds increase harvesting costs, reduce

produce quality, block water ways and increase the fire hazards. A yield loss of 20-

40% is estimated due to weeds in wheat crop which amount to ~28 billion rupees at

national level (Marwat et al., 2013).

13

Fig. 2: World Wheat Production (million tons)

Source: Pakistan Agriculture Research Council (PARC)

14

Table 4: Projected wheat requirements, area and yield in Pakistan (2010-2030)

15

Table 5: Reccomended wheat varieties in Pakistan

16

A yield loss of 20-40% is estimated due to weeds in wheat crop which

amount to ~28 billion rupees at national level (Marwat et al., 2013). The major

competitive weeds of wheat crop are Phalaris minor, Avena fatua, Cirsium avense,

Ammi visnaga, Convolvulus arvensis, Carthamus oxycantha, Chenopodium album,

Euphorbia helioscopia and Rumex dentatus (Hussain et al., 2007).

1.9. SELECTED INVASIVE SPECIES

1.9.1. Broussonetia papyrifera (L.) L’Herit. ex Vent. (Paper Mulberry)

Paper mulberry is a medium to large, deciduous, dioecious tree. It is native

to East Asia, common in China & Japan and widespread in tropical & subtropical

regions. Paper mulberry is listed amongst six worst invader plants in Pakistan

(Malik and Hussain, 2007). The tree was intentionally introduced during 1960s in

Islamabad and Rawalpindi as an avenue tree, but in a ~30 year period, it has

become highly invasive in many localities (Marwat et al., 2010). Adverse effects of

Broussonetia on ecosystem include damaged ecosystem services, reduced natural

biodiversity, negative effects on human health, choking of sewerage lines in urban

set-up and increased crow population (acting as seed dispersal vectors). Its pollen

causes rhinitis and asthma (Hsu et al., 2008; Huston, 2004). Adaptability to

different habitats, rapid growth rate, vegetative regeneration, effective dispersal by

birds and allelopathy contribute to its invasion success (Malik and Hussain, 2007).

1.9.2. Lantana camara L. (Sage plant)

Sage plant is medium-sized, perennial, aromatic, ornamental shrub. It is

native to neotropics, now established in over 60 countries and rated among top ten

worst weeds around the world (Qureshi et al., 2014). The shrub was introduced

throughout the tropics and subtropics during late 19th

century as hedge plant

17

(Shaukat et al., 2003). Adverse effects of Lantana on ecosystems include damaged

ecosystem services, soil erosion, reduced native biodiversity, encroaching of

agricultural lands, animal poisoning, sheltering disease vectors and allelopathic

effects on associated flora. Lantana is allelopathic plant and interferes with growth

and development of wide range of plants, including ferns, vines, crops and other

plants even its own populations (Ambika et al., 2003). Phenotypic plasticity, high

reproductive potential, immunization to grazing pressure, allelopathy and fire

tolerance contributes to its invasiveness (Bhakat and Maiti, 2012).

1.9.3. Parthenium hysterophorus L. (Congress weed, White top)

Parthenium is an annual, aromatic herb. It is native to Mexico and South &

Central Americas. It was accidentally introduced to many countries and now has

become a troublesome agricultural and rangeland weed in parts of Asia, Africa,

Australia and the Pacific Islands. Parthenium is documented among world’s top ten

weeds (Khan et al., 2014; Tamado and Milberg, 2000). It is assumed to move in

India along food grains trade from USA and supposed to enter Pakistan via road

links where automobiles cross at many places every day (Nath, 1988). Parthenium

weed was stated in Pakistan during 1980s from Gujarat, Punjab (Razaq et al.,

1994). Since then, it spread rapidly all through the Islamabad, parts of Khyber

Pukhtunkhwa and Punjab Province. Parthenium affects crop production,

biodiversity and animal & human health (Shabbir, 2013). Wide environmental

adaptability, photo and thermo-insensitivity, drought tolerance, high small light

weighed seed production (easy long distance travel via wind, water, animals, birds

and vehicles), longevity of seeds in soil seed banks and allelopathy contribute to its

invasiveness (Shabbir and Bajwa, 2006; Hassan et al., 2012; Khan et al., 2014).

18

1.9.4. Xanthium strumarium L. Syn. X. occidentale, X. pungens (Common

Cocklebur)

Common Cocklebur is an annual herb. It is native to North and South

America. It was introduced to Pakistan from Afghanistan in early 1980s during the

Afghan war. Massive migration of Afghan nomads and their livestock resulted in

small to large patches of this aggressive weed. Spiny fruit clinging to wool of

sheep/goats has been major force of its spread. Now it is ubiquitous weed found in

orchards, agricultural and wastelands (Hashim and Marwat, 2002). Reduced

biodiversity, negative effects on yield of row crops (soybean, cotton, maize and

groundnut), hosting crop pathogens, cattle poisoning and contamination of sheep

wool by lodging of burs are adverse effects of the weed. Facilitated dispersal of

prickly burs by adhering to human clothing, as contaminant of wool, by water,

viability of seeds up to five years, photo-insensitivity and allelopathy contribute to

invasiveness of the weed (Hussain et al., 2013; Qureshi et al., 2014).

1.10. OBJECTIVES

1. Assess ecological impacts of selected invasive plants to biodiversity in

Pothwar Region of Pakistan

2. Ascertain whether selected species get allelopathic advantage for their

invasion

3. Investigate herbicidal potential of selected invasive species against weeds

of wheat crop

4. Profile allelochemicals of herbicidal activity.

19

a: Broussonetia papyrifera b: Parthenium hysterophorus

c: Lantana camara d: Xanthium strumarium

Fig. 3: Studied Invader Species in Pothwar region of Pakistan

b: Lantana camara

20

Invasive; Naturalized; Not invasive; Not recorded

Fig. 4: Distribution map and invasion status of selected invaders around the globe

a: Parthenium hysterophorus b: Lantana camara

c: Broussonetia papyrifera d: Xanthium strumarium

21

Chapter 2

REVIEW OF LITERATURE

2.1. PLANT INVASIONS - AN ECOLOGICAL EXPLOSION

There has been rapid acceleration in rate of invasions attributed to

expansion of disturbed habitats associated with rapid human population growth.

Introduction of invasive plants may change structure and function of ecosystem e.g.

succession, species composition, biomass, net primary production and nutrient

cycling at population, community and landscape levels (Collier and Vankat, 2002).

Plant invasions deplete native species diversity, alter community

composition and effect ecosystem processes thus cause ecological and economic

imbalance (Kunzi et al., 2015). Exotic plants competitively exclude native

neighbors in recipient communities. A number of studies have provided data on

effects of exotic plants on altering community composition and reducing

indigenous diversity. These studies assumed diverse mechanisms that generate

significant invasion impacts. Among these processes are; allelopathy, competition

and native ecosystem characteristics alteration (Odat et al., 2011). Direct

competition with native flora may result in monocultures of exotic species e.g.

Psidium cattleianum in Mauritius and Parthenium hysterophorus in Pakistan,

Australia and India (Dogra et al., 2010). In various parts of the world, as many as,

80% of endangered species are threatened by alien invasive species (Pimentel et

al., 2005).

Invasion impacts of Asian shrub (Lonicera maackii) in secondary forests in

southwestern Ohio and adjacent states were reported by Collier and Vankat (2002).

21

22

In plots below crowns of L. maackii, lower species richness and abundance was

reported: overall species (53 and 63% lower richness and cover respectively), tree

seedlings with canopy potential (−41 and −68% lower richness and density) and

seed + bud bank (−34 and −33% richness and density). Individual taxa showed

lower abundance in plots below L. maackii as: 56% seed + bud bank, 86% herbs

and 100% trees. It was concluded that species richness and density decrease in

forests with extended dwelling time of L. maackii.

Relationship between invasion of Carpobrotus spp., Ailanthus altissima,

Oxalis pes-caprae and native plant communities diversity in Mediterranean islands

[Sardinia (Italy), Lesbos (Greece), Corsica (France), Porquerolles (France),

Menorca (Spain), Mallorca (Spain)] in different habitats (Roadside, abandoned

field, urban and ruderal habitat, Temporary stream) was reported by Vila et al.

(2004). Species abundances were recorded in close-paired plots. For Carpobrotus

spp., diversity was lower in invaded plots (difference ranging from 1.17 in

Mallorca-0.71 in Menorca). There was non-significant effect of island (F3,104=1.84,

p=0.145) and habitat (F1,106=0.395, p=0.531) on change between control and

invaded plots. For Ailanthus altissima, in Menorca, plant species diversity was

lower (df=16, t=3.24, p=0.005) in invaded (Hꞌ=2.79) than control plots (Hꞌ=3.38).

There was island (F3,82=2.97, p=0.036) and habitat (F2,75=6.21, p=0.003) interaction

effect on intensity of diversity change between control and invaded plots. For

Oxalis pes-caprae, diversity was lower in invaded plots in Sardinia (df=29, t=3.85,

p=0.001) and Lesbos (df=29, t=9.11, p<0.001). Diversities differed between control

and invaded paired plot in ruderal habitats (df=14, t=2.18, p=0.046), abandoned

fields (df=32, t=7.04, p<0.001) and in shrub land/forest habitat (df=57, t=2.22,

23

p=0.039) but not in orchards (df=11, t=0.15, p=0.886). The change between control

and invaded plots was effected by island (F4,112=19.08, p<0.000). and habitat

(F3,113=4.89, p=0.003).

Invasion effects of Impatiens glandulifera in Czech Republic were studied

by Hejda and Pysek (2006) using space for time substitution approach. Differences

in number of species (N), index of evenness (J) and Shannon index of diversity (H′)

were compared between control and invaded paired plots. Control plots harbored

0.23 more species per 16 m2. Higher values of evenness (J) and Shannon index of

diversity (H′) were reported. Study indicated restoration of plant communities

once I. glandulifera is removed.

The invasion effects of Ageratum conyzoides in Shivalik hills India was

studied by Dogra et al. (2009). Monocultures of this invader reduced number of

species (N) by 32.10%, dry biomass by 48.46% and α-diversity by 41.21% than

control plots. It was concluded that A. conyzoides adversely affects diversity and

productivity of indigenous vegetation.

Invasion effects of exotic shrub (Acacia saligna) on plant diversity of

northern part of Jordan were studied by Odat et al. (2011). Total number of species,

Simpson's (λ), Shannon's (Hꞌ) and Margalef's (R) diversity index of associated

species in 0.5m2 quadrate with six random replicates outside and inside the canopy

of A. saligna were studied. It was found that Shannon and Simpson diversity

indices reduced under A. saligna canopy (1.8800, 0.8831) compared to outside

canopy (2.210, 0.876) of trees, respectively (F=14.99,12.46; p<0.001). Competition

and intrinsic characters of A. saligna were suggested as possible mechanisms for its

invasion.

24

Table 6: Literature review for impact analysis of invasive plants around the globe

Invader species Species location Assessment

method

Assessment tools Conclusion(s) Reference

Chromolaena odorata (L.)

King & Robinson and

Parthenium hysterophorus

L.

Mysore district,

India

Curtis &

Mcintosh method

of vegetation

analysis

Frequency, density,

abundance

Heterogeneous infested sites

compared to semi-heterogeneous

non-infested sites

Sagar et al., 2015


L.

Jijiga zone,

Southeast

Ethiopia

Road transect

survey method

Evenness index (E),

Jaccard’s coefficient of

similarity (JCS)

Decreased composition and

diversity of vegetation in invaded

sites

Ayele et al., 2013

Ailanthus altissima Moldova Noua -

Berzasca

Direct

observation; GPS

spotting of

Ailanthus

specimens,

Biometric and

Statistical

processing

Measurement of trunk

with forest die, tree

height with

dendrometric pendulum

and locating species by

GPS

Species occurrence in clusters,

alignments and isolated specimens

Bostan et al., 2014


L.

Bilaspur, India Invaded and non-

invaded site

comparisons

Density, Frequency,

Importance value index

Threat to plant community

biological diversity in agriculture

fields

Kumari et al., 2014

Nassella neesiana (Trin. &

Rupr.) Barkworth

Yarramundi

Reach,

Australian

Capital Territory

Invaded and non-

invaded patches

comparison

Foliar cover and species

diversity

Reduced native plant diversity Faithfull et al.,

2008

Nassella neesiana (Trin. &

Rupr.) Barkworth

Temperate native

grasslands of

south-eastern

Invaded and non-

invaded patches

comparison

Plant richness Reduced species richness (species

m-2

)

Faithfull et al.,

2010

25


method


Australia

Hyparrhenia hirta (L.) Stapf Travelling stock

route in northern

New South

Wales, Australia

Invaded and non-

invaded plots

comparison

Plant richness Reduced native species richness Chejara et al., 2006

Acacia dealbata Link. Valley of river

Miño (Galicia,

Spain)

Visual

interpretation

Aerial photograph

interpretation and

analysis

Monospecific stands of invader Vazquez-de-la-

Cueva, 2014

Alternanthera philoxeroides China Invaded and non-

invaded plots

comparison

Importance value,

Patrick richness index,

Simpson diversity

index, Shannon-Wiener

diversity index, Pielou

evenness index

Increased diversity associated with

mall scale invasions while

decreased species diversity

associated with larger invasions

Wu et al., 2016

Sesuvium portulacastrum,

Potamogeton perfoliatus,

Parthenium hysterophorus,

Opuntia stricta, Operculina

turpethum, Malvastrum

coromandelianum,

Galinsoga parviflora,

Euphorbia tirucalli, Encelia

farinose

Saudi Arabia Invaded and non-

invaded plots

comparison

Density, Abundance,

Species richness, Cover

values (%)

Negative correlation of invasive

species with species dominance

Thomas et al., 2016

Megathyrsus maximus Mona Island,

Puerto Rico

Invaded and non-

invaded area

comparisons

Matrix projection

models

Reduced population growth rates in

invaded areas

Rojas-Sandoval et

al., 2016

Mikania micrantha Barandabhar Invaded and non- Simpson’s Index, Negative effect of Mikania species Basnet et al., 2016

26


method


Buffer Zone

Forest of

Chitwan National

Park, Nepal

invaded forest

areas comparison

Species density,

Individual tree basal

area per hectare

on stand structure of the forest

Asclepias syriaca L. Late successional

sandy old-fields

in the Great

Hungarian Plain

(Kiskunsag,

central Hungary)

Invaded and non-

invaded plots

comparison

Linear mixed effect

models

Negative effect on species cover Kelemen et al.,

2016

Acacia saligna North Nile Delta

of Egypt

Invaded and non-

invaded areas

comparison

Simpson diversity

index, Shannon-Wiener

diversity index,

evenness

Higher values of evenness and

lower values of species richness in

invaded plots

Abd-El-Gawad et

al., 2015

Triadica sebifera (L.) Small Stands along

Neches River,

near Diboll,

Texas

Paired-plot

experimental

design

Density, basal area,

quadratic mean

diameter, stand density

index and relative

density

Negative correlation of invader

species with density of native

species

Camarillo et al.,

2015

Quercus rubra Forest complexes

in the Poddębice

Forest District,

Poland

Invaded and non-

invaded areas

comparison

Number of species,

Shannon index,

Evenness, Sum of all

species cover index

Reduced native species richness and

abundance

Woziwoda et al.,

2014

Ipomoea cairica Guangdong

Province, China

Invaded and non-

invaded plots

comparison

Species diversity index Decreased plant richness and

diversity

Hui and

ShuangTao, 2012

Rubus niveus Scalesia forest in Correlation Vegetation height and Lower native species richness and Renteria et al.,

27


method


Santa Cruz

Island,

Galapagos

analysis between

R. niveus cover

gradient and

vascular plant

species richness,

cover and

vegetation

structure in plots

species cover, plant

species richness

cover 2012

Chromolaena odorata karst area of

Guangxi, China

Communities

comparisons from

different habitats

to establish three

sample plots in

artificial spare

woods (plot A)

and abandoned

land (plot B) of

Longzhou County

and shrubs on a

barren slope (plot

C)

Plant species richness Invasion of C. Odorata has an

adverse effect on biodiversity

GaoZhong et al.,

2012

Chromolaena odorata Tropical Sal

forest, Nepal

Invaded and

uninvaded

understory

vegetation

comparisons

Species richness Invaded plots were associated with

fewer species than uninvaded plots

Thapa et al., 2016

Fallopia japonica (Houtt.)

Ronse Decraene , F.

sachalinensis (F. Schmidt)

Czech Republic Invaded and

uninvaded plots

comparison

Sørensen index of

similarity, Shannon

diversity index (H′),

Reduced species richness, evenness

and diversity in invaded plots

Hejda et al., 2009

28


method


Ronse Decraene, F.

×bohemica (Chrtek &

Chrtková) J. P. Bailey,

Rumex alpinusL.,

Heracleum mantegazzianum

Sommier & Levier,

Rudbeckia laciniata L., Ster

novi-belgii L. agg.,

Helianthus tuberosus L.,

Solidago gigantea Aiton,

Lupinus polyphyllus Lindl.,

Imperatoria ostruthium L.,

Mimulus guttatus DC,

Impatiens glandulifera

Royle

evenness (J)

Ageratum conyzoides,


and Lantana camara

Shivalik hills of

Himachal

Pradesh, India

Invaded and

uninvaded plots

comparison

Density, abundance,

frequency, Basal area,

dominance, Simpson’s

Index of dominance,

evenness, Margalef’s

index of richness, index

of similarity and

dissimilarity

Reduced diversity, evenness and

richness of native species

Dogra et al., 2009

Ageratum conyzoides Shivalik hills of

Himachal

Pradesh

(Northwestern

Himalaya), India

Invaded and

uninvaded plots

comparison

Average Fresh Biomass

(g/m2 ), Average Dry

Biomass (g/m2 ),

Margalef Index of

Richness (R1),

Shannon’s Index of

Diversity (H’),

Ruduced productivity and diversity

of in invaded areas

Dogra et al., 2009

29


method


Simpson’s Index of

Dominance (λ),

Evenness (Es),

Similarity and

Dissimilarity index

Lantana camara Vindhyan plateau

in the

Sonebhadra

district of Uttar

Pradesh, India

Habitats with

different level of

canopy cover

were analyzed

Tree canopy cover (%),

Lantana cover (%),

Total herb cover (%),

Shannon-Weiner index

Modified spatial pattern of

herbaceous plant species

Sharma and

Raghubanshi, 2011

Prunus serotina Temperate,

mixed forest of

Compie`gne

(Northern

France)

Invaded and non-

invaded stand

vegetation

comparisons

Species richness,

number of rare species,

unweighed and weighed

CSI, herb layer cover,

generalist-specialist

measure θ, Rao’s

quadratic entropy FDQ

trait convergence and community

specialization, and reduced grain of

local heterogeneities

Chabrerie et al.,

2010

Elaeagnus Angustifolia L. West and central

Pontic desert

steppe zone

Comparison of

releves in vicinity

of windbreaks

established by E.

Angustifolia

Czekanowski-Sorensen

coefficient of similarity,

average percentage of

coverage

Incresed negative impact of E.

angustifolia when escaped from

windbreaks into wild

Sudnik-

wójcikowska et al.,

2009

Alternanthera philoxeroides Shangrao City,

China

Invaded and non-

invaded

communities

comparison

variance ratio (VR), chi-

square (χ2) correction

test, Jaccard index and

improved Godron M’s

measure

Negative association in plant

communities invaded by A.

philoxeroides. Decreased native

community stability and decresed

number of species

Lian-Jin and Tao,

2009

30


method


Microstegium vimineum

(Trin.) A. Camus

Mixed-hardwood

forest in

Tennessee

Microstegium

understory pre-

and post-canopy

disturbance

comparisons

Species richness of

native woody species.

Simpson’s and

Shannon’s diversity

indices

Decline in native woody species

stems/hectare with M. vimineum

cover

Oswalt et al., 2007

Alternanthera philoxiroides Nanjing, China Square intercept

method in plots

Abundance, frequency,

cover, importance value

Change in species composition of

weed community and decrease in

species diversity with increased

dominance of alligator weed

Jin-Cheng and

Sheng, 2006

Impatiens glandulifera Riparian

communities in

Czech Republic

Comparison of

invaded and

uninvaded sites

under the same

habitat conditions

(Space for time

substitution), and

removal of

invader species

from

experimental

plots

Number of species,

evenness, Shannon

diversity index and

importance values

Dominance of I. glandulifera at

expense of native nitrophilous

dominants

Hejda and Pysek,

2006

Juniperus Occidentalis Modoc, Lassen,

and Siskiyou

counties in

northeastern

California

Line-point

intercept

technique

Species richness,

understory cover,

Cheatgrass cover, Site

productivity

Change in community structure and

productivity. Reversal of change on

removal of western juniper but

increased opportunities for invasion

of cheatgrass

Coultrap et al.,

2008

Euphorbia esula L., Cirsium

arvense L.

Rocky Mountain

National Park,

Invaded and non-

invaded plot

Plant species diversity,

percent cover and

Higher richness of plant species and

relative cover (%) of sedges, herbs,

moss, lichen and fallen litter on

Pritekel et al., 2006

31


method


USA comparison frequency non-invasive plots

Syzigium jambos (L.) Alston Tropical

Premontane

Forest, Costa

Rica

Plot analysis Relative abundance of

S. jambos/plot and

density of other tree

seedlings, significance

of relationship between

S. jambos and coffee

seedling

Negative relationship between

relative abundance of S. jambos and

tree seedlings

Avalos et al., 2006

Lantana camara L. Ol-Donyo Sabuk

National Park,

Kenya

Invaded and non-

invaded site

comparison

Shannon-wiener

diversity (Hꞌ) and

evenness (JI) indices

Lower species diversity but higher

plant density

Wambua, 2010

Parthenium hysterophorus,

Lanatana camara, Ageratum

conyzoides

Hilly state of

Himachal

Pradesh, India

Infested and non-

infested Plots

analysis

Importance value index,

index of dominance,

richness, evenness

index, index of diversity

Abrupt decrease in vegetation Kohli et al., 2004

Lonicera maackii Oxford, Ohio

area

Plot comparison

below and away

crowns of L.

maackii

Species richness, cover,

tree seedlings with

canopy potential,

seed1bud bank

Decrease in species richness,

density, and tree seedlings richness

decreased in forests

Collier et al., 2002

Parthenium hysterophorus Gamo Gofa,

Ethiopia

Comparison

among sample

sites

Importance value

index, Simpson`s

index, Shannon-

diversity index and

evenness index

Reduction in diversity indices with

dominance of parthenium

Gebrehiwot and

Berhanu, 2015

Spartium junceum Cuenca Alta del

Manzanares

nature reserve in

Comparison of

invader S.

junceum stands

Soil properties, standing

vegetation, temporal

soil seed bank contents

Higher nitrogen content,

chamaephytes and therophytes as

predominant growth forms in soils

Gavilán et al., 2015

32


method


Madrid with the native

Cistus ladanifer

community

and net primary

production (NPP) of

annual grasslands

under Spartium

Acacia saligna Northern part of

Jordan

Plant species

comparison

among outside

and inside canopy

of A. saligna

Total number of

species, Simpson's

diversity index,

Shannon's diversity

index and Margalef's

diversity index

Reduction in native plant species

diversity under canopy

Odat et al., 2011

Acacia longifolia Northern and

southern Portugal

Invaded or non-

invaded plot

comparison

Shannon’s Index,

Pielou’s Evenness and

Simpson’s Diversity

Decreased canopy cover in lower

stratum of studied habitats, and at

some sites with increased leaf area

index and reduced light intensity in

understory. Reduced species

diversity in some habitats by up to

50% in invaded areas

Rascher et al., 2011

Lantana camara Nairobi National

Park, Kenya

Invaded and un-

invaded sites

comparison

Simpson’s and

Shannon-Weaver

indices

Diverse and rich un-invaded shrub-

grassland and riverine than invaded

Simba et al., 2013

Ailanthus altissima,

Carpobrotus spp. And

Oxalis pes-caprae

Mediterranean

islands

invaded and un-

invaded plots

comparison

Shannon-weaver

diversity index

Decreased diversity in invaded plots Vila et al., 2004

Tithonia diversifolia

(Hemsly) A. Gray

IIe-Ife,

Southwestern

Nigeria

Invaded and

uninvaded areas

comparison

Shannon’s index of

diversity, evenness

index, index of

similarity and

dissimilarity

Decrease in diversity of invaded

areas

Oludare and

Muoghalu, 2014

33

Invasion impacts of Lantana camara in Nairobi National Park, Kenya were

investigated by Simba et al. (2013) through random sampling in control and

invaded sites. Two sample t-test results for Shannon diversity values for control

(H'=4.003) and invaded (H'=3.592) riverine showed variations (t121=2.01; p=0.047),

but no differences were found for shrub-grassland and forest (p<0.05). However,

results indicated that control shrub-grassland and riverine were more diverse.

2.2. EVIDENCE OF ALLELOPATHIC ADVANTAGE TO PLANT

INVASIONS

Successful invasion of plants in new habitats is facilitated by their ability to

colonize disturbed habitats, rapid growth and reproduction, short life cycle,

production of seeds in large quantities, vegetative propagation, early flowering and

seeding, different phenology from natives and pest and disease-resistance.

Recently, secondary metabolites are approved for ecological dominance of invaders

(Balezentiene, 2015). Invasive plants compete for space, light and nutrients more

than endemics and colonize to form monotypic stands hence wipe out native flora

(Tilman, 1997).

Allelopathy is important factor for distribution and abundance of species

within communities. Novel weapons hypothesis (NWH) proposes that “invasive

species have allelopathic compounds to which native plants have not adapted”. The

hypothesis assumes invasions from aspect of lack of evolutionary relationships

among native and invasive species (Callaway and Aschehoug, 2000). Support for

hypothesis comes from

(i) Meta-analysis of previous studies

(ii) Comparative experiments with native range and invaded species

34

(iii) Experiments with isolated chemicals from native and invaded ranges

(iv) Comparisons of chemicals produced by invasive and native species

(v) Comparisons of allelopathic effects of invasive and native plant species (Ni

et al., 2012).

Schinus terebinthifolius, an exotic plant invading peninsular Florida

inhabits disturbed areas, forms monotypic stands and transforms native vegetation.

In laboratory and greenhouse experiments, Morgan and Overholt (2005) reported

negative effects on germination and biomass accumulation of native Florida

species, Rivina humilis and Bidens alba by aqueous extracts of S. terebinthifolius

leaves. This study proved allelopathy phenomenon of S. terebinthifolius.

Considering metal-nutrient mobilization capability of allelochemicals, it is

hypothesized that allelochemicals could be involved in resource acquisition. Study

on invasive Centaurea diffusa producing 8-hydroxyquinoline (8HQ) allelochemical

in nutrient manipulation treatments in hydroponic culture was tested by Tharayil et

al. (2008). It was shown that: C. diffusa utilizes 8HQ for iron acquisition. In C.

diffusa, there is possibly unique mechanism for uptake of 8HQ- iron (Fe) complex.

This study outlined phytotoxicity and competitive advantage of 8HQ to C. diffusa.

Zhang et al. (2009) tested the hypothesis that invasive Solidago canadensis

allelochemicals affect soil borne pathogens. Two soil borne pathogens (Rhizoctonia

solani, Pythium ultimum) and Lycopersicon esculentum were experimented to

indicate pathogen activity in terms of damping-off and mortality of seedlings. S.

canadensis rhizome extracts inhibited pathogen activity in Petri dish and sand

culture, providing evidence of its allelopathic effects on pathogens activity.

35

Greer et al. (2014) tested litter and leachate from native Andropogon

gerardii and invasive Bothriochloa ischaemum to Schizachyrium scoparium and

Andropogon gerardii. Germination, below & above ground biomass, and survival

rates were determined. Results indicated that B. ischaemum litter or leachate

reduced germination, growth and survival of S. scoparium and A. gerardii while A.

gerardii treatments had no effect on any of the species. It was suggested that B.

ischaemum gain allelochemical competitive advantage.

Balezentiene (2015) assessed total phenolic content (TPC) of invasive

Heracleum sosnovskyi and Heracleum mantegazzianum along germination

suppression of ryegrass and rapeseed. It was suggested that invaders may acquire

spread advantage by inhibiting germination of neighboring species. TPC varied

with H. mantegazianum plant parts and leachate concentration. The highest content

of phenolic compounds (87.98, 92.06 mgmL-1

) was documented in leaf leachate

(0.2%) of H. mantegazzianum and H. sosnovskyi respectively. A complete

inhibition was observed by leaf extracts (0.2%) of Heracleum spp. Strong negative

correlations were found between TPC in Heracleum species and germination of

ryegrass (r=-0.7) and rapeseed (r=-0.8).

2.3. NATURAL PRODUCTS FOR PEST MANAGEMENT

From a long time ago, natural products are used as tools for pest

management. Essays on agricultural practices by Roman and Greek scolars

mentioned use of essential oils for pest control. In far east, during Shengnong Ben

Tsao Jing era, more than 200 pesticidal plants were known (AD25-220) (Yang and

Tang, 1988). In far east, during Shengnong Ben Tsao Jing era, more than 200

pesticidal plants were known (AD25-220) (Yang and Tang, 1988).

36

Table 7: Allelochemicals isolated from Invasive plants

39

Findings of insecticidal powders from Derris elliptica root and Chrysanthemum

spp. Flower heads led to identification of insecticidal rotenone and pyrethrum

respectively.

About 11% of global agricultural pesticides are natural products or

compounds tracing back to natural products (Dayan et al., 2012). Natural product

based discovery has been least effective in area of weed management. Among 70%

of registered active natural products based pesticide components, only 8% are bio-

herbicides and 7% are approved by USEPA (Cantrell et al., 2012). This is

astonishing fact because weeds have leading damaging impact among pests on crop

productivity (Pimentel et al., 2005), and weed control is most demanding concern

of farmers (Stokstad, 2013).

Bioprospecting of phytotoxic compounds has led to commercial herbicides

(e.g. bialaphos, glufosinate, triketone herbicides and pelargonic acid). The cineoles

and other monoterpenes in essential oils of aromatic plants (e.g. Salvia spp., Laurus

nobilis, Eucalyptus spp., Xanthoxylum rhetsa, Artemisia spp.) are phytotoxic.

Herbicide ‘Cinmethylin’ is cineole herbicide incorporating monoterpene backbone

of 1,4-cineole, with addition of benzyl ether moiety to lower volatility of product

(El-Deek and Hess, 1986).

Bioassay-guided purification of crude extract of Piper longum led to

isolation of natural herbicide ‘Sarmentine’. Sarmentine interrupt plant cuticle

leading by desiccation and eventual tissue death (Lederer et al., 2004). Natural

leptospermone is active component of Calistemon spp. Optimization of this

triketone compound lead to bioherbicide ‘Sulcotrione’ (Beaudegnies et al., 2009).

Sulcotrione cause decolorization of plant tissues by inhibiting enzyme p-

40

hydroxyphenylpyruvate dioxygenase (HPPD) thus disrupts synthesis of carotenoids

and chlorophyll (Dayan et al., 2012).

Phosphinothricin (glufosinate) and Bialaphos are broad spectrum

herbicides. Bialaphos is fermentation product of Streptomyces hygroscopis clusters,

herbicide in Eastern Asia. Bialaphos is bioactivated into phosphinothricin before

employing its herbicidal action. Glufosinate (synthetic form of phosphinothricin) is

chemically produced as herbicide in rest of the world (Senseman, 2007; Duke and

Dayan, 2011). Phosphinothricin moiety of Bialaphos possesses C-P-C bond. This

P-methylated amino acid is structural analogue of glutamate and acts as inhibitor of

glutamine synthetase required for glutamine production. Inhibition of this enzyme

results in reduction in cellular pool of glutamine. This interrupts photosynthesis and

leads to death within few days (Seto et al., 1999).

2.4. ALLELOPATHY IN WEED MANAGEMENT PRACTICES

Plant derived chemicals offer a great potential for environment friendly, and

comparatively safer alternatives to synthetic herbicides. During past thirty years,

allelopathy impacts on agriculture have been identified and discussed (Qasem et

al., 2001; Singh et al., 2001; Duke et al., 2002). Putnam & Duke (1974) revealed

possible utilization of allelopathic crops to destroy weeds including weed

suppressive crop development and intercrops, rotational or cover crops (Putnam

and Duke, 1978). Studies on cover crops and residues for weed suppression have

been published (Moyer et al., 2000; Petersen et al., 2001; Weston, 2005). One of

developments in science of allelopathy is the direct / indirect utilization of this

interaction for weed management or as a component of integrated weed

management (IWM) program.

41

OH

CH3

POH

O

NH2

O

Phosphinothricin (Natural herbicide)

O

O

O

O

Leptospermone

(Lead compound)

O

O O Cl

SO2Me

Sulcotrione

(Herbicide)

O

OH

1,4-ciniole

(Lead compound)

O

OH

O

Cinmethylin

(Herbicide)

O

CH3

CH3

OH

OH

CH3

Cyperine

OHOOC

NO2

Cl

CF3

Acefluorfen

Fig. 5: Natural product based herbicides

42

One of developments in science of allelopathy is the direct / indirect

utilization of this interaction for weed management or as a component of integrated

weed management (IWM) program. The diverse aspects of allelopathy that can be

used are:

2.4.1. Allelopathic Cover and Smother Crops

Cover crops grown during regular cropping periods are known as smother

crops due to their overshadowing effect. They improve nutrient status, reduce soil

erosion, conserve moisture and control noxious weeds. They are either utilized as

living mulch or residue. Rye is excellent example with wide potential to control

weeds, attributed to release of allelochemicals DIBOA and BOA. Rye residue in

sweet corn and pumpkin fields with one herbicide application provides excellent

weed suppression (Galloway and Weston, 1996). Fagopyrum esculentum Moench.

(Common buckweed), Helianthus annuus L. (Sun flower), Hordeum vulgare L.

(Barley), Vicia villosa Roth (Hairy vetch), and Sorghum sp. are other examples of

allelopathic smother crops.

2.4.2. Allelopathic Green Manure Crops

Terms cover and green manure crops are interchangeably used. Green

manure crops are usually incorporated into soil while green or on maturity. Many

allelopathic legumes and crucifers are used as green manures. Glycine max (L.)

Merr. (Soybean), Mucuna pruriens (L.) DC. (Velvet bean), Trifolium spp.

(Clovers), Vicia villosa (Hairy vetch) are green manure legumes (Batish et al.,

2002; Ohno and Doolan, 2001). Weed suppression potential of Velvet bean is

attributed to release of allelochemical L-DOPA (Fujii, 1999). Among crucifers B.

43

campestris, Brassica hirta, B. nigra, B. juncea and Lepidium sativum (Al-khatib

and Boydston, 1999) reduce weed incidence. Glucosinolates are responsible for

inhibitory effects of crucifers (Boydston and Hang, 1995).

Unfortunately, the effects of cover/smother/green manure crops to control

weeds are often inconsistent. In order to get 100% success some additional

management practices have to be applied. Harmful effects of allelochemials

towards crops, residence time of allelochemicals in soil, type of species, cultivar

and age of crop and structure of target species are other issues related to such crops.

2.4.3. Crop Residues and Allelopathy

Crop residues reduce the incidence of weeds, help in decreasing reliance on

synthetic herbicides and improve croplands (Batish et al., 2002; Weston, 2005).

Residues of crops like wheat, barley, rye and clover are reported to control weed

growth (Ohno and Doolan, 2001). The utilization of crop residues for weed

management depends upon the type of crop residue, its amount and mode of

placement, the cropping pattern and environmental conditions. But unfortunately, if

not properly managed crop residues can also affect crop plants by the release of

allelochemicals during decomposition. Some of crop residues may also affect

nitrogen fixation and hence soil fertility (Rice, 1984; Rice,

1995).

2.4.4. Searching for Allelopathic Traits Among Wild Varieties of Crop Plants

During selection and cultivation of high yielding varieties, allelopathic

traits in crops were gradually eliminated. So in contrast to wild varieties, modern

crop cultivars have little or no capability of reducing weed/pest incidence. Such an

44

observation has made in accessions of oat (Avena sativa L.), cucumber (Cucumus

sativus L.), Soybean (Glycine max), mustard (Brassica spp.), wheat and rice (Wu et

al., 1999; Dilday et al., 2001). The lost allelopathic traits can be incorporated in

high yielding crop varieties by conventional breeding programs or by modern

genetic approaches (PCRs, RAPD, RFLP etc.) (Foley, 1999). Studies have been

carried out in rice to screen accessions for their potential weed suppressing ability

with view to incorporate these traits into modern highly yielding varieties.

2.4.5. Allelochemicals as Nature’s own Herbicides

Along allelopathic interactions, allelochmicals have a great scope for

potential use as weed suppressants and future development of novel agrochemicals

based on them. These natural products exhibit a vast diversity and potential to be

used as bioherbicides. While commercial herbicides have ~20 mode of action

(MOA) sites (Duke, 2012) evidence from natural phytotoxin literature suggests that

there are many more viable MOAs. Some of allelochemicals from higher plants

have been widely explored against weeds e.g. ailanthone cineole, citronellol,

parthenin, artemisinin, sorgoleone, DIBOA, BOA, L-DOPA and caffeine.

Monoterpenes (cineole & citronellol) exhibit phytotoxicity against Echninochloa

crus-galli, Cassia obtusifolia L., Ageratum conyzoides L. and Parthenium

hysterophorus L. (Romagni et al., 2000; Singh et al., 2001). DIBOA and BOA

inhibit growth and development of velvetleaf, barnyard grass, crabgrass, and

prosomillet (Putnam, 1988).

Phytotoxicity-directed extraction and fractionation of aerial parts of

Mikania micrantha (Shao et al., 2005) led to isolation of sesquiterpenoids:

deoxymikanolide, dihydromikanolide and 2,3-epoxy-1-hydroxy4,9-germacradiene-

45

12,8:15,6-diolide. These allelochemicals inhibited germination and seedling growth

of test species. Deoxy-mikanolide possessed strongest allelopathic activity. In

bioassay with Lactuca sativa seeds, deoxymikanolide showed strong

(IC50=47mg/ml); dihydromikanolide showed weaker (IC50=96 mg/ml) while 2,3-

epoxy-1-hydroxy-4,9-germacradiene-12,8:15,6-diolide showed least (IC50=242

mg/ml) toxic effect on radicle length. 50 mg/ml Deoxymikanolide caused yellowish

lesions at root tips of lettuce seedlings while 250 mg/ml killed lettuce seedlings. To

evaluate their toxicity in natural habitats, three companion species in south China,

Pinus massoniana, Acacia mangium and Eucalyptus robusta were tested and

similar results were obtained.

Topal et al. (2006) examined herbicidal effects of ‘Catechol’ against

Cirsium arvense, Papaver rhoeas, Lamium amplexicaule, Sinapis arvensis,

Triticum vulgare and Hordeum vulgare. In comparison to 2,4-D (synthetic

herbicide), 13.64 mm of catechol had strong herbicidal result, as effective as 2,4-D

on field poppy.

Baratelli et al. (2012) investigated allelopathic potential of Terminalia

catappa L. leaves and fruits on Euphorbia heterophylla L., Lactuca sativa L. and

Commelina benghalensis L. Ethyl-acetate and dichloromethane fractions of fruit

ethanolic extracts showed highest activity in phytotoxicity bioassays. Vanillic, 2-

pentadecanone; ferulic, siringic, palmitic, p-coumaric and stearic acids were

characterized in dichloromethane fraction, and b-sitosterol-3-O-b-D-glucoside and

3,4,40-tri-O-methyl ellagic acid were isolated from it.

Shao et al. (2012) isolated xanthinosin from fruits of Xanthium italicum as

eco-friendly herbicide. The compound was bioassayed against Amaranthus

46

mangostanus, Lactuca sativa, Triticum aestivum and Lolium multiforum. 160µM

Xanthinosin significantly inhibited seedling growth of all test species. 4mM

xanthinosin, completely inhibited seed germination of all test plants.

Lotina-Hennsen et al. (2013) suggested Tricolorin A, isolated from

Ipomoea tricolor as biodegradable herbicide. Tricolorin A acted as pre- and post-

emergence plant growth inhibitor. In pre-emergence, it displayed broad-spectrum

weed control, inhibiting germination of Triticum vulgare, Lolium mutliflorum,

Physalis ixocarpa and Trifolium alexandrinum seeds. Tricolorin A inhibited seed

respiration and seedling growth. Respiration was suggested as one of targets of

Tricolorin A. At a concentration of 60µM, Tricolorin A acted as post emergence

plant growth inhibitor by reducing dry plant biomass by 62%, 37%, 33%, and 22%

for L. multiflorum, T. alexandrinum, T. vulgare, and P. ixocarpa, respectively, after

18 days of application.

Elhaak et al. (2014) studied allelopathic suppression effects of Silybum

marianum aqueous and solvent extracts against Avena fatua, Vicia sativa, Phalaris

minor, Euphorbia heliscopia, Trifolium resupinatum, Malva parviflora and wheat

cultivars Sakha 61, Gimiza 9 and Sakha 93. Distilled water and 0-80% ethanol and

acetone concentrations were used to extract phenolic compounds of S. marianum

plant parts. Extracted amount of phenolic compounds from seeds, flowers, leaves,

and stem of S. marianum were indicated as acetone>ethanol>water with a highest

value for plant flowers. Germination percentages of wheat cultivars were slightly or

not affected by plant extracts. Ethanol extracts completely inhibited germination of

Phalaris seeds while leaf extract did the same to Vicia and Malva weeds. Study

suggested use of S. marianum acetone extract, as safe natural bioherbicide.

47

Mengal et al. (2015) conducted experiments to observe response of weeds

and wheat crop to selected allelopathic weeds. The treatments included T1 = weedy

Check (Control), T2 = Chenopodium album (30%), T3 = Chenopodium album

(60%), T4 = Convolvulus arvensis (30%), T5 = Convolvulus arvensis (60%) and

T6=T2+T4. The allelopathic effect of C. arvensis (60%) showed significant impact

(P<0.05) on growth and yield traits of wheat with 81.66% wheat seed germination,

16.08cm spike length, 84.48cm plant height, 3.97g grain weight/spike, 43.33

grains/spike, 47.82g seed index (1000 grain weight) value and 4059 kg/ha grain

yield; while weed density of 33.33m-2

was documented 20 days after sowing, weed

fresh weight 61.00gm-2

, 11.00m-2

weed density at maturity , 11.71m-2

weed dry

weight with highest weed control percentage 50.42% . Treatments were ranked as

Chenopodium album (60%): 2nd

, C. album + C. arvensis (30+30%): 3rd

,

Convolvulus arvensis (30%): 4th

and Chenopodium album (30%): 5th

. It was

determined that water extract of Convolvulus arvensis (60%) may be used for weed

suppression in wheat crop.

Dayan et al. (2015) investigated MOA of Sarmentine isolated from fruits of

Piper spp. 100µM sarmentine induced membrane integrity loss in cucumber

cotyledon disc-assays, whereas 3 mM pelargonic acid was required for similar

effect. Sarmentine was 10-30x more active than pelargonic acid on velvetleaf, wild

mustard, crabgrass and redroot pigweed. Activity of 30µM sarmentine was

stimulated by light, suggesting possible interference of this compound with

photosynthetic processes. Complete inhibition of photosynthetic electron transport

was observed at same concentration of the compound. On thylakoid membranes,

Sarmentine compete for binding site of plastoquinone thus act as photosystem II

48

(PSII) inhibitor. Sarmentine inhibited enoyl-ACP reductase activity. Herbicidal

activity of sarmentine was suggested as complex process linked with multiple

action mechanisms.

49

Chapter 3

MATERIALS AND METHODS

3.1. ECOLOGICAL IMPACT ANALYSIS

3.1.1. Study Area

The Pothwar is north-eastern plateau in Pakistan, making the northern part

of Punjab. It edges Azad Kashmir (western parts) and Khyber Pakhtunkhwa

(southern parts). Pothwar Zone extends from 32.5˚N to 34.0˚N Latitude and 72˚E to

74˚E Longitude. It lies between Indus and Jhelum River. The plateau expanses

from salt range northward to foothills of Himalayas. The Pothwar region embraces

Jhelum (32.9405°N, 73.7276°E), Islamabad (33.73°N, 73.09°E), Attock (33.76°N,

72.36°E), Rawalpindi (73.04°E, 33.59°N), and Chakwal (72.85°E, 32.93°N)

districts. Total area of Pothwar region is 28488.9 Km2 (Rashid and Rasul, 2011).

Pothwar region has extreme climate with hot summers and cold winters. Weather is

divided into four seasons; Cold (December-March); Hot (April-June); Monsoon

(July-September) and Post-Monsoon season (October-November). This area

practices an average annual rainfall of 812 mm, about half of which occurs in

Monsoon months (July-September). The mean maximum temperature rises till the

month of June and then falls appreciably with advent of rains being coldest in

January (14.62-18.7°C). Average temperatures range from 14°C in January to 37°C

in June (Pakistan Meteorological Department University Road Karachi, Pakistan)

(Fig. 6). The region has broadly four types of soil; loess, river alluvium, residual

and piedmont alluvium. Due to dynamic climate and combination of hills and

plains, Pothwar region is rich in biodiversity. Native vegetation is characterized by

49

https://en.wikipedia.org/wiki/Plateau

https://en.wikipedia.org/wiki/Pakistan

https://en.wikipedia.org/wiki/Punjab,_Pakistan

https://en.wikipedia.org/wiki/Azad_Kashmir

https://en.wikipedia.org/wiki/Khyber_Pakhtunkhwa

50

open patches of grasses and forb species. Albizia lebbeck (L.) Benth., Acacia

modesta Wall., Abies pindrow (Royle ex D. Don) Royle, Cassia fistula L., Cedrela

toona Roxb. ex Rottler, Dalbergia sissoo Roxb., Dodonaea viscosa Jacq., Ficus

religiosa L., Ficus benghalensis L., Melia azedarach L., Olea cuspidata Wall. Ex

G. Don., Zizyphus jujuba Mill. and Zizyphus nummularia (Burm.f.) Wight & Arn.

are principle species in the region (Shabbir et al., 2012; Ghufran et al., 2013).

3.1.2. Experimental Design

Field work was carried out during July-August (being the maximum growth

period of plants), 2016. The effect of invasion was studied in each of five districts

(Attock, Chakwal, Jhelum, Islamabad & Rawalpindi). Ecological indices for

selected invaders were calculated and compared at various sites. The sampling

technique was random sampling. For each district, six invaded and six non-invaded

paired vegetation plots (each 3.16×3.16m in size, i.e., 10m2 in area) were sampled.

Plot of invaded vegetation (‘invaded plot’) where the invader showed dominance

was considered as ‘treatment’ and a second vegetation plot, ~0.5-1 km apart from

treatment, where invader has no dominance (‘non-invaded plot’) was considered as

the “control”. A total of 60 vegetation plots were sampled (consisting of six paired

samples per district, and hence 30 treatments; 30 controls for the entire Pothwar

region) (see Figure 7). Within each randomly chosen plot (10m2 in area), all

vascular plant species in control and invaded plots were identified to species level.

3.1.3. Data Analyses

Species frequency data were created and invasion impacts of selected four

invaders on local flora were assessed by calculating and comparing ecological

indices including Margalef’s index of richness, Shannon-Weaver index of diversity,

51

Simpson index of dominance and index of evenness for control and invaded sites.

These parameters were calculated as:

(i) Margalef ′s index of richness (R) =S−1

lnN

Where, N = Total number of individuals

S = Total number of species

(ii) Shannon-Weaver index of diversity (𝐻′) = − ∑ 𝑆𝑖=1 (

𝑛𝑖

𝑁× 𝑙𝑛

𝑛𝑖

𝑁)

Where, N = Total number of individuals of all species

n = Actual number of individuals of one species

(iii) Simpson index of dominance (𝜆) = 1 −∑ 𝑛𝑖

𝑆𝑖=1 (𝑛𝑖−1)

𝑁(𝑁−1)

N= Total number of individuals of all species

n = Number of individuals of one species

(iv) Index of evenness (𝐸) =𝐻′

𝑙𝑛𝑆

Where Hꞌ is Shannon’s index

S=Number of species

Rarefaction curves were plotted to determine if sampling was adequate in

each district using observed, Coleman’s, Jackknife, Bootstrap and Chao2 models in

PRIMER v. 7 (Clarke and Warwick, 2001). All gave comparable results;

consequently only that of real (observed) data are presented. Data were then

subjected to univariate and multivariate analyses of non-metric multidimensional

scaling procedure (Clarke and Gorley, 2015). Data were log transformed to achieve

criteria of normality (evenness and Simpson index of diversity). For invasion

52

impact analysis, diversity indices including total number of species (S), abundance

(N), species richness (R), species evenness (Jꞌ), Shannon index of diversity (H′) and

Simpson index of dominance (λ) were calculated for control as well as for invaded

plots. The above ecological indices were subjected to analysis of variance

(ANOVA) with invasion status and districts as factors using IBM SPSS v. 21.

Differences between ecological indices for five districts were individually tested

for significance between invaded and control plots by multiple comparison tests of

t-test. Data were further analyzed for species assemblages by non-metric

multidimensional scaling (nMDS) in two-three dimensions with invasion status

(control, invaded) as factor using PRIMER V.7 software. nMDS was used to

ordinate the similarity of data between site categories (invaded, control) based on

Bray-Curtis dissimilarity matrix following log-transformation of species

abundance data due to zero species count in some plots.

The range of clustering of sites and locations in response to invasion were

assessed by analysis of similarity (ANOSIM) and similarity percentage (SIMPER).

ANOSIM relates mean difference of ranks between and within groups, generating

Global statistic (R). The values of Global statistic (R) range from -1 to +1. Values

near 0 and negative values demonstrate similarity among groups. Values impending

+1 indicate a strong dissimilarity among groups (Clarke and Warwick, 2001;

Osunkoya et al., 2017). SIMPER identified species contributed most to average

dissimilarity between groups (invaded and control plots). This technique calculates

average impact of each species contributing to dissimilarity between groups

(Clarke and Warwick, 2001). Values of percentage similarity between groups range

between 0 to 100, with 100 stating maximum similarity.

53

0

20

40

60

80

100

120

140

160

180

200

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Tem

p. (

°C)

/ R

ain

fall

(mm

)

A. Max. Temp.

A. Min. Temp.

A. Rainfall

Chakwal

0

50

100

150

200

250

300

350


Tem

p. (

°C)

/ R

ain

fall

(mm

)

A. Max. Temp.

A. Min. Temp.

A. Rainfall

Islamabad

0

50

100

150

200

250

300

350

400


Tem

p. (

°C)

/ R

ain

fall

(mm

)

A. Max.Temp.

Rawalpindi

54

Fig. 6: Mean monthly climate data of Pothwar region, Pakistan

(Provided by Pakistan Meteorological Department University Road, Karachi).

0

50

100

150

200

250

300


Tem

p. (

°C)

/ R

ain

fall

(mm

)

A. Max. Temp.A. Min. Temp.A. Rainfall

Jhelum

0

50

100

150

200

250

300


Tem

p. (

°C)

/ R

ain

fall

(mm

)

A. Max. Temp.

A. Min. Temp.

A. Rainfall

Attock

55

Fig. 7: Distribution of plots for impact analysis of invaders in Pothwar region, Pakistan.

56

3.2. ALLELOPATHY SCREENING BIOASSAY

Phytotoxicity directed extraction and fractionation of selected invasive

species was carried through modified methodology of Shao et al. (2012); Othman

et al. (2012) and Islam and Kato-Noguchi (2014).

3.2.1. Collection and Drying of Plant Material

The fresh, healthy plant parts (leaves, roots, stem, fruits) for each

species were collected from invaded sites in Pothwar region, Pakistan.

3.2.2. Toxicity Assessment of Different Plant Parts

The methodology described by Shao et al. (2012) was adopted. Shade dried

plant materials at room temperature for each of selected species (P. hysterophorus,

B. papyrifera, X. strumarium and L. camara) were separated into roots, leaves,

fruits and stems. Five grams of each plant material was ground into powder and

soaked in 100 mL methanol (95% Merk Germany) for 24 h to afford 0.05 gmL-1

of

plant material. Radish and lettuce seeds were used for toxicity assessment because

of year-round commercial availability and their sensitivity and common use in

phytochemical bioassays. Three mL of plant extract for each plant part was added

to Petri plates lined with Whatman No. 1 filter paper. After complete solvent

evaporation (methanol), 5 mL distilled water was added to each Petri plate

followed by addition of 10 radish seeds. Petri plates were sealed with Parafilm to

prevent water loss, wrapped in aluminium foil to cause darkness hence etiolation

and incubated at 25°C. Control Petri plates were maintained without any plant

extract. Three replicates were made for each treatment. Radicle and hypocotyl

length (mm) of germinated seedlings were measured after 3 days as:

𝐺𝑟𝑜𝑤𝑡ℎ 𝑖𝑛ℎ𝑖𝑏𝑖𝑡𝑖𝑜𝑛 (%) = 100𝐿𝑐 − 𝐿𝑡

𝐿𝑐

57

Where Lt and Lc are shoot and root length of treatment and control respectively.

Overall seedling growth inhibition calculated as:

Overall seedling growth inhibition (%) = [(HI/2) + (EI/2)]

Where HI is Hypocotyl Inhibition; EI is Epicotyl Inhibition

3.2.3. Statistical Analysis

The data were analyzed by ANOVA using statistical package IBM SPSS.

Treatment means were compared through Tukey’s test at 5% level of probability.

Plant material showing maximum inhibitory effect was selected for fractionation.

3.3. HERBICIDAL ACTIVITY ANALYSIS

3.3.1. Fractionation of Methanol Plant Extracts

Through phytotoxicity bioassay, Lantana camara leaves and Xanthium

strumarium fruits were selected for further evaluation. Initially 1Kg of each of two

plant parts was ground into fine powder and exhaustively extracted through cold

maceration with 95% methanol (5L) at room temperature for seven days. Filtrate

was evaporated at 40ºC under vacuum using rotary evaporator to obtain crude dry

extract. Fractionation was done using modified Kupchan method of solvent-solvent

partitioning (Wagenen et al., 1993). Solvents based on difference in polarity and

density were used for fractionation of crude methanol extract. n-Hexane,

chloroform and ethyl acetate were sequentially added in separatory funnel in order

to get ethyl acetate, n-hexane, chloroform and aqueous fractions (Fig. 8). The

fractions were utilized to perform seedling growth inhibition bioassays.

3.3.2. Herbicidal Activity Analysis Through Germination Bioassay

For each organic fraction seedling growth inhibition bioassay was carried

58

out following the methodology of Othman et al. (2012) and Islam and Kato-

Noguchi (2014) to isolate most potent fraction for chemical studies. The

experiment was carried out using two monocot (Phalaris minor and Avena fatua)

and two dicot (Chenopodium album and Rumex dentatus) test weed species in

wheat (one of the most important cash crops in Pakistan). Assay was performed at

500, 1000 and 10,000 mgL-1

concentration of extracts for each organic fraction.

Filter papers were placed in Petri plates and 5 mL of each extract was added. The

solvent was allowed to evaporate overnight followed by addition of 5mL distilled

water in the residue. To each petri plate 10 seeds of test weeds surface sterilized

with 0.05% mercuric chloride were placed separately. The plates were sealed with

parafilm and incubated at room temperature. The radicle and hypocotyl lengths of

each seedling were measured after 5 days of incubation in dark at room

temperature. Control Petri plates were also maintained.

Overall seedling growth inhibition (%) = [(HI/2) + (EI/2)]

Where HI is Hypocotyl Inhibition; EI is Epicotyl Inhibition

3.3.3. Statistical Analysis

The data were analyzed by ANOVA on statistical package SPSS. Treatment

means were compared using Tukey’s test at 5% level of probability.

3.4. ALLELOCHEMICAL ANALYSIS

Phytochemical profiling of most active fraction for active component

analysis was carried out. The methodology consisted of getting step gradient

elusions via flash column chromatography that was subsequently assayed for

toxicity against radish seeds at 1mg/mL concentration. Fraction with highest

activity was analyzed through gas chromatography-mass spectrometry (GC-MS).

59

Fig. 7: Modified Kupchan Method of solvent-solvent partitioning (Wagenen et al., 1993)

60

Chapter 4

RESULTS


Plant diversity around the world is reducing rapidly due to various threats.

The invasion of alien plant species is second highest threat to plant diversity after

habitat loss. Invaders reduce species richness at rates depending on geographical

positions in invaded areas (Willis and Whittaker, 2002; Ortega and Pearson, 2005).

Phytosociological studies help to understand extent of invasion. Multiple analyses

of ecological parameters at different locations derive general explanations of

impact on species diversity and richness in plant communities (Zhao and Fang,

2006). In current study, invasion impacts of four top invaders viz. Broussonetia

papyrifera, Lantana camara, Parthenium hysterophorus and Xanthium strumarium

were assessed by multivariate statistical analysis in Pothwar region of Pakistan.

Parthenium hysterophorus: To assess sampling completeness, rarefaction curves

plotting cumulative number of species as a function of sampling effort were used

which indicated that sampling was reasonably complete (Fig. 9). A total of 56 plant

species from 50 genera were documented during the study (Table 8). A total of 56

species were recorded in control compared with 37 in infested plots. Mean species

diversity and richness/quadrat was higher in control plots (Fig. 10). Comparisons of

ecological indices showed significant difference across districts and invasion status.

Parthenium invasion exhibited variable impact across five districts by reducing

species number per plot (S) and abundance (N) up to a maximum of 40% in Attock.

Control plots harbored on average 6.033±1.75 (mean±SD, n=30) species. This was

statistically significant (t=2.09, df=29, p=0.045).

60

61

Fig. 9: Rarefaction curve showing cumulative number of species recorded as a

function of sampling effort

25

30

35

40

45

50

55

60

1 3 5 7 9 11

Sp

ecie

s co

un

t

Samples

S obs

62

Table 8: Plant species found in studied plots, family and life form

S# Plant Species Family Life

form

1 Achyranthes aspera L. Amaranthaceae Herb

2 Anagallis arvensis L. Primulaceae Herb

3 Argemone mexicana L. Papaveraceae Herb

4 Amaranthus viridis L. Amaranthaceae Herb

5 Astragalus scorplurus Bunge. Papilionaceae Herb

6 Bellis perennis L. Asteraceae Herb

7 Broussonetia papyrifera (L.) L’Herit. ex Vent. Moraceae Tree

8 Calotropis procera Br. Asclepiadaceae Shrub

9 Cannabis sativa L. Cannabaceae Herb

10 Cenchrus biflorus Roxb. Poaceae Grass

11 Chenopodium ambrosioides L. Chenopodiaceae Herb

12 Circium arvense L. Asteraceae Herb

13 Convolvulus arvensis L. Convolvulaceae Herb

14 Cynodon dactylon L. (Pers.) Poaceae Grass

15 Datura alba Nees Solanaceae Shrub

16 Datura innoxia Miller Solanaceae Shrub

17 Dicanthium annulatum Stapf. Poaceae Grass

18 Digitaria ciliaris (Retz.) Koeler Poaceae Grass

19 Erianthus munja L. Poaceae Grass

20 Fumaria indica (Hausskn.) Pugsley Fumariaceae Herb

21 Impatiens edgeworthii Hook. f. Balsaminaceae Herb

22 Lathyrus aphaca L. Papilionaceae Herb

23 Malvestrum coromandelianum (L.) Garcke Malvaceae Herb

24 Medicago polymorpha L. Papilionaceae Herb

25 Poa annua L. Poaceae Grass

26 Portulaca oleracea L. Aizoaceae Herb

27 Prosopis cineraria (Linn.) Druce Mimosaceae Tree

28 Prunella vulgaris L. Labiateae Herb

29 Ranunculus muricatus L. Ranunculaceae Herb

http://www.efloras.org/florataxon.aspx?flora_id=5&taxon_id=10650






63

S# Plant Species Family Life

form

30 Ricinus communis L. Euphorbiaceae Shrub

31 Rosa brunonii Lindl. Rosaceae Shrub

32 Rosa damascena Mill. Rosaceae Shrub

33 Rumex hastatus D. Don Polygonaceae Shrub

34 Rumex dentatus L. Polygonaceae Herb

35 Saxifragra androsacea L. Saxifragaceae Herb

36 Silybum marianum (L.) Gaertn. Asteraceae Herb

37 Solanum incanum L. Solanaceae Shrub

38 Solanum miniatum Beruh. ex Willd. Solanaceae Herb

39 Solanum surattense Burm. F. Solanaceae Shrub

40 Solanum nigrum L. Solanaceae Herb

41 Sonchus asper (L.) Hill Asteraceae Herb

42 Sorghum halepense L. Poaceae Grass

43 Suaeda fruticosa Forsk. Amaranthaceae Shrub

44 Swertia paniculata Wall. Gentianaceae Herb

45 Taraxacum officinale (L.) Weber ex F.H. Wigg Asteraceae Herb

46 Tamarix aphylla (L.) Karst. Tamaricaceae Tree

47 Tephrosia purpurea (L.) Pers. Papilionaceae Herb

48 Tinospora cordifolia Miers ex Hook. f Menispermaceae Herb

49 Tribulus terrestris L. Zygophyllaceae Herb

50 Urtica dioica L. Urticaceae Herb

51 Withania somnifera L. (Dunal) Solanaceae Shrub

52 Zizyphus mauritiana Lamk. Rhamnaceae Shrub

53 Capsella bursa-pestoris (L.) Medik. Brassicaceae Herb

54 Cyperus rotundus L. Cyperaceae Sedge

55 Polygonum plabegem R. Br. Polygonaceae Herb

56 Eclipta prostata L. Asteraceae Herb



https://www.google.com.pk/search?biw=1366&bih=662&q=Solanum+surattense&spell=1&sa=X&ved=0ahUKEwj2pITM4YrXAhXImZQKHQxQDAIQvwUIIygA


https://en.wikipedia.org/wiki/Carl_Linnaeus

https://en.wikipedia.org/wiki/Georg_Heinrich_Weber

https://en.wikipedia.org/wiki/F.H._Wigg





64

A total of 181 and 154 individuals were recorded in control and invaded

plots respectively. Similarly, abundance in control and invaded plots differed by

3.7±3.83 (mean±SD, n=30) and the difference was significant (t=4.34, df=29,

p<0.0001). Control plots also exhibited higher values of species richness by

difference of 0.15±0.51, species evenness by 0.019±0.02; Shannon index of

diversity by 0.2±0.34 and Simpson index of dominance by 0.22±0.35 (Table 7). For

individual district, native flora differed significantly in species density (S),

abundance per plot (N), species evenness (Jꞌ) and Simpson index of dominance (λ)

but not in overall species richness (R) and Shannon index of diversity (Hꞌ).

Parthenium invasion had significant impacts on all ecological indices except

species richness (R) at site 1 (Attock). For site 2 (Chakwal), only abundance was

affected significantly. For site 3 (Islamabad) invasion impacts were not significant

only on native species abundance. Species evenness (Jꞌ) was non-significant for site

4 (Jhelum) while for site 5 (Rawalpindi) only index significantly affected by

Parthenium invasion was species evenness (Jꞌ) (Table 9). The ordination (nMDS)

and ANOSIM showed significant magnitude of differences between species

composition of invaded and control plots in all sites with global R values of 0.876

(p=0.002), 0.519 (p=0.002), 0.598 (p=0.002), 0.907 (p=0.002) and 0.759 (p=0.002)

for Attock, Chakwal, Islamabad, Jhelum and Rawalpindi, respectively (Fig. 11).

The greatest dissimilarity between invaded and control plots was noticed by

Jhelum. Similarity percentage (SIMPER) analysis of data suggested those species

contributing most to average dissimilarity between control and invaded groups.

This analysis also computed average contribution of species causing dissimilarity.

Few top species separating invaded plots from non-invaded plots (control) for

analysis are enlisted in Table 11.

65

Table 9: Analysis of variance (ANOVA) of invasion impacts and district on diversity indices of local plant community

Ecological index SUMMARY ANOVA Mean (±SD)

District (D) Invasion

status (IS)

DˣIS

Interaction

Control (30) Invaded (30)

No. of species (S)/10m2 ** ** *** 6.033±1.75 5.133±1.83

Abundance (N)/10m2 ** *** ** 14.4±3.81 10.70±3.86

Species Richness (R) ** NS *** 1.87±0.49 1.62±0.53

Species evenness (Jꞌ) NS ** NS 0.028±0.039 0.009±0.006

Shannon index of diversity (Hꞌ) ** ** *** 1.73±0.29 1.53±0.406

Simpson index of dominance (λ) ** ** *** 1.72±0.29 1.50±0.42

*** P ≤ 0.001; ** P ≤ 0.02; * P ≤ 0.05; NS (not significant) P >0.05

66

Fig. 10: Mean values/10m2 for ecological indices of invaded vs. control plots in

different sites

0

2

4

6

8

10

12

14

16

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Attock

0

2

4

6

8

10

12

14

16

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Chakwal

0

2

4

6

8

10

12

14

16

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Rawalpindi

0

2

4

6

8

10

12

14

16

18

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Jhelum

0

2

4

6

8

10

12

14

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Islamabad

67

Table 10: Student’s t-test for significance of differences between control and

invaded plots at different sites

Site Number

of

species

(S)

Abundance

(N)

Species

Richness

(R)

Species

Evenness

(Jꞌ)

Shannon

index of

diversity

(Hꞌ)

Simpson

index of

dominance

(λ)

Attock * ** NS * ** *

Chakwal NS * NS NS NS NS

Rawalpindi ** NS ** ** ** **

Jhelum *** ** ** NS ** **

Islamabad NS NS NS *** NS NS


68

Fig. 11: Multidimensional scaling (MDS) ordination and analyses of similarity

(ANOSIM) results of invasion status data for Pothwar region, Pakistan (open

symbols are for control, uninvaded plots, and closed symbols are for invaded plots).

Attock

Non-metric MDSTransform: Log(X+1)

Resemblance: S17 Bray-Curtis similarity

Invasion stauscontrol

Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.11

Chakwal




Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.13

Jehlum




Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.09

Islamabad




Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.09

RawalpindiNon-metric MDS

Transform: Log(X+1)


Invasion statuscontrol

Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.06

BroussonetiaNon-metric MDS

Transform: Log(X+1)



Invaded

control

control

control

control

control

control

I nvaded

I nvaded

I nvaded

I nvaded

I nvaded

I nvaded

2D Stress: 0.09

d: Jhelum

ANOSIM (Global R): 0.519

P < 0.002

c: Chakwal


P < 0.002


P < 0.002


P < 0.002

b: Islamabad a: Attock

f: Pooled data for

Pothwar Region

e: Rawalpindi


P < 0.002


P<0.002

69

Table 11: SIMPER analysis of Parthenium invaded and control sites in Pothwar region, Pakistan. Data have been pooled prior to

analyses across districts

Average dissimilarity = 60.14%

Average abundance

Species Control Invaded Av. Diss. Diss./SD Contribution (%)

Poa annua L. 2.94 0.00 2.38 8.06 3.95

Lathyrus aphaca L. 0.00 2.69 2.18 5.82 3.63

Solanum miniatum L. 2.47 0.00 2.00 7.85 3.32

Ricinus communis L. 2.19 0.00 1.77 2.05 2.95

Convolvulus arvensis L. 1.80 1.79 1.49 1.32 2.48

Taraxacum officinale (L.) Weber ex F.H. Wigg 1.77 0.00 1.40 2.07 2.32

Rosa damascena Mill. 1.82 0.18 1.38 1.41 2.29

Tribulus terrestris L. 1.62 0.00 1.31 2.03 2.18

Fumaria indica (Hausskn.) Pugsley 2.35 1.15 1.31 1.55 2.18

Tephrosia purpurea (L.) Pers. 0.00 1.63 1.29 1.36 2.15

Portulaca oleracea L. 1.94 1.10 1.26 2.37 2.10

Circium arvense L. 1.63 0.00 1.25 1.69 2.08

Saxifragara androsacea L. 1.73 0.54 1.24 1.51 2.06


https://en.wikipedia.org/wiki/Georg_Heinrich_Weber

https://en.wikipedia.org/wiki/F.H._Wigg

https://www.google.com.pk/search?q=Rosa+damascena&sa=X&ved=0ahUKEwjOhve9nt3VAhWJgbwKHak-DWgQ7xYIIygA

70


Average abundance

Species Control Invaded Av. Diss. Diss./SD Contribution (%)

Anagallis arvensis L. 2.67 1.49 1.24 1.24 2.06

Tinospora cordifolia Miers ex Hook. f. 1.52 0.00 1.23 1.90 2.04

Solanum nigrum L. 1.87 0.86 1.21 1.92 2.02

Saxifragra androsacea L. 1.51 0.00 1.19 1.34 1.97

Tamarix aphylla (L.) Karst. 1.39 0.40 1.15 0.96 1.91

Solanum incanum L. 1.74 1.04 1.12 1.56 1.87

Eclipta prostata L. 1.95 1.02 1.12 1.25 1.86

*Values are average abundance ranking (1-rare; 2-common; 3-very common; >4-dominant)

71

Broussonetia papyrifera: To assess sampling completeness, rarefaction curves


which indicated that sampling was reasonably complete (Fig. 12). A total of 65

plant species from 60 genera were documented during the study (Table 12). A total

of 65 species were recorded in control plots compared with 52 in infested plots.

Mean species diversity and richness/quadrat was higher in control plots (Fig. 13).

Comparisons of ecological indices showed significant differences for all ecological

indices in invaded and control plots across sites while differences were significant

across invasion status except species evenness. Paper mulberry exhibited variable

impacts in five sites by reducing species number per plot (S) and abundance (N) by

a maximum of 48% in Islamabad. Control plots harbored on average 9.07±2.50

(mean±SD, n=30) species. This was by 3.54±2.08 more than invaded plots and the

difference was marginally significant (t=2.09, df=29, p=0.045). In total 298 and

156 individuals were recorded in control and invaded plots respectively. Similarly,

abundance in control and invaded plots differed by 2.97±3.96 (mean±SD, n=30)

and the difference was significant (t=3.34, df=29, p=0.00). Control plots exhibited

higher values of species richness by a difference of 0.89±0.53, species evenness by

0.0004±0.006, Shannon index of diversity by 0.5±0.29 and Simpson index of

dominance by 0.081±0.042. (Table 13). For individual sites, Paper mulberry

invasion had significant impacts on all ecological indices except species evenness

(Jꞌ) at site 1 (Attock). For site 2, (Chakwal) species abundance and Simpson index

of dominance was not affected significantly. For site 3 (Islamabad) invasion

impacts on species evenness were not significant. Species richness, evenness (Jꞌ)

and Simpson index of dominance was non-significant for site 4 (Jhelum) while for

site 5 (Rawalpindi) all species evenness was not affected significantly (Table 14).

72



35

40

45

50

55

60

65

70

1 3 5 7 9 11

Sp

ecie

s co

un

t

Samples

S obs

73


S.# Plant Species Family Life form

1 Abutilon indicum (L.) Sweet. Malvaceae Shrub

2 Acacia nilotica (L.) Delice Mimosaceae Tree

3 Adhatoda vasica Nees. Acanthaceae Shrub

4 Ajuga bracteosa Wall. Labiateae Herb

5 Alternanthera pungens Kunth Amaranthaceae Herb


7 Aristida cyanatha Neez ex Steud Poaceae Grass

8 Arundo donax L. Poaceae Grass

9 Asparagus spp. Asparagaceae Herb

10 Berberis lycium Royle Berberidaceae Shrub

11 Boerhavia diffusa L. Nyctaginaceae Herb

12 Boerhavia procumbens Banks ex Roxb. Nyctaginaceae Herb

13 Bergenia ciliata (Haw.) Sternb. Moraceae Tree

14 Calotropis procera (Aiton) W.T. Aiton Asclepiadaceae Shrub



17 Cenchrus ciliaris L. Poaceae Grass


19 Chrozophora tinctoria (L.) Euphorbiaceae Herb

20 Citrullus colocynthis (L.) Schrad Cucurbitaceae Herb

21 Corchorus depressus (L.) Stocks Tiliaceae Herb

22 Croton tiglium L. Euphorbiaceae Herb

23 Cymbopogon jwarancusa (Jones) Schult. Poaceae Grass

24 Cynodon dactylon (L.) Pers Poaceae Grass

25 Datura inoxia Mill Solanaceae Shrub

26 Desmostachya bipinnata (L.) Stapf Poaceae Herb

27 Digera muricata (L.) Mart. Amaranthaceae Herb

28 Dodonaea viscosa (L.) Jacq. Sapindaceae Shrub

29 Echinochloa crus-galli (L.) Beauv Poaceae Grass

30 Euphorbia helioscopia L. Euphorbiaceae Herb

https://en.wikipedia.org/wiki/Adrian_Hardy_Haworth

https://en.wikipedia.org/wiki/Kaspar_Maria_von_Sternberg



74


31 Euphorbia prostrata Ait. Euphorbiaceae Herb


33 Heteropogon contortus

(L.) P.Beauv. ex Roem. & Schult.

Poaceae Grass

34 Kochia indica Wight. Chenopodiaceae Herb

35 Lantana camara L. Verbenaceae Shrub

36 Lasiurus sindicus Henr. Poaceae Grass

37 Malva parviflora L. Malvaceae Herb

38 Malvestrum corromendilianum (L.) Garcke Malvaceae Herb

39 Medicago polymorpha L. Papillionaceae Herb

40 Melilotus indicus Linn. Papillionaceae Herb

41 Otostegia limbata (Benth.) Boiss. Labiateae Shrub

42 Oxalis corniculata L. Oxalidaceae Herb

43 Parthenium hysterophorus L. Asteraceae Herb

44 Phragmites karka (Retz.) Trin. Ex Steud. Poaceae Grass

45 Portulaca quadrifida L. Aizoaceae Herb

46 Potentilla supina L. Rosaceae Herb

47 Prosopis cineraria (Linn.) Druce Mimosaceae Shrub


49 Ricinus communis L. Euphorbiaceae Herb




53 Saccharum spontaneum L. Poaceae Grass

54 Silybum marianum (L.) Gaertner Asteraceae Shrub


56 Solanum surattense Burm. f. Solanaceae Herb

57 Stellaria media (L.) Vill. Caryophyllaceae Herb

58 Sueda fructicosa Forsk. Chenopodiaceae Shrub


60 Tamarix aphylla (Linn.) Karst. Tamaricaceae Tree


https://en.wikipedia.org/wiki/Palisot_de_Beauvois

https://en.wikipedia.org/wiki/Johann_Jacob_Roemer

https://en.wikipedia.org/wiki/Josef_August_Schultes



75


61 Tephrosia purpurea (Linn.) Pers. Papillionaceae Herb

62 Themeda anathera (Nees ex Steud.) Hack. Poaceae Grass





76

Table 13: Summary ANOVA of invasion impacts and site on diversity indices of local plant community


Site (S) Invasion

status (IS)

SˣIS

Interaction


No. of species (S)/10m2 * *** ** 6.81±2.50 5.53±1.65

Abundance (N)/10m2 * *** ** 22.1±3.81 19.13±4.12

Species Richness (R) NS *** * 2.58±0.59 1.69±0.47

Species evenness (Jꞌ) NS NS NS 0.0077±0.005 0.0073±0.007

Shannon index of diversity (Hꞌ) * *** ** 2.15±0.27 1.65±0.32

Simpson index of dominance (λ) * *** ** 0.203±0.075 0.122±0.033

*** P ≤ 0.001; ** P ≤ 0.02; * P ≤ 0.05; NS (not significant) P>0.05

77

Fig. 13: Mean values/10m2 for ecological indices of invaded vs control plots in

different sites

0

3

6

9

12

15

18

21

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Attock

0

3

6

9

12

15

18

21

24

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Chakwal

0

3

6

9

12

15

18

21

24

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Islamabad

0

3

6

9

12

15

18

21

24

N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Jhelum

0

3

6

9

12

15

18

21

24

27

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Rawalpindi

78



Site Number

of

species

(S)

Abundance

(N)

Species

Richness

(R)

Species

Evenness

(Jꞌ)

Shannon

index

(Hꞌ)

Simpson

index (λ)

Attock ** ** ** NS *** ***

Chakwal ** N S ** ** * N S

Islamabad ** ** ** N S ** **

Jhelum * * N S N S * N S

Rawalpindi ** ** ** N S ** **


79


(ANOSIM) results of invasion status data for Pothwar region, Pakistan; closed

symbols are representative of invaded sites while open for control ones

JehlumNon-metric MDS

Transform: Log(X+1)



Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.09

ChakwalNon-metric MDS

Transform: Log(X+1)



Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.13

AttockNon-metric MDS

Transform: Log(X+1)



Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.12

IslamabadNon-metric MDS

Transform: Log(X+1)



Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.09


Transform: Log(X+1)



Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.06


Transform: Log(X+1)



Invaded

control

control

control

control

control

control

I nvaded

I nvaded

I nvaded

I nvaded

I nvaded

I nvaded

2D Stress: 0.09


P<0.004

c: Islamabad

b: Chakwal

d: Jhelum

e: Rawalpindi f: Pooled data for Pothwar

region


P<0.002 ANOSIM (Global R): 0.730

P<0.002


P<0.002

a: Attock


P<0.002


p<0.002

80

Table 15: SIMPER analysis of Broussonetia invaded and control sites in Pothwar region, Pakistan


Average abundance

Species Invaded Control Av. Diss. Diss/SD Contribution

(%)


Malvastrum coromandelianum (L.) Garcke 2.57 0.00 1.46 4.14 2.55

Cynodon dactylon (L.) Pers. 2.44 0.00 1.36 1.91 2.38

Silybum marianum (L.) Gaertn. 0.81 2.69 1.12 1.54 1.97

Calotropis procera (Aiton) W.T.Aiton 2.02 0.00 1.12 1.89 1.96

Datura innoxia Mill. 1.98 0.00 1.04 1.33 1.82

Digeria muricata L. (Mart.) 1.94 0.00 1.02 1.36 1.79

Kochia indica Wight. 1.40 3.12 1.01 1.22 1.77

Desmostachya bipinnata (L.) Stapf 2.96 1.25 1.00 1.29 1.74

Swertia paniculata Wall. 2.30 0.85 0.97 1.53 1.70

Euphorbia helioscopia 1.77 0.00 0.96 2.14 1.68

Solanum surratensis Burm. F. 2.28 0.88 0.95 1.29 1.66

Melilotus indica L. 2.09 0.62 0.93 1.53 1.63

https://en.wikipedia.org/wiki/William_Aiton

https://en.wikipedia.org/wiki/William_Townsend_Aiton


https://en.wikipedia.org/wiki/Otto_Stapf

81


Average abundance

Species Invaded Control Av. Diss. Diss/SD Contribution

(%)

Alternanthera pungens 1.62 0.00 0.93 1.27 1.62

Corchorus depressus (L.) Stocks 1.74 0.00 0.92 1.36 1.62

Stellaria media 2.32 0.76 0.92 1.57 1.62

Fumaria indica 1.73 0.00 0.92 1.33 1.61

Ranunculus muricatus 1.77 0.46 0.92 1.43 1.61

Cenchrus bifloris 1.60 1.98 0.86 1.17 1.50

Anagallis arvensis 1.45 2.44 0.86 1.17 1.50


82

The ordination (nMDS) and ANOSIM showed significant magnitude of differences

between species composition of invaded and control plots in all sites with global R

values of 0.740 (p=0.002), 0.302 (p=0.002), 0.813 (p=0.002), 0.730 (p=0.002) and

0.691 (p=0.002) for Attock, Chakwal, Islamabad, Jhelum and Rawalpindi,

respectively (Fig. 14). The greatest dissimilarity between invaded and control plots

was noticed by Islamabad.

Lantana camara: To assess sampling completeness, rarefaction curves plotting

cumulative number of species as a function of sampling effort were used which

indicated that sampling was reasonably complete (Fig. 15). A total of 66 plant

species from 59 genera were documented during the study (Table 16). A total of 56

species were recorded in control plots compared with 37 in infested plots. Mean

species diversity and richness/quadrat was higher in control plots (Fig. 16).

Comparisons of ecological indices showed significant differences across sites and

invasion status (Table 1). Lantana invasion exhibited variable impact in five sites

by reducing species number per plot (S) and abundance (N) by a maximum of 46%

in Chakwal. Control plots harbored on average 13.90±3.50 (mean±SD, n=30)

species. This was by 1.734±0.14 more than invaded plots and the difference was

significant (t=2.27, df=29, p=0.00). In total, 212 and 139 individuals were recorded

in control and invaded plots respectively. Similarly, abundance in control and

invaded plots differed by 2.3±1.80 (mean±SD, n=30) and the difference was

significant (t=4.08, df=29, p=0.00). Control plots also exhibited higher values of

species richness by a difference of 0.15±0.41, species evenness by 0.019±0.12;

Shannon index of diversity by 0.20±0.40 and Simpson index of dominance by

0.22±1.27 (Table 17). For individual district, native flora differed significantly in

species density, abundance/plot, species evenness and Simpson index of dominance

83



25

35

45

55

65

1 3 5 7 9 11

Spe

cie

s co

un

t

Samples

S obs

84


S.# Plant species Family Life form


2 Achillea millefolium L. Asteraceae Shrub


4 Adhatoda vasica Nees Acanthaceae Herb

5 Adiantum venustum D. Don Pteridaceae Fern

6 Ageratum houstonianum Mill. Asteraceae Herb


8 Anaphalis nepalensis (Spreng.) Hand.-Mazz Asteraceaa Herb

9 Arisaema flavum (Forsk.) Schott. Araceae Tree

10 Arisaema jacquemontii Blume Araceae Tree

11 Aristida cyanatha Neez ex Steud Poaceae Grass

12 Astragalus scorplurus Bunge. Papillionaceae Herb

13 Barleria cristata L. Acanthaceae herb

14 Berberis lyceum Royle Berberidaceae Shrub

15 Bergenia ciliate (Haw.) Sternb. Saxifragaceae Herb

16 Boerhavia diffusa L. Nyctaginaceae Herb

17 Calotropis gigantea R. Br. Asclepiadaceae Shrub

18 Calotropis procera (Wild.) R. Br. Asclepiadaceae Shrub


20 Capparis decidua (Forssk.) Edgew. Capparidaceae Shrub


22 Cassia fistula L. Caesalpinaceae Tree


24 Cenchrus setigerus Vahl. Poaceae Grass

25 Chenopodium album L. Chenopodiaceae Herb

26 Chrozophora tinctoria L. Euphorbiaceae Herb

27 Clematis grata Wall. Ranunculaceae Herb

28 Convolvulus arvensis L. Convolvulaceaee Herb

29 Conyza Canadensis (L.) Cronquist Asteraceae Herb

30 Corchorus depressus (L.) Stocks Tiliaceae Herb

85


31 Croton bonplandianus Bat. Euphorbiaceae Herb

32 Croton tiglium L. Euphorbiaceae Herb

33 Cynodon dactylon Pers. Poaceae Grass



36 Datura stramonium L. Solanaceae Shrub

37 Dendrocalamus strictus (Roxb.) Nees Poaceae Grass


39 Dicanthium foveolatum (Del.) Roberty Poaceae Grass

40 Digera muricata (L.) Mart Amaranthaceae Herb

41 Digitaria ciliaris (Retz.) Koel. Poaceae Grass

42 Eclipta prostrata L. Asteraceae Herb

43 Embelia robusta Roxb. Myrsinaceae Shrub


45 Euphorbia helioscopia L. Euphorbiaceae Herb

46 Euphorbia prostrata Ait. Euphorbiaceae Herb

47 Euphorbia royleana Boiss Euphorbiaceae Herb

48 Geranium nepalense Sweet Geraniaceae Herb

49 Heliotropium strigosum Willd. Boraginaceae Herb

50 Indigofera heterantha Wall. ex Brandis Papilionaceae Herb

51 Justicia adhatoda L. Acanthaceae Shrub

52 Lactuca serriola L. Asteraceae Herb

53 Lespedeza juncea (L.f.) Pers. Papilionaceae Shrub


55 Medicago polymorpha Willd. Papilionaceae Herb

56 Medicago sativa L. Papilionaceae Herb

57 Melilotus indica (L.) All. Papilionaceae Herb

58 Melilotus sativa L. Papilionaceae Herb

59 Morus spp. Moraceae Tree

60 Myrsine africana L. Myrsinaceae Shrub


86




64 Phragmites karka (Retz.) Trin. Ex Steud. Poaceae Grass

65 Polygonum plabegem Polygonaceae Herb


87



Site (S) Invasion

status (IS)

SˣIS Interaction Control (30) Invaded

(30)

No. of species (S)/10m2 *** *** *** 13.90±3.50 12.166±2.78

Abundance (N)/10m2 *** *** *** 17.6667±1.75 16.66±2.50

Species Richness (R) *** *** *** 3.74±0.72 2.7±0.91

Species evenness (Jꞌ) *** *** ** 0.98±0.005 0. 92±0.25

Shannon index of diversity (Hꞌ) *** *** *** 2.56±0.27 1.56±0.65

Simpson index of dominance (λ) *** *** *** 0.27±0.23 0.17±0.08

*** P ≤ 0.001; ** P ≤ 0.02; * P ≤ 0.05; NS (not significant) P>0.05

88


different sites

0

5

10

15

20

25

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Attock

0

2

4

6

8

10

12

14

16

18

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Chakwal

0

5

10

15

20

25

30

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Islamabad

0

5

10

15

20

25

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Jhelum

0

5

10

15

20

25

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Rawalpindi

89



Site Number

of species

(S)

Abundance

(N)

Species

Richness

(R)

Species

Evenness

(Jꞌ)

Shannon

index

(Hꞌ)

Simpson

index (λ)

Attock *** *** *** NS ** ***

Chakwal NS NS ** NS NS NS

Islamabad ** ** ** NS ** **

Jhelum *** *** *** *** *** ***

Rawalpindi *** *** *** ** *** ***


90



symbols are representative of invaded sites while open for control ones.

Lantana attockNon-metric MDS

Transform: Log(X+1)


Invasion statusControl

Invaded

Control

Control

Control

Control

Control

ControlI nvaded

I nvaded

I nvaded

I nvaded

I nvaded

I nvaded

2D Stress: 0.01

ChakwalNon-metric MDS

Transform: Log(X+1)



Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.07

ISlamabadNon-metric MDS

Transform: Log(X+1)



InvadedCont rol_1

Cont rol_2

Cont rol_3Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.12


Transform: Log(X+1)



Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4


I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.01




Invaded

Cont rol_1

Cont rol_2

Cont rol_3 Cont rol_4Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.07

LantanaNon-metric MDS

Transform: Log(X+1)



Invaded


Cont rol_3

Cont rol_4


I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.01

a: Attock b: Chakwal

c: Islamabad d: Jhelum

e: Rawalpindi

ANOSIM (Global R): 1

P<0.002 ANOSIM (Global R): 0.974

P<0.002


P<0.002


P<0.002


P<0.002

f: Pooled data for Pothwar region

ANOSIM (Global R): 1

p<0.002

91

Lantana invasion had significant impacts on all ecological indices except species

evenness (Jꞌ) at site 1 (Attock). For site 2 (Chakwal), only species richness was

affected significantly. For site 3 (Islamabad) invasion impacts were not significant

on native species evenness while all ecological indices were significantly affected

for site 4 (Jhelum) and site 5 (Rawalpindi) (Table 18). The ordination (nMDS) and

ANOSIM showed significant magnitude of differences between species

composition of invaded and control plots in all sites with global R values of 1.00

(p=0.002), 0.974 (p=0.002), 0.728 (p=0.002), 0.983 (p=0.002) and 0.930 (p=0.002)

for Attock, Chakwal, Islamabad, Jhelum and Rawalpindi, respectively (Fig. 17).

The greatest dissimilarity between invaded and control plots was noticed by

Attock. Similarity percentage (SIMPER) analysis of data suggested species




analysis are enlisted in Table 19. Stellaria media, Oxalis corniculata, Cynodon

dactylon, Digitaria ciliaris, Malva parviflora, Croton tiglium, Eclipta prostrata,

Clematis grata, Chenopodium album, Calotropis procera, Medicago sativa,

Achyranthus aspra, Solanum nigrum, Datura stramonium and Sonchus asper were

top contributing species causing difference between control and invaded plots in

Pothwar region.

Xanthium strumarium: To assess sampling completeness, rarefaction curves


which indicated that sampling was reasonably complete (Fig. 18). A total of 64

plant species from 59 genera were documented during the study (Table 20).

92

Table 19: SIMPER analysis of Lantana invaded and control sites in Pothwar region, Pakistan


Average abundance

Species Control Invaded Av. Diss. Diss/SD Contribution (%)

Stellaria media (L.) Vill. 3.04 1.71 1.38 7.99 2.10

Oxalis corniculata L. 2.98 0.00 1.35 9.94 2.06


Digitaria ciliaris (Retz.) Koeler 2.74 0.00 1.24 6.40 1.89

Malva parviflora L. 2.70 0.00 1.22 7.69 1.86

Croton tiglium L. 2.65 1.77 1.20 9.38 1.83

Eclipta prostrata (L.) L. 2.65 0.18 1.19 12.44 1.82

Clematis grata (Wall.) Kuntze 2.54 1.62 1.15 6.88 1.76

Chenopodium album L. 2.46 2.35 1.12 4.51 1.71

Calotropis procera (Aiton) W.T.Aiton 2.43 0.01 1.11 5.98 1.69

Medicago sativa L. 2.41 1.23 1.09 7.67 1.67

Achyranthus aspra L. 2.40 2.14 1.08 5.91 1.65

Solanum nigrum L. 2.38 2.31 1.06 9.35 1.64

https://en.wikipedia.org/wiki/William_Aiton

https://en.wikipedia.org/wiki/William_Townsend_Aiton

93


Average abundance

Species Control Invaded Av. Diss. Diss/SD Contribution (%)

Datura stramonium L. 2.37 2.18 1.07 9.14 1.63

Sonchus asper (L.) Hill. 2.25 1.49 1.02 8.80 1.55

Digera muricata (L.) Mart 2.15 2.21 0.98 6.44 1.49

Bergenia ciliate (Haw.) Sternb. 2.15 2.01 0.97 6.11 1.48


Cannabis sativa L. 2.12 2.38 0.96 6.36 1.46



94

A total of 64 species were recorded in control plots compared with 52 in

infested plots. Mean species diversity and richness/quadrat was higher in control

plots (Fig. 19). Comparisons of ecological indices showed significant difference

across sites and invasion status (Table 21). Xanthium invasion exhibited variable

impact in five sites by reducing species number per plot (S) and abundance (N) by

a maximum of 46% in Chakwal. Control plots harbored on average 10.86±2.79

(mean±SD, n=30) species. This was by 2.86±2.39 more than invaded plots and the

difference was significant (t=-4.27, df=29, p=0.00). In total 226 and 140

individuals were recorded in control and invaded plots respectively. Similarly,

abundance in control and invaded plots differed by 2.3±1.83 (mean±SD, n=30) and

the difference was significant (t=6.08, df=29, p=0.00). Control plots also exhibited

higher values of species richness by a difference of 0.80±0.71, species evenness by

0.42±0.22, Shannon index of diversity by 1.11±0.60 and Simpson index of

dominance by 3.45±1.57 (Table 14). For individual sites, Xanthium invasion had

significant impacts on ecological indices except species richness and evenness at

site 2 (Chakwal). For site 3, (Islamabad) none of ecological index was affected

significantly. For site 4 (Jhelum) invasion impacts on abundance were not

significant. Species evenness (Jꞌ) was non-significant for site 5 (Rawalpindi). For

site 1 (Attock) all ecological indices were significantly affected. The ordination

(nMDS) and ANOSIM showed significant magnitude of differences between

diversity indices of invaded and control plots in all sites with global R values of

0.537 (p=0.002), 0.909 (p=0.002), 0.307 (p=0.0028), 0.417 (p=0.002) and 1.00

(p=0.002) for Attock, Chakwal, Islamabad, Jhelum and Rawalpindi respectively

(Fig. 20). The greatest dissimilarity between invaded and control plots was noticed

by Rawalpindi.

95



35

40

45

50

55

60

65

70

1 3 5 7 9 11

Sp

ecie

s co

un

t

Samples

S obs

96


S. # Plant Species Family Life form


2 Ajuga bracteosa Wall. Labiateae Herb

3 Albizia lebbeck (L.) Benth. Mimosaceae Tree



6 Artemisia scoparia Waldst. & Kit. Asteraceae Herb

7 Asparagus adscendens Asparagaceae Shrub

8 Astragalus scorplurus Bunge. Papillionaceae Herb

9 Barleria cristata L. Acanthaceae Shrub

10 Boerhavia procumbens Banks ex Roxb. Nyctaginaceae Herb

11 Calotropis procera (Aiton) W.T. Aiton Asclepiadaceae Shrub


13 Capparis decidua (Forssk.) Edgew. Capparidaceae Shrub

14 Cassia fistula L. Caesalpiniaceae Tree

15 Chenopodium album L. Chenopodiaceae Herb

16 Clematis grata Wall. Ranunculaceae Herb


18 Cotinus coggyria Scop. Anacardiaceae Shrub

19 Cynodon dactylon (L.) Pers. Poaceae Grass

20 Dendrocalamus strictus (Roxb.) Nees Poaceae Grass


22 Dicanthium foveolatum (Del.) Roberty Poaceae Grass

23 Digitaria ciliaris (Retz.) Koel Poaceae Grass

24 Dodonaea viscose (L.) Jacq. Sapindaceae Shrub

25 Echinochloa crus-galli (L.) P. Beauv. Poaceae Grass

26 Eclipta alba (L.) Hassk. Asteraceae Herb

27 Eragrostis cilianensis

(All.) Vign. ex Janchen

Poaceae Grass

28 Euphorbia clarkeana Hook. f. Euphorbiceae Herb

29 Euphorbia helioscopia L. Euphorbiceae Herb





https://en.wikipedia.org/wiki/Carlo_Allioni

https://en.wikipedia.org/w/index.php?title=Vign.&action=edit&redlink=1

https://en.wikipedia.org/wiki/Erwin_Emil_Alfred_Janchen

97


30 Euphorbia milii Des Moul. Euphorbiceae Herb


32 Geranium nepalense Sweet Geraniaceae Herb

33 Heliotropium strigosum Willd. Boraginaceae Herb

34 Lactuca serriola L. Asteraceae Herb

35 Lantana camara L. Verbenaceae Herb

36 Lespedeza juncea (Linn.f.) Pers. Papillionaceae Herb


38 Malvastrum coromandelianum (L.) Garcke Malvaceae Herb

39 Opuntia monacantha (Willd.) Haw. Cactaceae Shrub




43 Peganum harmala L. Zygophyllaceae Herb

44 Rhamnus pentapomica Edgew. Rhamnaceae Shrub



47 Saccharum spontaneum L. Poaceae Grass

48 Setaria pumila (Poir.) Roemer & Schultes Poaceae Grass

49 Silybum marianum (L.) Gaertn. Asteraceae Shrub



52 Solanum surattense Burm. F. Solanaceae Herb

53 Sorghum halepense L. Pers. Poaceae Grass

54 Stellaria media (L.) Vill. Caryophyllaceae Herb

55 Suaeda fruticosa Forsk. Chenopodicaeae Shrub


57 Tephrosia purpurea (L.) Pers. Papillionaceae Herb

58 Themada anathera (Nees ex Steud.) Hack. Poaceae Grass

59 Trianthema portulacastrum Aizoaceae Herb




98


61 Trichosanthes cucumerina L. Cucurbitaceae Herb

62 Typha domingensis Typhaceae Herb

63 Withania somnifera (L.) Dunal Solanaceae Shrub

64 Ziziphus mauritiana Lam. Rhamnaceae Shrub

99



Site (S) Invasion

status (IS)

S ˣ IS

Interaction


No. of species (S)/10m2 *** *** *** 8.00±2.79 6.70±1.98

Abundance (N)/10m2 *** *** *** 14.4±3.81 10.70±3.86

Species Richness (R) *** NS *** 2.31±0.66 2.12±0.56

Species evenness (Jꞌ) NS * ** 0.028±0.039 0.009±0.006

Shannon index (Hꞌ) of diversity *** *** *** 2.00±0.31 1.82±0.31

Simpson index (λ) of dominance *** ** *** 0.17±0.05 0.14±0.04


100


different sites

0

2

4

6

8

10

12

14

16

18

20

22

S N D H' J' λ

Me

an v

alu

es/

10

m2

plo

t

Ecological index

Control

Invaded

Attock

0

5

10

15

20

25

30

S N D H' J' λ

Me

an v

alu

es/

10

m2

plo

t

Ecological index

Control

Invaded

Chakwal

0

2

4

6

8

10

12

14

16

18

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Islamabad

0

3

6

9

12

15

18

21

S N D H' J' λ

Me

an v

alu

es/

10

m2

plo

t

Ecological index

Control

Invaded

Jhelum

0

3

6

9

12

15

18

S N D H' J' λ

Me

an v

alu

e/1

0m

2 p

lot

Ecological index

Control

Invaded

Rawalpindi

101



Site Number

of

species

(S)

Abundance

(N)

Species

Richness

(R)

Species

Evenness

(Jꞌ)

Shannon

index of

diversity

(Hꞌ)

Simpson

index of

dominance

(λ)

Attock * ** ** N S * *

Chakwal ** ** N S N S ** **

Islamabad N S N S N S N S N S N S

Jhelum ** N S *** N S ** **

Rawalpindi ** ** ** * ** **


102



symbols are representative of invaded sites while open for control ones.

AttockNon-metric MDS

Transform: Log(X+1)


Invasion status

control

Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.12

Chakwal



Invasion status

control

Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.04

IslamabadNon-metric MDS

Transform: Log(X+1)


Invasion status

control

Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.14


Transform: Log(X+1)


Invasion status

control

InvadedCont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.03


Transform: Log(X+1)


Invasion status

control

Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4


I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0

XanthiumNon-metric MDS

Transform: Log(X+1)


Invasion status

control

Invaded

control

control

control

control

control

control

I nvaded

I nvaded

I nvaded

I nvaded

I nvaded

I nvaded

2D Stress: 0.09

ANOSIM (Global R): 0.537 P < 0.002


P < 0.002


P < 0.0028



f: Pooled data for Pothwar

region

a: Attock b: Chakwal

c: Islamabad

d: Jhelum

e: Rawalpindi


p<0.002

103

Table 23: SIMPER analysis of Xanthium invaded and control sites in Pothwar region, Pakistan


Average abundance

Species Control Invaded Av. Diss. Diss/SD Contribution

(%)

Solanum nigrum L. 2.93 1.03 1.73 5.58 3.21


Parthenium hysterophorus L. 2.69 1.61 1.58 9.91 2.93

Dodonaea viscosa Jacq. 2.59 1.35 1.51 4.16 2.81


Ajuga bracteosa Wall. 2.41 1.34 1.42 5.80 2.63

Rumex dentatus L. 2.16 1.48 1.25 2.17 2.32

Typha domingensis Pers. 2.70 1.44 1.13 2.57 2.10

Withania somnifera (L.) Dunal 1.95 0.90 1.12 3.28 2.09

Lantana camara L. 1.89 0.52 1.11 1.95 2.06


Solanum surratensis Burm. F. 1.86 0.00 1.07 1.23 1.98

Cotinus coggygria Scop. 1.89 0.84 0.96 1.19 1.78

104


Average abundance

Species Control Invaded Av. Diss. Diss/SD Contribution

(%)

Boerhavia procumbens Banks ex Roxb. 1.61 0.00 0.95 3.03 1.77

Dichanthium foveolatum (Del.) Roberty 1.62 1.55 0.95 1.42 1.77


Sorghum halepense (L.) Pers. 1.48 1.06 0.91 1.35 1.69



Oxalis corniculata L. 1.43 0.00 0.87 0.97 1.61


105

Similarity percentage (SIMPER) analysis of data suggested those species




analysis are enlisted in Table 23. Solanum nigrum, Cynodon dactylon, Parthenium

hysterophorus, Dodonaea viscosa, Tamarix aphylla, Ajuga bracteosa, Rumex

dentatus, Typha domengensis, Withania somnifera and Lantana camara were top

contributing species causing difference between control and invaded plots in

Pothwar region.

4.2. ALLELOAPTHY BIOASSAYS AND HERBICIDAL ACTIVITY

For toxicity assessment of selected invaders overall seedling growth

inhibition was examined for aqueous extracts of different plant parts (leaves, roots,

stem and fruits) affording 0.05 gmL-1

of plant material. Overall seedling growth

inhibition results are demonstrated in Table 16. The strength of phytotoxicity

varied with plant parts being maximum for Lantana camara leaves (96.59±1.00)

followed by Xanthium strumarium fruits (92.13±2.89). Based on these findings,

these two plant parts were selected for herbicidal activity against selected test

species (Table 24). Crude methanol extracts of Lantana leaves and Xanthium fruits

were prepared by cold maceration technique and were subjected to fractionation.

Fractionation resulted in three organic (ethyl acetate, chloroform and n-hexane) and

one aqueous fraction for each crude extract. Bioassays were performed at

1,000ppm concentration against selected weed test species (monocot: Avena fatua

and Phalaris minor; Dicot: Rumex dentatus and Chenopodium album) of wheat

crop (Figure 21).

106

Plant Plant Part Overall seedling growth inhibition (%)

Lantana camara L. *Leaves 96.59±1.00

a

Stem 52.45±1.28b

Fruits 30.50±0.95c

Roots 28.22±0.89c

Parthenium hysterophorus L. Leaves 73.79±1.31a

Stem 55.06±2.46b

Fruits 34.34±1.16c

Roots 20.82±1.93d

Xanthium strumarium L. Leaves 74.18±2.30b

Stem 46.21±1.68c

Table 24: Seedling growth inhibition of Radish seeds by different plant parts of Lantana camara L.,

Parthenium hysterophorus L., Xanthium strumarium L. and Broussonetia papyrifera (L.) L’Herit. ex Vent at

0.05gmL−1

aqueous extract

107

Plant Plant Part Overall seedling growth inhibition (%)

*Fruits 92.13±2.89

a

Roots 20.59±3.2d

Broussonetia papyrifera (L.) L’Herit. ex Vent Leaves 60.67±2.13b

Stem 79.09±1.92a

Fruits 42.13±2.89a

Roots 54.93±1.94c

Overall seedling growth inhibition (%) calculated as [(HI/2) + (EI/2)], Where HI is Hypocotyl Inhibition; EI is Epicotyl

Inhibition

108

Table 25: Seedling growth inhibition (%) of weed test species by Lantana camara leaves and Xanthium strumarium

fruits solvent fractions at 500 ppm

Solvent

fraction

P. minor A. fatua R. dentatus C. album T. aestivum

(Galaxy13)

X. strumarium fruits Chloroform 27.24±1.21 23.72±1.98 17.84±2.35 35.17±3.32 9.18±1.62

Ethyl acetate 12.15±1.65 15.62±2.32 25.13±1.66 16.38±4.31 3.86±2.43

n-Hexane 11.35±3.21 14.86±5.21 18.36±1.85 6.43±2.37 3.88±2.67

Aq. MeOH 18.54±3.73 22.34±3.65 21.64±2.57 14.37±5.41 8.44±2.10

L. camara leaves Chloroform 24.65±3.68 21.58±3.95 35.46±4.29 23.46±4.71 6.76±3.21

Ethyl acetate 21.62±5.72 17.83±1.16 31.28±2.81 11.76±1.63 7.90±2.51

n-Hexane 9.64±1.85 13.75±3.61 4.59±2.15 5.71±1.52 5.78±1.52

Aq. MeOH 16.59±2.13 6.98±4.52 11.65±1.86 7.81±2.31 17.47±3.57


Inhibition

109

Table 26: Seedling growth inhibition (%) of weed test species by Lantana camara leaves and Xanthium strumarium

fruits solvent fractions at 1,000 ppm

Solvent

fraction


(Galaxy13)


Ethyl acetate 20.51±1.79 47.48±1.77 38.33±0.96 43.24±5.37 9.98±1.67

n-Hexane 33.65±8.13 36.77±7.68 24.55±2.79 20.51±1.79 11.64±0.85

Aq. MeOH 43.24±5.37 41.93±3.04 31.88±3.04 33.65±8.13 13.14±3.51

L. camara leaves Chloroform 72.85±2.69 49.44±3.25 64.86±7.89 59.26±5.40 10.01±1.05

Ethyl acetate 64.86±7.89 38.33±0.96 45.19±1.46 39.21±0.87 24.01±0.85

n-Hexane 37.46±1.49 24.55±2.79 12.78±1.45 20.62±1.74 12.97±0.95

Aq. MeOH 45.19±1.49 31.88±3.04 37.46±1.49 24.54±1.72 21.78±1.00


Inhibition

110

Table 27: Seedling growth inhibition (%) of weed test species by Lantana camara leaves and Xanthium strumariu fruits

solvent fractions at 10,000 ppm

Solvent

fraction


(Galaxy13)


Ethyl acetate 86.84±0.58 67.3±1.42 72.56±1.65 75.04±1.67 13.94±0.95

n-Hexane 87.38±0.68 59.41±1.34 61.08±1.09 65.37±1.54 44.37±0.64

Aq. MeOH 57.02±0.63 42.78±0.47 59.32±1.47 54.79±0.71 21.98±1.78

L. camara leaves

Chloroform 41.98±1.67 51.76±1.08 51.78±1.15 53.52±1.03 29.64±0.85

Ethyl acetate 78.98±1.06 68.47±0.65 78.67±0.85 74.90±1.04 21.94±0.96

n-Hexane 76.34±0.87 64.21±0.65 58.67±0.85 65.90±1.74 29.11±2.73

Aq. MeOH 51.45±0.95 42.52±1.62 52.89±1.90 41.51±2.84 36.76±3.65


Inhibition

111

Fig. 21: Growth of Triticum aestivum, Avena fatua, Phalaris minor, chenopodium

album and Rumex dentatus under 1000ppm chloroform extract of L. camara leaves

extract

112

y = 18.465ln(x) - 85.272

0

20

40

60

80

100

0 5000 10000

See

dli

ng

gro

wth

inh

ibit

ion

Conc. (mgL-1)

Chloroform (P. minor)

y = 25.905ln(x) - 153.01

0

20

40

60

80

100

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Ethyl acetate

y = 24.866ln(x) - 140.98

0

20

40

60

80

100

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Hexane

y = 11.122ln(x) - 43.195

0

20

40

60

80

0 5000 10000Se

ed

ling

gro

wth

in

hib

itio

n

Conc. (mgL-1)

Aq. MeOH

y = 13.606ln(x) - 64.271

0

20

40

60

80

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Hexane

y = 3.3103ln(x) + 14.297

0

20

40

60

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Chloroform (A. fatua)

y = 15.081ln(x) - 68.797

0

20

40

60

80

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

E. acetate

y = 5.2023ln(x) - 3.0436

0

10

20

30

40

50

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Aq. MeOH

y = -2.951ln(x) + 50.801 0

20

40

60

0 5000 10000See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Chloroform (R. dentatus)

y = 15.59ln(x) - 70.714

0

20

40

60

80

0 5000 10000See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

E. acetate

113

y = 14.663ln(x) - 74.493

0

20

40

60

80

0 5000 10000

See

dlin

g gr

ow

th in

hib

itio

n

Conc. (mgL-1)

Hexane

y = 12.412ln(x) - 54.784

0

20

40

60

80

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Aq. MeOH

y = 4.2861ln(x) + 15.73

0

10

20

30

40

50

60

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Chloroform (C. album)

y = 18.132ln(x) - 90.092

0

20

40

60

80

100

0 5000 10000Se

ed

ling

gro

wth

in

hib

itio

n

Conc. (mgL-1)

E. acetate

y = 19.626ln(x) - 115.33

0

20

40

60

80

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Hexane

y = 12.41ln(x) - 58.111

0

10

20

30

40

50

60

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Aq. MeOH

Fig. 21: IC50 seedling growth curves for weed test species by X. strumarium

fruits solvent extracts

114

y = 0.9653ln(x) + 39.307 0

20

40

60

80

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Chloroform (P.minor)

y = 15.879ln(x) - 63.052

0

20

40

60

80

100

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

E. acetate

y = 20.914ln(x) - 114.54

0

20

40

60

80

100

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Hexane

y = 9.397ln(x) - 32.21

0

10

20

30

40

50

60

0 5000 10000Se

ed

ling

gro

wth

in

hb

itio

n

Conc. (mgL-1)

Aq. MeOH

y = 7.7974ln(x) - 17.119

0

10

20

30

40

50

60

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Chloroform (A. fatua)

y = 15.946ln(x) - 77.163

0

20

40

60

80

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

E. acetate

y = 10.045ln(x) - 47.648

0

10

20

30

40

50

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Aq. MeOH

y = 2.6531ln(x) + 30.95

0

10

2030

40

50

60

70

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Chloroform (R. dentatus)

y = 16.939ln(x) - 91.931 0

20

40

60

80

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Hexane

y = 15.502ln(x) - 63.681

0

20

40

60

80

100

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

E. acetate

115

y = 18.524ln(x) - 112.55

0

10

20

30

40

50

60

70

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Hexane

y = 11.992ln(x) - 55.271

0

10

20

30

40

50

60

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Aq. MeOH

y = 6.8883ln(x) - 5.8651

0

10

20

30

40

50

60

70

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Chloroform (C. album)

y = 19.676ln(x) - 104.52

0

20

40

60

80

100

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

E. acetate

y = 19.985ln(x) - 118.03

0

10

20

30

40

50

60

70

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Hexane

y = 10.275ln(x) - 51.87

0

10

20

30

40

50

0 5000 10000

See

dlin

g gr

ow

th

inh

ibit

ion

Conc. (mgL-1)

Aq. MeOH

Fig. 22: IC50 seedling growth curves for weed test species by L. camara leaves

solvent extracts

116

Table 28: IC50 values of seedling growth for weed test species by X. strumarium

fruits solvent extracts

Weed test species Solvent extract IC50 (mgL-1

)

Phalaris minor Chloroform 1519.08

Ethyl acetate 2531.86

Hexane 2165.41

Aq. methanol 4356.13

Avena fatua Chloroform 48311.62


Hexane 4440.73


Rumex dentatus Chloroform 1311


Hexane 4867.24


Chenopodium album Chloroform 2867.91


Hexane 4555.22


117

Table 29: IC50 values of seedling growth for weed test species by L. camara

leaves solvent extracts

Weed test species Solvent extract IC50 (mgL-1

)

Phalaris minor Ethyl acetate 64691.51

Chloroform 1235.95

Hexane 2610.92


Avena fatua Ethyl acetate 5474.57

Chloroform 2906.20

Hexane 4354.43


Rumex dentatus Chloroform 1313.28


Hexane 6471.11


Chenopodium album Ethyl acetate 3328.05

Chloroform 2574.01

Hexane 4481.92


118

Overall seedling growth inhibition was calculated as an average function of

hypocotyl and epicotyl growth inhibition in comparison to control. All fractions

showed growth inhibition to different rates. Maximum inhibition was shown by

chloroform fraction of Lantana camara leaves against Phalaris minor and Rumex

dentatus (Table 25-27). It was assumed that this fraction contains compounds

active against monocot as well as dicot weed species. Based on these results,

chloroform fraction of Lantana leaves was selected for further compound analysis

studies.

4.3. ALLELOCHEMICAL CHARACTERIZATION

Chloroform fraction was selected on the basis of its herbicidal activity.

Silica gel was used for column chromatography. Sample was loaded after

adsorption on silica gel by making a uniform and even layer. Mobile phase of

Hexane : Ethyl acetate (60:40) was used based on TLC profiling. A total of 31

elusions were collected in small column vials. They were left overnight to make

them concentrated and were again subjected to Thin Layer Chromatography (TLC).

Vanillin TLC stain was used for visualization purpose. Fractions with similar TLC

pattern were combined and bio-assayed against radish seeds at 1mg/mL. Sub-

fraction (iii) of fraction 23 showed highest growth inhibition therefore selected for

further analysis. GC-MS (Shimadzu GC-MS-QP2010 ultra) with Helium gas as

carrier was used to find out purity of compound and possible compound

identification. GCMS analysis showed the compound as Vitexin (C21H20O10)

(flavone glucoside).

119

Fig. 24: GC spectrum of compound isolated from chloroform fraction. A single

peak eluted after 14.3min showing an isolated compound with an abundance of

52500 in the sample

120

O

OH

OH

OHH

OH

OH

OH

O

OH

O

Fig. 25: Structure of isolated compound from chloroform fraction of

Lantana camara leaves (Vitexin)

121

Chapter 5

DISCUSSION


Parthenium hysterophorus exerts significant impact on natural communities

by displacement of native species by formation of its large monocultures. In present

study, comparisons of ecological indices across invaded and control plots indicated

significant differences in ecological parameters. These findings are in-line with

other studies on this alien invasive weed, which indicated its strong effects on

ecosystem properties (Riaz and Javaid, 2011; Riaz and Javaid, 2010; Shabbir and

Bajwa, 2007).

The results showed modifications in vegetation composition of invaded and

control plots. Analysis of variance for ecological indices among invaded and

control plots showed significant decrease in ecological indices across site and

invasion status. These results are consistent with other studies on invasive species

indicating their negative effects on bio-diversity and ecosystem properties

(Manchester, 2000; McNeely, 2001; Grice, 2006; Borokini et al., 2011; Jeschke et

al., 2014; Panetta and Gooden, 2017). In our study, despite the negative effect of P.

hysterophorus on species composition, species evenness of control and invaded

plots was not significantly different. That is contradiction to above-mentioned

studies; however, a few studies have shown that invasive species pose little or no

effect on species diversity (Martin, 1999; Hejda and Pysek, 2006; Timsina et al.,

2011). It is reported elsewhere that Parthenium invasion enriches compositional

diversity but may result in extinction of native species (Nigatu and Sharma, 2013).

Wide environmental adaptability, drought tolerance, photo and thermo-

121

122

insensitivity, high seed production and short life cycle (being an annual), small and

light seeds capable of long distance travel via water, wind, birds, animals and

vehicles, longevity of seeds in soil seed banks, strong competition and allelopathy

contribute to its invasiveness (Shabbir and Bajwa, 2006; Hassan et al., 2012; Khan

et al., 2014). Allelopathy plays important role in invasion of Parthenium weed. The

major allelopathic compounds reported from P. hysterophorus are, gentisic, o-

coumaric, p-coumaric, ferulic, vallinic, caffeic, salicylic acid, p-hydroxybenzoic

acid, trans-cinammic acid and sesquiterpene lactone (Borah et al. 2016). These

allelochemicals are supposed to reduce native seed germination, allowing the weed

to pre-empt space and establish monocultures.

Parthenium invasion exhibited variable impacts in five sites by reducing

species number per plot (S), abundance (N), Species richness (R), species evenness

(Jꞌ), Simpson index of dominance (λ) and Shannon index of diversity (Hꞌ). The

trend of decrease in ecological indices in invaded plots is similar to invasion studies

on P. hysterophorus from Australia, Ethiopia, Nigeria, Tanzania and India (Grice,

2006; Kilewa and Rashid, 2012; Seta et al., 2013; Borokini et al., 2011;

Abdulkerim-Ute and Legesse, 2016).The most effected site by Parthenium invasion

was Jhelum followed by Attock, Rawalpindi, Chakwal and Islamabad. The least

invasion impacts in Islamabad compared to other sites are probably because of

management practices in the area being its importance as metropolitan region of

Pakistan while highest dissimilarity in invaded and control plots in Jhelum is

possibly due to saline soil of the area (Anonymous, 2017).

The ordination (nMDS) and ANOSIM showed significant magnitude of the

differences between species assemblages of invaded and control plots. The

123

difference was significant for all of five study sites but the greatest dissimilarity

between invaded and control plots were noticed by Jhelum. It was reported that

Parthenium plant survives naturally more in higher level of soil salinity (Upadhyay

et al., 2013), a condition inimical to establishment of many native plant species.

Consequently the higher invasion impacts in Jhelum are possibly due to its saline

soil (Anonymous, 2017). SIMPER analysis showed dominance of few species in

invaded plots than in control. These were Tephrosia purpurea and Lathyrus

aphaca. Possible reason for their presence in invaded plot may be due to their

aggressive nature as weeds in their own right. Perhaps higher contribution values of

Fabaceae weeds is due to competition potential with Parthenium as suggested by

Belachew & Tessema (2015) and Gnanavel (2013).

Broussonetia papyrifera exerts significant impacts on natural communities

by displacement of native species and hence, exerts discrepancy in natural

ecosystems. This discrepancy results in formation of its large monocultures. In

present study, comparisons of ecological indices across invaded and control plots

indicated significant differences in study area. These findings are in-line with

studies on other alien invasive weeds, which indicated strong effects of the invader

on ecosystem properties (Riaz and Javaid, 2011; Riaz and Javaid, 2010; Shabbir

and Bajwa, 2007). The results show modifications in vegetation composition of

invaded and control plots. Analysis of variance among invaded and control plots

showed significant decrease in ecological indices across site and invasion status.

These results are consistent with studies on invasive species indicating their

negative effects on bio-diversity and ecosystem properties (Manchester, 2000;

McNeely, 2001; Grice, 2006; Borokini et al., 2011; Jeschke et al., 2014; Panetta

and Gooden, 2017). Adaptability to different habitats, rapid growth rate, strategy of

124

vegetative regeneration, effective dispersal by birds and allelopathy contribute to its

invasion success (Malik and Hussain, 2007). Allelopathy especially plays important

role in invasion of this weed. The major allelopathic compounds found in B.

papyrifera are, Broussonin A, Broussonin B, (+)-Marmesin, Broussochalcone A,

(2S)-euchrenone a7, Broussoflavonol F, Naringetol, Albanol A, Moracin N,

Isogemichalcone C, Chushizisin H, Broussoflavonol E, Broussoflavonol G,

Broussoflavonol C, Broussoflavonol D, Chushizisin I, Broussoflavonol B,

Broussoflavonol A, Broussoflavan A, Broussoflavonol F, Kazinol A, Kazinol B,

Gancaonin P, Uralenol, Isolicoflavonol, Chushizisin C, Chushizisin D, Chushizisin

E, Chushizisin B, Chushizisin A, Chushizisin F, Broussochalcone A,

Broussoaurone A, Chushizisin G, Broussinol, Isobavachalcone, Broussochalcone

B, Broussonin C, Broussonin F, Broussin, Broussonin E (Mei et al., 2009; Lee et

al., 2001; Fukai et al., 1986). These allelochemicals are supposed to reduce native

seed germination, allowing the weed to pre-empt space and establish monocultures.

Paper mulberry invasion exhibited variable impacts in five sites by reducing

the species number per plot (S), abundance (N), Species richness (R), species

evenness (Jꞌ), Simpson index of diversity (λ) and Shannon index of dominance (Hꞌ).

The trend of decrease in ecological indices in invaded plots is similar to invasion

studies on B. papyrifera from Australia, Argentina, Carolina, Florida, Columbia,

Louisiana, Georgia, North Carolina, Maryland, Pennsylvania, Oklahoma, Uganda,

South Tennessee and Virginia (Ghersa et al., 2002; Csurhes, 2016). The most

effected site by Paper mulberry invasion was Islamabad followed by Attock,

Jhelum, Rawalpindi and Chakwal. The most invasion impacts in Islamabad

compared to other sites are probably because of its initial introduction in Capital

territory during 1960swith objective to make capital city green (Qureshi et al.,

125

2014).

The ordination (nMDS) and ANOSIM showed significant magnitude of

differences between species assemblages of invaded and control plots. The

difference was significant for all of five study sites but the greatest dissimilarity

between invaded and control plots. In current study, we noticed negative effects of

Paper mulberry on all of ecological indices in invaded over control plots. The

highest impact is noticed in Islamabad. SIMPER analysis showed dominance of

few species in invaded plots than in control. These were Tribulus terrestris,

Malvastrum coromandelianum, Cynodon dactylon, Silybum marianum, Calotropis

procera, Datura innoxia, Digeria muricata, Kochia indica and Desmostachya

bipinnata. It was noticed that grasses are most affected by Paper mulberry invasion.

Reduction in grass density due to Paper mulberry invasion is reported earlier by

Bosu et al. (2013).

Lantana camara is predominant in some countries in the world including

Pakistan, Australia, India, and Africa (Goncalves et al., 2014). Lantana weed exerts

significant impact on natural communities by native species displacement hence

exert imbalance in natural ecosystems. Ecologically diversified adaptability of L.

camara allows its rapid expansion resulting in monoculture formation and native

biodiversity reduction. The chances of invasiveness of Lantana are high in future

due to its rapid spread, high adaptability to different environments, tenacious

resistance to cutting and burning and climate change (Taylor et al., 2012; Zhang et

al., 2014). In present study, comparisons of ecological indices across invaded and

control plots indicated significant differences in study area. These findings are in-

line with other studies on alien invasive weeds, which indicated strong effects of

126

the invader on ecosystem properties (Riaz and Javaid, 2011; Riaz and Javaid, 2010;

Shabbir and Bajwa, 2007). In Pakistan, Lantana invasion is reported earlier from

Rawalpindi and Islamabad (Malik and Husain, 2006; Fatimah and Ahmed, 2012;

Khan et al., 2010). In current study, comparisons of ecological indices across

invaded and control plots in Pothwar region indicated significant differences in

ecological diversity indices. These findings are consistent with other studies on this

invasive species, which indicated its strong effects on ecosystem properties

(Lemma et al., 2015; Tadesse et al., 2017).

Phenotypic plasticity, high reproductive potential, immunization to grazing

pressure, allelopathy and fire tolerance contributes to invasiveness of Lantana

(Bhakat and Maiti, 2012). Allelopathy plays important role in invasion of this

weed. The major allelopathic compounds found in Lantana weed are salicylic acid,

gentisic acid, coumarin, p-hydroxybenzoic acid, ferulic acid, lantadene A, 6-methyl

coumarin, lantadene B, oleanolic acid, lantalonic acid, icterogenin, lantolonic,

ursolic acid and oleonolic acid (Yadav et al., 2016).

The results demonstrate differences in vegetation composition of invaded

and control plots. Analysis of variance among invaded and control plots showed

significant decrease in ecological indices across site and invasion status. These

findings are consistent with other studies on invasive species indicating strong

negative effects of invasive species on floral diversity and ecosystem properties


al., 2014; Panetta and Gooden, 2017).

Lantana invasion exhibited variable impacts in five sites (districts) by

reducing species number per plot (S), abundance (N), Species richness (R), species

127

evenness (Jꞌ), Simpson index of dominance (λ) and Shannon index of diversity (Hꞌ).


studies on L. camara from Australia (Duggin and Gentle, 1998), Fiji (Taylor and

Kumar, 2014), Eastern Africa (Shackleton et al., 2017), South Africa (Vardien et

al. 2012), China (Fan et al., 2010), Ethiopia (Chanie and Assefa, 2015) and India

(Dobhal et al., 2011; Priyanka and Joshi, 2013). In current study, we noticed

negative effects of L. camara on all of ecological indices in invaded over control

plots. The highest impact is noticed in Attock district.

Xanthium strumarium is predominant in some countries in the world

including Pakistan, Australia, India, America and Turkey (Shafique et al., 2007).

Xanthium weed exerts significant impact on natural communities by native species

displacement hence exert imbalance in natural ecosystems. Ecologically diversified

adaptability of X. strumarium allows its rapid expansion resulting in monoculture

formation thus native biodiversity reduction (Tadesse et al., 2017). Xanthium

invasion was also reported earlier in Islamabad (Khan et al., 2010); North-west

Pakistan (Marwat et al., 2010); Khyber Pakhtunkhwa (Khan et al., 2011); upper

Indus plains in Punjab (Malik et al., 2012) in Pakistan. In current study,

comparisons of ecological indices across invaded and control plots in Pothwar

region indicated significant differences in ecological diversity indices. These

findings are consistent with other studies on this invasive species, which indicated

its strong effects on ecosystem properties (Lemma et al., 2015; Tadesse et al.,

2017). Facilitated dispersal of prickly burs by adhering to human clothing and

animals, by water, as contaminant of wool, viability of seeds up to five years,

photo-insensitivity and allelopathy are traits related to invasiveness of the weed

(Hussain et al., 2013; Qureshi et al., 2014). Allelopathy plays important role in

128

invasion of this weed. The major allelopathic compounds found in X. strumarium

are, xanthinin, xanthatin, xanthumin, atractyloside, xanthostrumarin, phytosterols,

carboxyatractyloside; isoxanthanol, xanthanol, 4-oxo-bedfordia acid, xanthinosin,

xanthanolides, hydroquinone, α and γ-tocopherol, caffeoylquinic acids, deacetyl

xanthumin, thiazinedione, linoleic acid, carboxyatractyloside and several

sesquiterpene lactones (Kamboj and Saluja, 2010).

The results demonstrate differences in vegetation composition of invaded

and control plots. Analysis of variance among invaded and control plots showed

significant decrease in ecological indices across site and invasion status. These

findings are consistent with other studies on invasive species indicating strong

negative effects of invasive species on floral diversity and ecosystem properties


al., 2014; Panetta and Gooden, 2017).

Xanthium strumarium invasion exhibited variable impacts in five sites by

reducing species number per plot (S), abundance (N), Species richness (R), species

evenness (Jꞌ), Simpson index of dominance (λ) and Shannon index of diversity (Hꞌ).


studies on X. strumarium from Ethiopia, Zimbabwe, Pakistan, Nigeria, Tanzania

and India (Lemma et al., 2015; Tadesse et al., 2017; Seifu et al., 2017; Chikuruwo

et al., 2017, Hussain et al., 2014). In current study, we noticed negative effects of

X. strumarium on all of ecological indices in invaded over control plots. The

highest impact is noticed in Rawalpindi district.


differences between diversity indices of invaded and control plots. The difference

129

was significant for all of five study sites but greatest dissimilarity between invaded

and control plots were noticed by Rawalpindi. Invasion of X. strumarium was

reported earlier as top invasive species from Rawalpindi region along two other

species, viz. Prosopis juliflora and Lantana camara (Malik and Husain, 2006).

SIMPER analysis showed 53.90% overall dissimilarity among invaded and control

plots. Analysis showed herbs to be most affected by Xanthium invasion than shrubs

and trees. These were Solanum nigrum, Parthenium hysterophorus, Ajuga

bracteosa, Rumex dentatus, Typha domengensis, Malva parviflora, Tribulus

terrestris and Oxalis corniculata. Results indicate that grass and herb species were

more affected by Xanthium invasion than shrubs and trees.


differences between diversity indices of invaded and control plots. The difference

was significant for all of five study sites but greatest dissimilarity between invaded

and control plots were noticed by Attock. Invasion of L. camara was reported

earlier as top invasive species from Attock region along two other species, viz.

Prosopis juliflora and Xanthium strumarium (Malik and Husain, 2006). SIMPER

analysis showed 65.56% overall dissimilarity among invaded and control plots.

Analysis showed herbs to be most affected by Lantana invasion than shrubs and

trees. These were Solanum nigrum, Parthenium hysterophorus, Ajuga bracteosa,

Rumex dentatus, Typha domengensis, Malva parviflora, Tribulus terrestris and

Oxalis corniculata. There are related studies reporting diversity loss due to Lantana

invasion (Sharma et al., 2009; Gooden et al., 2009; Singh et al., 2014).

For four studied invaders, the invasion impacts on biodiversity in Pothwar

region were ranked as Lantana camara > Xanthium strumarium Parthenium >

130

hystreophorus> Broussonetia papyrifera. Allelopathic interactions of studied

invaders might be playing a crucial role in their invasion process. El-Ghareeb

(1991) and Ridenour & Callaway (2001) suggested role of allelopathy to ability of

exotic species to become dominant in encroached plant communities. Callaway and

Vivanco (2007) demonstrated allelopathy for invader Centaurea diffusa.

Allelopathic effects of invaders Centaurea maculosa Lam. (Ridenour and

Callaway, 2001), Lantana camara L. (Sharma et al., 2005), Alliaria petiolata (M.

Bieb.) Cavara & Grande, (Callaway et al., 2008), Solidago canadensis L.

(Abhilasha et al., 2008), Solidago Canadensis (Zhang et al., 2009), Bothriochloa

ischaemum (Greer et al., 2014) and Centaurea diffusa (Tharayil et al., 2008)

contribute to their invasion success.

5.2. ALLELOAPTHY BIOASSAYS AND HERBICIDAL ACTIVITY

Proof of allelopathy requires proof of production of phytotoxin(s) by donor

species. Bioassay is most important tool used to ascertain toxic potential of plant

extracts. There is a broad range of bioassays that can be easily performed in

laboratory. Seed germination bioassay studies are most widely used techniques in

allelopathy to screen allelopathic potential (Macias et al., 2008; Osbourn and

Lanzotti, 2009). Results about toxicity can be established by observing parameters

in target plant, including effects on seed germination rate, root growth, hypocotyl

elongation, whole plant growth or some functional processes (Blum, 2014).

For preliminary toxicity assessment of selected plants, radish seeds were

used because they have been used as standard in basic phytotoxicity studies for the

reason of their rapid growth inhibition response to phytotoxins (De-Feo et al.,

2003; Fatima et al., 2009). Radish seeds are exposed to plant extracts and toxicity

131

is evaluated on the basis of seedling growth inhibition. It is cost effective, safe,

reliable, reproducible and easy to handle assay without any special equipment

requirements that make it valuable bench-top bioassay in research (Arzu and

Camper, 2002). Lantana leaves and Xanthium fruits were selected based on these

assays.

Currently 216 herbicide-resistant biotypes of weeds in 45 countries have

potential allelopathic activities (Bhowmik, 2000). There is need for further research

to select the species exhibiting allelopathy. Allelopathy researchers isolated and

tested phytotoxicity of many allelochemicals (An et al., 2000; Orr et al., 2005;

Santos et al., 2007). Yang et al. (2006) studied phytotoxicity of allelochemicals in

Ageratina adenophora on rice seedlings. Kannan and Kulandaivelu (2007) studied

bioactivity of withaferin A from Withania somnifera roots. Xuan et al. (2006) and

Fan et al. (2006) identified allelochemicals from different weeds. Leicach et al.

(2007; 2009) used chromatography methods and spectroscopy for alkaloid

identification from plants.

The structure of the purified compound was identified as Vitexin (glucoside

flavone). 18mg of the compound from 500g of dried L. camara leaves. Vitexin has

been previously isolated from Vitex agnus-castus, Passiflora incarnata,

Phyllostachys nigra, Pennisetum glaucum, Fagopyrum esculentum and Crataegus

monogyna and flowers of Lantana camara. It is confirmed to possess anti-

inflammatory, antioxidant and anti-nociceptive activity (El Kassem et al., 2011; El-

Kassem et al., 2012) however; no phytotoxic activity has ever been reported for it.

Flavonoids are frequently associated with allelopathy and numerous

investigations have associated with them having phytotoxic activity (Perry et al.,

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652013000300881#B20

132

2007, Simoes et al., 2008, Cipollini et al., 2008) show the importance of flavonoids

as allelophathic agents. These compounds have been shown inhibitory to ATP

generation and electron transport in chloroplasts and mitochondria (Singh and Tu,

1996). It is suggested to further analyze the action mechanism of isolated comound

as of the hundreds of identified allelochemicals, only for a few mode of action

(MOA) have been determined. Usually allelochemicals operate by mechanisms not

possessed by synthetic compounds making natural compounds promising source of

new leads to herbicides.




133

Recommendations

1. This work can be processed to study dose response (hormesis) analysis of

isolated compound from L. camara leaves in lab as well as in field

conditions. Based on results, it can be presented as potential commercial

herbicide or can act as lead compound in the industry

2. Potential action sites of the isolated compound in studied weeds can be

investigated which may result in identification of novel action site.

133

134

SUMMARY

The increased occurrence of invasion around the world poses a major threat

to indigenous diversity. Plant invasions in novel areas deplete species diversity,

alter indigenous community composition, affect ecosystem process and thus cause

huge ecological and economic imbalance. Invasive species studies in the past

revealed that the effects of invasion are complex and can permanently alter the

function and structure of communities, cause local annihilations and changes in

ecosystem processes. Invasion by alien plant species affect the composition and

dynamics of species on a wide scale and have great impact on ecosystem functions.

The decrease in ecological diversity indices in invaded over control sites in present

study indicated that plant communities become less productive due to studied

invaders hence a threat to plant diversity of invaded areas. There is urgent need of

appropriate control measures including use of proven biological control agents for

this weed in Pakistan as done elsewhere around the Globe (e.g., Australia and

South Africa (Kaur et al., 2014; Strathie et al., 2011).

Results provided evidence about herbicidal potential of tested plant species

against weeds of wheat crop (Avena fatua, Phalaris minor, Chenopodium album

and Rumex dentatus). To the best of our knowledge Lantana camara leaves have

not been previously reported to possess flavonoid compound ‘vitexin’ and tested

against weeds of wheat crop. So this investigation has provided a clue about its

herbicidal importance for further research.

134

135

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xiv

Annexure 1: Distribution of plots for impact analysis of Lantana camara L. in


S.# District Non Infested sites Infested sites

Latitude Longitude Latitude Longitude

1 Attock 33.54027 72.474916 33.77222 72.35398

2 Attock 33.27471 72.397563 33.77247 72.35388

3 Attock 33.19626 71.853012 33.77254 72.35388

4 Attock 33.65818 72.113483 33.7725 72.35391

5 Attock 33.40561 72.165382 33.77218 72.35398

6 Attock 33.77727 72.626924 33.77238 72.35392

7 Chakwal 33.03184 73.074953 32.45144 72.5629

8 Chakwal 33.03847 72.627863 32.95613 72.86692

9 Chakwal 32.82951 72.725797 32.95142 72.86264

10 Chakwal 33.00204 72.09035 32.96334 72.87293

11 Chakwal 32.8232 72.302409 32.95616 72.86695

12 Chakwal 32.8348 72.949675 32.96336 72.87243

13 Islamabad 33.6473 73.132824 33.54731 73.17496

14 Islamabad 33.74251 73.060974 33.2675 73.2932

15 Islamabad 33.62709 72.877171 33.55119 73.17161

16 Islamabad 33.75418 73.153534 33.55121 73.17167

17 Islamabad 33.55105 73.173918 33.55963 73.16522

18 Islamabad 33.67855 73.221071 33.5472 73.17508

19 Jhelum 32.99351 73.549017 32.92635 73.72921

xv

20 Jhelum 32.7533 73.361073 32.92761 73.72809

21 Jhelum 33.10644 73.420991 32.93781 73.71693

22 Jhelum 33.01095 73.262912 32.92769 73.72802

23 Jhelum 32.94736 73.374611 32.9263 73.72917

24 Jhelum 32.63517 73.184612 32.93778 73.71697

25 Rawalpindi 33.25991 73.043812 33.55954 73.16534

26 Rawalpindi 33.64263 73.453936 33.27091 73.2908

27 Rawalpindi 33.33173 73.412042 33.27091 73.29072

28 Rawalpindi 33.79239 73.295057 33.27502 73.28137

29 Rawalpindi 33.45265 73.220536 33.27496 73.28938

30 Rawalpindi 33.5971 72.811904 33.26736 73.29315

xvi

Annexure 2: Distribution of plots for impact analysis of Xanthium strumarium L.

in Pothwar region, Pakistan



1 Attock 33.26852 72.38076 33.77264 72.35387

2 Attock 33.82221 72.51754 33.77264 72.35389

3 Attock 33.35134 71.96722 33.77266 72.35383

4 Attock 33.60424 72.17156 33.77218 72.35385

5 Attock 33.5951 72.45974 33.77278 72.35422

6 Attock 33.52636 72.72192 33.77277 72.35426

7 Chakwal 32.7824 72.97105 33.2695 73.2944

8 Chakwal 32.85372 72.67703 32.97502 72.88657

9 Chakwal 32.79957 72.12658 32.9756 72.88656

10 Chakwal 32.79736 72.12609 33.00749 72.9296

11 Chakwal 33.07268 72.58338 33.00748 72.92962

12 Chakwal 32.99303 72.97345 33.0077 72.92957

13 Islamabad 33.74495 73.07789 33.55653 73.16738

14 Islamabad 33.65969 72.99969 33.55657 73.1672

15 Islamabad 33.63418 72.88882 33.55556 73.16839

16 Islamabad 33.56264 73.15206 33.55554 73.16832

17 Islamabad 33.73827 73.20965 33.56455 73.1616

18 Islamabad 33.66677 73.18396 33.56465 73.16157

19 Jhelum 33.09408 73.48255 32.93058 73.72552

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20 Jhelum 32.96734 73.34177 32.93052 73.72556

21 Jhelum 32.60984 73.11047 32.92943 73.72663

22 Jhelum 32.53028 72.71395 32.92947 73.7267

23 Jhelum 32.78681 73.42663 32.92643 73.729

24 Jhelum 32.97268 73.56759 32.92653 73.72903

25 Rawalpindi 33.25989 72.81164 33.26011 73.30769

26 Rawalpindi 33.44583 73.07083 33.26025 73.30748

27 Rawalpindi 33.815 72.74927 33.25753 73.30897

28 Rawalpindi 33.19724 73.16808 33.25737 73.30882

29 Rawalpindi 33.76408 73.42033 33.26957 73.29445

30 Rawalpindi 33.48383 73.47908 33.60774 72.92959

xviii

Annexure 3: Distribution of plots for impact analysis of Parthenium

hysterophorus L. in Pothwar region, Pakistan



1 Attock 33.76107 72.37749 33.77474 72.35545

2 Attock 33.48377 72.07113 33.77479 72.35546

3 Attock 33.60365 72.35143 33.77252 72.35388

4 Attock 33.91367 72.5067 33.77255 72.35384

5 Attock 33.6583 72.59222 33.77466 72.35691

6 Attock 33.37587 72.40829 33.77448 72.35772

7 Chakwal 32.929 72.69845 32.93327 72.85687

8 Chakwal 32.97477 71.92183 32.93338 72.85674

9 Chakwal 32.80349 72.99044 32.93423 72.85579

10 Chakwal 33.03038 72.95831 32.93421 72.85569

11 Chakwal 32.75875 72.30241 32.9338 72.85643

12 Chakwal 32.97361 72.38456 32.9333 72.85657

13 Islamabad 33.71307 73.13643 33.56645 73.15998

14 Islamabad 33.56264 73.13495 33.56626 73.16022

15 Islamabad 33.68697 73.06969 33.54324 73.17808

16 Islamabad 33.60547 73.19319 33.54318 73.17812

17 Islamabad 33.67994 72.95085 33.54816 73.17378

18 Islamabad 33.67855 73.22107 33.54812 73.17375

19 Jhelum 32.96931 73.61676 32.93142 73.72236

xix

20 Jhelum 33.1247 73.51508 32.93134 73.7224

21 Jhelum 32.97806 73.41359 32.93416 73.7165

22 Jhelum 32.86224 73.24308 32.93404 73.71651

23 Jhelum 32.51608 72.69548 32.9317 73.72214

24 Jhelum 32.77398 73.4523 32.9316 73.72207

25 Rawalpindi 33.29672 72.98762 33.27612 73.28851

26 Rawalpindi 33.78109 73.34359 33.27613 73.28852

27 Rawalpindi 33.47071 73.38866 33.27146 73.29381

28 Rawalpindi 33.55648 73.00129 33.27134 73.29372

29 Rawalpindi 33.3011 73.27954 33.264 73.31151

30 Rawalpindi 33.78304 72.74593 33.26393 73.31157

xx

Annexure 4: Distribution of plots for impact analysis of Broussonetia

papyrifera (L.) L’Herit. ex Vent. in Pothwar region, Pakistan



1 Attock 33.55287 72.38741 33.77252 72.35388

2 Attock 33.34541 72.50512 33.77474 72.35545

3 Attock 33.65818 72.11348 33.77466 72.35691

4 Attock 33.28548 71.90258 33.77255 72.35384

5 Attock 33.28664 72.18025 33.77479 72.35546

6 Attock 33.67813 72.58727 33.77448 72.35772

7 Chakwal 32.80349 72.99044 32.93423 72.85579

8 Chakwal 33.03038 72.95831 32.93421 72.85569

9 Chakwal 33.06896 71.96645 32.93338 72.85674

10 Chakwal 32.95927 72.5143 32.93327 72.85687

11 Chakwal 32.73893 71.94549 32.9333 72.85657

12 Chakwal 33.15206 72.58284 32.9338 72.85643

13 Islamabad 33.6175 73.1453 33.54324 73.17808

14 Islamabad 33.72299 73.07694 33.56626 73.16022

15 Islamabad 33.63205 72.89204 33.54816 73.17378

16 Islamabad 33.53618 73.13426 33.56645 73.15998

17 Islamabad 33.70957 72.92055 33.54318 73.17812

18 Islamabad 33.71325 73.21611 33.54812 73.17375

19 Jhelum 32.96931 73.61676 32.93134 73.7224

xxi

20 Jhelum 32.97806 73.41359 32.93404 73.71651

21 Jhelum 33.09496 73.39611 32.93416 73.7165

22 Jhelum 32.50532 72.76719 32.93142 73.72236

23 Jhelum 32.82343 73.37461 32.9316 73.72207

24 Jhelum 32.61535 73.05573 32.9317 73.72214

25 Rawalpindi 33.81278 72.70132 33.26393 73.31157

26 Rawalpindi 33.15735 73.06143 33.27613 73.28852

27 Rawalpindi 33.25706 72.71498 33.27134 73.29372

28 Rawalpindi 33.46725 72.92198 33.27146 73.29381

29 Rawalpindi 33.47567 73.38866 33.27612 73.28851

30 Rawalpindi 33.68564 73.45352 33.264 73.31151

Qureshi et al.: Multivariate impact analysis of Parthenium hysterophorus invasion on above-ground plant diversity in Pothwar

region of Pakistan

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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(5):5799-5813.

http://www.aloki.hu ISSN 1589 1623 (Print) ISSN 1785 0037 (Online)

DOI: http://dx.doi.org/10.15666/aeer/1605_57995813

2018, ALÖKI Kft., Budapest, Hungary

MULTIVARIATE IMPACT ANALYSIS OF PARTHENIUM

HYSTEROPHORUS INVASION ON ABOVE-GROUND PLANT

DIVERSITY IN POTHWAR REGION OF PAKISTAN

QURESHI, H.1,2*

– ARSHAD, M.1 – BIBI, Y.

1 – AHMAD, R.

3 – OSUNKOYA, O. O.

4 – ADKINS, S. W.

2

1Department of Botany, PMAS-Arid Agriculture University, 46300-Rawalpindi, Pakistan

2School of Agriculture and Food Science, The University of Queensland

St. Lucia-4072 Brisbane, Australia

3Quality Enhancement Cell, PMAS-Arid Agriculture University- 46300 Rawalpindi, Pakistan

4Invasive Plant & Animal Science, Biosecurity Unit, Queensland- Department of Agriculture &

Fisheries, Brisbane-4001, Australia

*Corresponding author

e-mail: [email protected], [email protected]

(Received 3rd

Mar 2018; accepted 10th May 2018)

Abstract. Phytosociological studies help to understand extent of biological invasion. Multiple analyses of

ecological parameters at different locations derive general explanations of impact on species diversity in

plant communities. Current study assessed the impact of Parthenium hysterophorus (an annual weed of

great significance in Pakistan and worldwide) invasion on native vegetation in Pothwar region of

Pakistan. The approach used for the study was random samplings with two categorical factors: invaded

and non-invaded under same habitat conditions. Differences in number of species (S), abundance (N),

species richness (R), evenness (Jꞌ), Shannon diversity index (Hꞌ) and Simpson index of dominance (λ)

were compared between invaded and control plots by t-test series. Control plots harbored by average of

0.9 more species per 10 m2. The control category was more diverse (Hꞌ = 1.73) than invaded category

(Hꞌ = 1.53). The higher value of species richness in control plots shows the heterogeneous nature of

communities and vice versa in invaded plots. The lower value of index of dominance in invaded plots

shows less sample diversity than in the control ones. At multivariate scale, ordination (nMDS) and ANOSIM showed significant magnitude of differences between invaded and control plots in all sites. The

most effected site by Parthenium invasion was Jhelum followed by Attock, Rawalpindi, Chakwal and

Islamabad. The decrease in diversity indices in invaded over control sites indicated less productive plant

communities due to Parthenium invasion. This makes Parthenium a candidate of consideration for

appropriate control measures.

Keywords: invasion impacts, diversity indices, multivariate analysis, diversity conservation, PRIMER

Introduction

There has been a rapid acceleration in the number and rate of plant invasions

attributed to increased dispersal of exotics and expansion of disturbed habitats

associated with rapid human population growth (Collier and Vankat, 2002). The

introduction of invasive plants may change the structure and function of ecosystem,

e.g., alterations in succession, species composition, biomass, net primary productivity

and nutrient cycling (Charles and Dukes, 2007; Dassonville et al., 2008). Invasive

species may also deplete available resources. Other studies have shown changes at

population, community and landscape levels (Collier and Vankat, 2002; Qureshi et al.,

2014). Consequently, studying the community level impacts of the invader identifies its


region of Pakistan

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potential effects and provides valued information for management and nature

conservation strategies (Hejda et al., 2009).

Plant invasions deplete native species diversity, alter community composition and

effect ecosystem processes thus cause ecological and economic imbalance (Kunzi et al.,

2015). Exotic plants coexist in relative harmony in native habitat but competitively

exclude neighbors in recipient communities. Various studies have provided data on the

effects of exotic invasive plants on declining indigenous diversity and altering native

community composition. These studies assumed different mechanisms that generate

substantial invasion impacts. Among these processes are allelopathy, competition, and

alteration of native ecosystem characteristics (Odat et al., 2011). Direct competition

with native flora may result in monocultures of exotic species, e.g. Parthenium

hysterophorus in Pakistan, Australia and India and Psidium cattleianum in Mauritius

(Dogra et al., 2010). In different parts of the world, 80% of endangered species are

threatened by alien invasive species (Pimentel et al., 2005).

Parthenium is an aromatic, annual herb native to Mexico, southern United States and

South & Central America. It was inadvertently introduced to many countries and now

has become a troublesome rangeland and agricultural weed in parts of Africa, Asia,

Australia and the Pacific Islands (Fig. 1). Because of its status in the world, it is

documented among world’s top ten worst weeds (Tamado and Milberg, 2000; Khan et

al., 2014). P. hysterophorus is assumed to move in to India along food grains trade in

from USA. It then has spread to sub-continent (Nath, 1988). It is supposed to enter

Pakistan via road links where automobiles cross at many places every day. In Pakistan,

Parthenium weed was stated in the 1980s from Gujarat, Punjab (Razaq et al., 1994).

Since then, it has spread rapidly all through to Islamabad, Punjab Province and parts of

Khyber Pukhtunkhwa. Parthenium affects biodiversity, crop production and human and

animal health (Shabbir, 2013). It grows in a range of habitats. Wide environmental

adaptability, drought tolerance, photo and thermo-insensitivity, high seed production,

small and light weighted seeds adept of long distance travel via water, wind, birds,

animals and vehicles, longevity of seeds in soil seed banks, strong competition and

allelopathy contribute to its invasiveness (Khan et al., 2014; Hassan et al., 2012;

Shabbir and Bajwa, 2006).

Figure 1. Distribution map and invasion status of Parthenium weed around the globe


region of Pakistan

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Parthenium is one of the worst weeds presently known in Pakistan. No previous

study is reported from Pothwar region regarding its ecological impacts. The current

study was carried out to find out (1) what is the effect of Parthenium weed on

ecological diversity indices in different districts of Pothwar region (assuming each

district as ‘site’); (2) do the effects on diversity differ between different sites (districts)

in the area?

Materials and methods

Study area

Pothwar is a north-eastern plateau in Pakistan, making the northern part of Punjab. It

edges Azad Kashmir (the western parts) and Khyber Pakhtunkhwa (southern parts).

Pothwar Zone extends from 32.5°N to 34.0°N latitude and 72°E to 74°E longitude and

lies between the Indus and Jhelum River. The plateau expanses from salt range

northward to the foothills of Himalayas. The Pothwar region embraces Jhelum,

Islamabad, Attock, Rawalpindi and Chakwal districts (Table 1). Total area of Pothwar

region is 28488.9 km2. (Rashid and Rasul, 2011). Pothwar region has an extreme

climate with hot summers and cold winters. Weather is divided into four seasons: Cold

(December-March); Hot (April-June); Monsoon (July-September) and Post-Monsoon

season (October-November). This area gets an average annual rainfall of 812 mm, about

half of which occurs in the Monsoon months (July-September). The mean maximum

temperature rises till the month of June and then falls appreciably with advent of rains,

being coldest in January (14.62-18.7 °C). Average temperatures range from 14 °C in

January to 37 °C in June (Fig. 2). The region has broadly four types of soil: loess, river

alluvium, residual and piedmont alluvium. Due to dynamic climate and combination of

hills and plains, Pothwar region is rich in biodiversity. Native vegetation is

characterized by open patches of grasses and forb species. Albizia lebbeck (L.) Benth.,

Acacia modesta Wall., Abies pindrow (Royle ex D. Don) Royle, Cassia fistula L.,

Cedrela toona Roxb. ex Rottler, Dalbergia sissoo Roxb., Dodonaea viscosa Jacq.,

Ficus religiosa L., Ficus benghalensis L., Melia azedarach L., Olea cuspidata Wall. Ex

G. Don., Zizyphus jujuba Mill. and Zizyphus nummularia (Burm. f.) Wight & Arn. are

principle species in the region (Shabbir et al., 2012; Ghufran et al., 2013).

Table 1. Coordinates of study sites

District Coordinates

Jhelum 32.94°N, 73.72°E

Islamabad 33.73°N, 73.09°E

Attock 33.76°N, 72.36°E

Rawalpindi 33.59°N, 73.04°E

Chakwal 32.93°N, 72.85°E

Experimental design

Field work was carried out during July-August (being the maximum growth period

of plants), 2016. The effect of invasion was studied in each of five districts (Attock,

Chakwal, Jhelum, Islamabad and Rawalpindi). Ecological indices for selected invaders


region of Pakistan

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were calculated and compared at various sites. The sampling technique was random.

For each district six invaded and six non-invaded paired vegetation plots (each

3.16 × 3.16 m in size, i.e., 10 m2 in area) were sampled. Based on visual observations,

plot of invaded vegetation (‘invaded plot’) where the invader showed dominance was

considered as ‘treatment’ and a second vegetation plot, usually 0.5-1 km apart from

treatment, where invader has no dominance (‘non-invaded plot’) was considered as the

“control”. The estimated density of the weed in the area across locations was 4/m2. In

all, a total of 60 vegetation plots were sampled (consisting of six paired samples per

district, and hence 30 treatments; 30 controls for the entire Pothwar region) (Fig. 3).

Within each randomly chosen plot (10 m2 in area), all vascular plant species in control

and invaded plots were identified to species level.

Figure 2. Mean monthly climate data of Pothwar region, Pakistan for year 2017. (Data sourced

from Pakistan Meteorological Department University Road Karachi, Pakistan)


region of Pakistan

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Figure 3. Distribution of plots for impact analysis of Parthenium hysterophorus in Pothwar

region

Data analyses

Species frequency data were created and invasion impacts of P. hysterophorus on

local flora were assessed by calculating and comparing ecological indices including

Margalef’s index of richness, Shannon–Weaver index of diversity, Simpson index of

dominance and index of evenness for control and invaded sites (Magurran, 1998). These

parameters were calculated as (Eqs. 1, 2, 3 and 4):

(Eq.1)

N = Total number of individuals; S = Total number of species.

Shannon-Weaver index of diversity '

1

( ) S

i i

i

n nH ln

N N

(Eq.2)

N = Total number of individuals of all species; n = Actual number of individuals of one

species.

(Eq.3)

N = Total number of individuals of all species; n = Number of individuals of one

species.

Index of evenness '

( )H

ElnS

(Eq.4)

Hꞌ is Shannon’s index; S = Number of species.


region of Pakistan

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Rarefaction curves were plotted to determine if sampling was adequate in each

district using observed, Coleman’s, Jackknife, Bootstrap and Chao2 models in PRIMER

v. 7 (Clarke and Warwick, 2001). All gave comparable results; consequently only that

of real (observed) data are presented. Data were then subjected to univariate and

multivariate analyses of non-metric multidimensional scaling procedure (Clarke and

Gorley, 2015). Data were log transformed to achieve criteria of normality (evenness and

Simpson index of diversity). For invasion impact analysis, diversity indices including

total number of species (S), abundance (N), species richness (R), species evenness (Jꞌ),

Shannon index of diversity (H′) and Simpson index of dominance (λ) were calculated

for control as well as for invaded plots. The above ecological indices were subjected to

analysis of variance (ANOVA) with invasion status and districts as factors using IBM

SPSS v. 21. Differences between ecological indices for five districts were individually

tested for significance between invaded and control plots by multiple comparisons tests

of t-test. Data were further analyzed for species assemblages by non-metric

multidimensional scaling (nMDS) in two-three dimensions with invasion status

(control, invaded) as factor using PRIMER V.7 software. nMDS was used to ordinate

the similarity of data between site categories (invaded, control) based on Bray-Curtis

dissimilarity matrix following log-transformation of species abundance data due to zero

species count in some plots. The range of clustering of sites and locations in response to

invasion were assessed by analysis of similarity (ANOSIM) and similarity percentage

(SIMPER). ANOSIM relates mean difference of ranks between and within groups,

generating the Global statistic (R). The values of Global statistic (R) range from -1 to

+1. Values near 0 and negative values demonstrate similarity among groups. Values

impending +1 indicate a strong dissimilarity among groups (Clarke and Warwick, 2001;

Osunkoya et al., 2017). SIMPER identified species contributed most to average

dissimilarity between groups (invaded and control plots). This technique calculates

average impact of each species contributing to dissimilarity between groups (Clarke and

Warwick, 2001). Values of percentage similarity between groups range between 0 to

100, with 100 stating maximum similarity.

Results

To assess sampling completeness, rarefaction curves plotting cumulative number of

species as a function of sampling effort were used which indicated that sampling was

reasonably complete (Fig. 4). A total of 56 plant species from 50 genera were

documented during the study (Table 2). A total of 56 species were recorded in control

plots compared with 37 in infested plots. Mean species diversity and richness/quadrat

was higher in control plots (Fig. 5).

Table 2. Plant species found in studied plots, family and life form

S# Plant species Family Life form



3 Argemone mexicana L. Papaveraceae Herb


5 Astragalus scorplurus Bunge. Papilionaceae Herb

6 Bellis perennis L. Asteraceae Herb

7 Broussonetia papyrifera (L.) L’Herit. ex Vent. Moraceae Tree


region of Pakistan

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S# Plant species Family Life form

8 Calotropis procera Br. Asclepiadaceae Shrub




12 Circium arvense L. Asteraceae Herb


14 Cynodon dactylon L. (Pers.) Poaceae Grass


16 Datura innoxia Miller Solanaceae Shrub


18 Digitaria ciliaris (Retz.) Koeler Poaceae Grass



21 Impatiens edgeworthii Hook. f. Balsaminaceae Herb

22 Lathyrus aphaca L. Papilionaceae Herb

23 Malvestrum coromandelianum (L.) Garcke Malvaceae Herb

24 Medicago polymorpha L. Papilionaceae Herb

25 Poa annua L. Poaceae Grass


27 Prosopis cineraria (Linn.) Druce Mimosaceae Tree

28 Prunella vulgaris L. Labiateae Herb


30 Ricinus communis L. Euphorbiaceae Shrub



33 Rumex hastatus D. Don Polygonaceae Shrub


35 Saxifragra androsacea L. Saxifragaceae Herb

36 Silybum marianum (L.) Gaertn. Asteraceae Herb



39 Solanum surattense Burm. F. Solanaceae Shrub


41 Sonchus asper (L.) Hill Asteraceae Herb

42 Sorghum halepense L. Poaceae Grass

43 Suaeda fruticosa Forsk. Amaranthaceae Shrub


45 Taraxacum officinale (L.) Weber ex F.H. Wigg Asteraceae Herb


47 Tephrosia purpurea (Linn.) Pers. Papilionaceae Herb




51 Withania somnifera L. (Dunal) Solanaceae Shrub

52 Zizyphus mauritiana Lamk. Rhamnaceae Shrub



55 Polygonum plabegem R. Br. Polygonaceae Herb

56 Eclipta prostata L. Asteraceae Herb


region of Pakistan

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Figure 4. Rarefaction curve showing cumulative number of species recorded as a function of sampling effort

Figure 5. Mean values/10 m2 for ecological indices of invaded vs control plots in different sites.

(S = Number of species; N = Abundance; D = Species richness; H’ = Shannon index of

diversity; J’ = Species evenness; λ = Simpson index of dominance)


region of Pakistan

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Comparisons of ecological indices showed significant difference across districts and

invasion status. Parthenium invasion exhibited variable impact across five districts by

reducing species number per plot (S) and abundance (N) up to a maximum of 40% in

Attock. Control plots harbored on average 6.033 ± 1.75 (mean ± SD, n = 30) species.

This was greater than that observed in the invaded plots (5.133 ± 1.83) and the

difference was statistically significant (t = 2.09, df = 29, p = 0.045). A total of 181 and

154 individuals were recorded in control and invaded plots respectively. Similarly,

abundance in control and invaded plots differed by 3.7 ± 3.83 (mean±SD, n = 30) and

the difference was significant (t = 4.34, df = 29, p < 0.0001). Control plots also

exhibited higher values of species richness by a difference of 0.15 ± 0.51, species

evenness by 0.019 ± 0.02; Shannon index of diversity by 0.2 ± 0.34 and Simpson index

of dominance by 0.22 ± 0.35 (Table 3).

Table 3. Analysis of variance (ANOVA) of invasion impacts and district on diversity indices

of local plant community

Ecological index

SUMMARY ANOVA Mean (±SD)

District (D) Invasion

status (IS)

DˣIS

interaction Control (30) Invaded (30)

No. of species (S)/10 m2 ** ** *** 6.033±1.75 5.133±1.83

Abundance (N)/10 m2 ** *** ** 14.4±3.81 10.70±3.86

Species richness (R) ** NS *** 1.87±0.49 1.62±0.53

Species evenness (Jꞌ) NS ** NS 0.028±0.039 0.009±0.006

Shannon index of diversity (Hꞌ) ** ** *** 1.73±0.29 1.53±0.406

Simpson index of dominance (λ) ** ** *** 1.72±0.29 1.50±0.42

***P ≤ 0.001; **P ≤ 0.02; *P ≤ 0.05; NS (not significant) P > 0.05

For individual district, native flora differed significantly in species density (S),

abundance per plot (N), species evenness (Jꞌ) and Simpson index of dominance (λ) but

not in overall species richness (R) and Shannon index of diversity (Hꞌ). Parthenium

invasion had significant impacts on all ecological indices except species richness (R) at

site 1 (Attock). For site 2 (Chakwal), only abundance was affected significantly. For site

3 (Islamabad) invasion impacts were not significant only on native species abundance.

Species evenness (Jꞌ) was non-significant for site 4 (Jhelum) while for site 5

(Rawalpindi) the only index significantly affected by Parthenium invasion was species

evenness (Jꞌ) (Table 4).

Table 4. Student’s t-test for significance of differences between control and invaded plots at

different sites

Site Number of

species (S)

Abundance

(N)

Species

richness (D)

Species

evenness (Jꞌ)

Shannon index

of diversity (Hꞌ)

Simpson index

of dominance (λ)

Attock * ** NS * ** *

Chakwal NS * NS NS NS NS

Rawalpindi ** NS ** ** ** **

Jhelum *** ** ** NS ** **

Islamabad NS NS NS *** NS NS

***P ≤ 0.001; **P ≤ 0.02; *P ≤ 0.05; NS (not significant) P > 0.05


region of Pakistan

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between species composition of invaded and control plots in all sites with global R

values of 0.876 (p = 0.002), 0.519 (p = 0.002), 0.598 (p = 0.002), 0.907 (p = 0.002) and

0.759 (p = 0.002) for Attock, Chakwal, Islamabad, Jhelum and Rawalpindi, respectively

(Fig. 6). The greatest dissimilarity between invaded and control plots was noticed by

Jhelum.


P < 0.002


P < 0.002


P < 0.002


P < 0.002


P<0.002

Islamabad




Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.09

Attock




Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.11

Chakwal




Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.13

Jehlum




Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.09


Transform: Log(X+1)



Invaded

Cont rol_1

Cont rol_2

Cont rol_3

Cont rol_4

Cont rol_5

Cont rol_6

I nvaded_1

I nvaded_2

I nvaded_3

I nvaded_4

I nvaded_5

I nvaded_6

2D Stress: 0.06


Transform: Log(X+1)



Invaded

control

control

control

control

control

control

I nvaded

I nvaded

I nvaded

I nvaded

I nvaded

I nvaded

2D Stress: 0.09


P < 0.002


P < 0.002

a: Attock

b: Islamabad

c: Chakwal d: Jhelum

e: Rawalpindi f: Pooled data for

Pothwar Region

Figure 6. Multidimensional scaling (MDS) ordination and analyses of similarity (ANOSIM)

results of invasion status data for Pothwar region, Pakistan (open symbols are for control, uninvaded plots, and closed symbols are for invaded plots)

Similarity percentage (SIMPER) analysis of data suggested those species

contributing most to average dissimilarity between control and invaded groups. This

analysis also computed average contribution of species causing dissimilarity. Few top

species separating invaded plots from non-invaded plots (control) for analysis are

enlisted in Table 5. Tephrosia purpurea and Lathyrus aphaca were found in control


region of Pakistan

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plots while they were not observed in invaded plots, whereas a grass species (Poa

annua), and broad leaf species like Solanum, Ricinus and Taraxacum were

conspicuously displaced in Parthenium invaded plots.

Table 5. SIPMER analysis of Parthenium invaded and control sites in Pothwar region,

Pakistan. Data have been pooled prior to analyses across districts


Average abundance

Species Control Invaded Av. Diss. Diss./SD Contribution

(%)

Poa annua L. 2.94 0.00 2.38 8.06 3.95

Lathyrus aphaca L. 0.00 2.69 2.18 5.82 3.63

Solanum miniatum L. 2.47 0.00 2.00 7.85 3.32

Ricinus communis L. 2.19 0.00 1.77 2.05 2.95

Convolvulus arvensis L. 1.80 1.79 1.49 1.32 2.48

Taraxacum officinale (L.) Weber ex F.H. Wigg 1.77 0.00 1.40 2.07 2.32

Rosa damascena Mill. 1.82 0.18 1.38 1.41 2.29


Fumaria indica (Hausskn.) Pugsley 2.35 1.15 1.31 1.55 2.18

Tephrosia purpurea (L.) Pers. 0.00 1.63 1.29 1.36 2.15


Circium arvense L. 1.63 0.00 1.25 1.69 2.08

Saxifragara sndrosacea L. 1.73 0.54 1.24 1.51 2.06


Tinospora cordifolia Miers ex Hook. f. 1.52 0.00 1.23 1.90 2.04

Solanum nigrum L. 1.87 0.86 1.21 1.92 2.02

Saxifragra androsacea L. 1.51 0.00 1.19 1.34 1.97



Eclipta prostata L. 1.95 1.02 1.12 1.25 1.86

Values are average abundance ranking (1-rare; 2-common; 3-very common; >4-dominant)

Discussion

Parthenium weed exerts significant impact on natural communities by displacement

of native species and hence exert discrepancy in natural ecosystems. This discrepancy

results in formation of its large monocultures. In present study, comparisons of

ecological indices across invaded and control plots indicated significant differences in

the study area. These findings are in-line with other studies on this alien invasive weed,

in which indicated strong effects of the invader on ecosystem properties, e.g., in grazing

and wastelands of district Attock (Riaz and Javaid, 2011), district Hafizabad, (Riaz and

Javaid, 2010) and Islamabad, Pakistan (Shabbir and Bajwa, 2007).

The results show modifications in vegetation composition of invaded and control

plots. Analysis of variance among invaded and control plots showed significant

decrease in ecological indices across site and invasion status. These results are

consistent with other studies on invasive species indicating their negative effects on bio-

diversity and ecosystem properties (Manchester, 2000; McNeely, 2001; Grice, 2006;


region of Pakistan

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Borokini et al., 2011; Jeschke et al., 2014; Panetta and Gooden, 2017). In our study,

despite the negative effect of P. hysterophorus on species composition, species

evenness of control and invaded plots was not significantly different. That is

contradiction to above-mentioned studies; however, a few studies have shown that

invasive species pose little or no effect on species diversity (e.g., Martin, 1999; Hejda

and Pysek, 2006; Timsina et al., 2011). It is reported elsewhere that Parthenium

invasion enriches compositional diversity but may result in extinction of native species

(Nigatu and Sharma, 2013).

Wide environmental adaptability, drought tolerance, photo and thermo-insensitivity,

high seed production and short life cycle (being an annual), small and light seeds

capable of long distance travel via water, wind, birds, animals and vehicles, longevity of

seeds in soil seed banks, strong competition and allelopathy contribute to the

invasiveness of Parthenium weed (Shabbir and Bajwa, 2006; Hassan et al., 2012; Khan

et al., 2014). Allelopathy especially plays important role in the invasion of this weed.

The major allelopathic compounds found in P. hysterophorus are, gentisic, o-coumaric,

p-coumaric, ferulic, vallinic, caffeic, salicylic acid, p-hydroxybenzoic and trans-

cinammic acids and sesquiterpene lactone etc. (Borah et al., 2016). These

allelochemicals are supposed to reduce native seed germination, allowing the weed to

pre-empt space and establish monocultures.

Parthenium invasion exhibited variable impacts in five sites by reducing species

number per plot (S), abundance (N), species richness (R), species evenness (Jꞌ),

Simpson index of dominance (λ) and Shannon index of diversity (Hꞌ). The trend of

decrease in ecological indices in invaded plots is similar to invasion studies on P.

hysterophorus from Australia, Ethiopia, Nigeria, Tanzania and India (Grice, 2006;

Kilewa and Rashid, 2012; Seta et al., 2013; Borokini et al., 2011; Abdulkerim-Ute and

Legesse, 2016). The most effected site by Parthenium invasion was Jhelum followed by

Attock, Rawalpindi, Chakwal and Islamabad. The lowest invasion impacts in Islamabad

compared to other sites are probably because of management practices in the area being

its importance as metropolitan region of Pakistan while highest dissimilarity in invaded

and control plots in Jhelum is possibly due to the saline soil of the area (Anonymous,

2017).


between species assemblages of invaded and control plots. The difference was

significant for all of five study sites but the greatest dissimilarity between invaded and

control plots were noticed by Jhelum. It was reported that the Parthenium plant has a

higher survival rate in higher level of soil salinity (Upadhyay et al., 2013), a condition

inimical to establishment of many native plant species. Consequently the higher

invasion impacts in Jhelum are possibly due to its saline soil (Anonymous, 2017).

SIMPER analysis showed dominance of fewer species in invaded plots than in

control. These were Tephrosia purpurea and Lathyrus aphaca. Possible reason for their

presence in invaded plot may be due to their aggressive nature as weeds in their own

right. Perhaps higher contribution values of Fabaceae weeds is due to competition

potential with Parthenium as suggested by Belachew and Tessema (2015); Gnanavel

(2013). There is an urgent need of appropriate control measures including the use of

proven biological control agents for this weed in Pakistan as done elsewhere around the

globe/world, e.g., Australia and South Africa (Kaur et al., 2014; Strathie et al., 2011).


region of Pakistan

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Conclusion

The increased occurrence of invasion around the world poses a major threat to

indigenous diversity. Plant invasions in novel areas deplete species diversity, alter

indigenous community composition, affect ecosystem processes and thus cause huge

ecological and economic imbalance. Invasive species studied in the past revealed that

the effects of invasion are complex and can permanently alter the function and structure

of communities, cause local annihilations and changes in ecosystem processes. Invasion

by alien plant species affect the composition and dynamics of species on a wide scale

and have great impact on ecosystem functions. The decrease in ecological diversity

indices in invaded over control sites in present study indicated that plant communities

become less productive due to Parthenium invasion, hence it is a threat to plant

diversity of invaded areas. There is an urgent need of appropriate control measures

including the use of proven biological control agents for this weed in Pakistan.

Acknowledgements. Pakistan Meteorological Department University Road Karachi, Pakistan is

acknowledged for providing climate data of Pothwar region, Pakistan.

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