Studies on Ecology of Invasive Species in Forest and Grassland Ecosystems of Northern Western Ghats

STUDIES ON ECOLOGY OF INVASIVE SPECIES IN FOREST AND GRASSLAND ECOSYSTEMS OF NORTHERN WESTERN GHATS

A Thesis submitted to Goa University for the Award of the Degree of DOCTOR OF PHILOSOPHY in BOTANY

By MR. BHARAT BAJIRAO PATIL

Research Guide Prof. M. K. JANARTHANAM

Goa University Taleigao Goa 2014 STATEMENT

I state that the present thesis “Studies on ecology of invasive species in forest and grassland ecosystems of Northern Western Ghats” is my original contribution and the same has not been submitted on any occasion for any other degree or diploma of this

University or other University/Institute. To the best of my knowledge, the present study is the first comprehensive work of its kind from the area mentioned. The literature related to the problem investigated has been cited. Due acknowledgements have been made wherever facilities and suggestions have been availed of.

Place:- Goa University (Bharat Bajirao Patil) Date:- Candidate

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CERTIFICATE

This is to certify that the thesis entitled “Studies on ecology of invasive species in forest and grassland ecosystems of Northern Western Ghats”, submitted by Mr.

Bharat Bajirao Patil for the award of the degree of Doctor of Philosophy in Botany, is based on his original and independent work carried out by him during the period of study, under my supervision.

The thesis or any part thereof has not been previously submitted for any other degree or diploma in any University or Institute.

Place:- Goa University (M. K. Janarthanam) Date:- Research Guide

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DEDICATED TO

Founder Padmabhushan Late Devchandji Shah

& President Late Shriman Kiranbhai Shah Janata Shikshan Mandals, Devchand

College, Arjunnagar.

Respected Parents Shri. Bajirao & Sou. Shevanta Patil.

Beloved Teachers, Family & Friends.

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ACKNOWLEDGEMENT

I wish to express my sincere thanks and deep gratitude to my research guide Prof. M. K. Janarthanam, Goa University for his valuable and unfailing guidance during my entire work. I thank him for helping me & make my dream a reality. I am thankful to Prof. P.V. Desai, Prof. G. N. Nayak, Prof. Saroj Bhosale, Former Deans, Faculty of Life Sciences & Environment, for their encouragement throughout the work. I am thankful to Prof. B. F. Rodrigues, Head, Department of Botany. I am also thankful to Prof. D. J. Bhat, Prof. P. K. Sharma former Heads, Department of Botany, Goa University for providing me with all facilities for the smooth functioning of my work. My sincere thanks to Prof. S. Krishnan VC’s nominee for his valuable suggestions. I am grateful to teaching faculty Prof. V. U. Kerkar & Dr. Nandkumar Kamat, for their support during this study. My sincere thanks to Prof. S.R. Yadav, Prof. G.B. Dixit, Dr. Nilesh Malpure Department of Botany Shivaji University Kolhapur, for their guidance in identification of plant specimens & providing herbarium reference at SUKH and Dr. P.G. Diwakar BSI Pune for his help during my work. My special thanks to Forest & Wild Life Divisions of Goa, Maharashtra & Karnataka for allowing me in forest areas for ecological work and also to Shri. Prakash N. Kamu. My sincere thanks to Agricultural Department Soil Survey & Soil Testing Laboratory, Kolhapur. Officer N.S. Parit, staff & also Shri. Ramchandra Patil. Soil Testing Laboratory, Assistant Director of Agriculture (F.T.) Ela-Old-Goa, Officers Vishwas Shirodkar, Agnel Viegas, K. Kudtarkar, R. A. Balkrishna & Staff for their help in testing soil samples. I am very grateful to President Shriman Ashishbhai Shah, Vice Presidents Pratibhabhabhi & Truptibhabhi Shah, Former Principal Shri. K.S. Daddi, Principal Dr. P. M. Herekar, Teachers Shri. K.S. Raibagi, Dr. R.J. Bhalerao, Dr. K.S. Hardikar, Dr. D.D. Patil & my colleagues from the department Dr. P.D. Shirgave, Dr. V.S. Khude, Dr. L.P. Lanka, Mrs. K.D. Patil-Birnale, Dr. A.S. Donar, Sagar Mane & all my rest of the colleagues, each & every aspect of Devchand College, Arjunnagar for their encouragement.

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My appreciation to my fellow research friends Dr. Arun Chandore, Dr. Shankar Shendage, Dr. Vinod Gosavi, Dr. Manoj Lekhak, Dr. Emilia Mascarenhas, Dr. Harshala Gad, Dr. Jyosna Dessai, Dr. Ashish & Dr. Pratibha Prabhugaonkar, Dr. M. Baskaran, Dr. Andrew E Willis, Dr. James D’ Souza, Sidhesh Naik, Mr. Ravikiran Pagare, Anup Deshpande, Dr. Mayur Nandikar, Kiran Gaude, Dr. Shivraj Bhoite, Dr. Samit Kadam, Dr. Jyoti Vaingankar, Dr. Shilpa Bhonsle, Dr. Cassie Rodrigues, Dr. Seema Dessai, Mrs. Wendy Xavier, Mrs. Pallavi Randive, Mrs. Abhipsa Mohpatra, Mrs. Prabha Pillai for all the help. My special thanks to non-teaching staff Mr. Gajanan Tari, Miss. Anna D’souza, Mrs. Gracy Godinho, Mrs. Nutan Chari, Mr. Vithal Naik, Mr.Vasudev Gaonkar Mr. Dilip Agapurkar, Mr. Chudu Gawas, Late Mr. Suresh Kondekar Department of Botany, Goa University, for their kind co-operation and help. I am very grateful to Shri. Maruti & Sou. Lilawati Nandwdekar, Shri. Satappa & Sou. Sangeeta Patil, Shri. Sampat & Sou. Sunanda Dhamankar. I am deeply grateful to Principal Shri. Vasantrao & Sou. Suvarna Nagare, brothers Shri. Kiran & Shri. Mohan Patil, Sou. Manisha, Sou. Sunita, Sou. Savita Patil, Nisha, Eesha & Shivtej Patil for their constant inspiration & support.

Bharat Bajirao Patil

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LIST OF FIGURES

Fig. Name Page No. No. 1 Study area consisting of North and South Goa districts of Goa, 25 Kolhapur and Sindhudurg districts of Maharashtra and Belgaum and Uttara Kannada districts of Karnataka 2 A-B: Chromolaena odorata; C-D: Lantana camara; E-F: 26 Parthenium hysterophorus 3 Distribution of Chromolaena odorata based on actual observations 55 in the study area 4 Positive correlation between pH of the soil and Chromolaena 56 odorata patch size 5 Slight negative correlation between canopy gap and and 56 Chromolaena odorata patch size 6 Bimodal distribution of Chromolaena odorata along the altitudinal 57 gradient with its absence in mid altitudes of Northern Western Ghats 7 Scatter diagram showing slight decrease in number of herbs with 57 increasing area of occupancy by C. odorata 8 Herbs and saplings exclusively present in sites infested / non- 58 infested with C. odorata. 9 Herbs and saplings predominantly present in sites not-infested with 59 C. odorata. 10 Herbs and saplings predominantly present in sites infested with C. 60 odorata. 11 Maps of potential distribution of Chromolaena odorata based in 61 MAXENT model. 12 Graphs of potential distribution of Chromolaena odorata based in 62 MAXENT model. 13 Distribution of Lantana camara based on actual observations in the 81 study area 14 Positive correlation between pH of the soil and Lantana camara 82 patch size 15 Positive correlation between slope and Lantana camara patch size 82 16 Positive correlation between altitude and Lantana camara patch 83 size 17 Negative correlation between canopy gap and Lantana camara 83 patch size 18 Distribution of Lantana camara along the altitudinal gradient 84 19 Area occupied by Lantana camara and the number of herbs 84 (species) 20 Herbs and saplings exclusively present in sites infested / non- 85 infested with Lantana camara 21 Herbs and saplings predominantly present in sites not infested 86 with Lantana camara

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22 Herbs and saplings predominantly present in sites infested with L. 87 camara 23 Maps of potential distribution of Lantana camara based on 88 MAXENT model. Predictions are based on 29 samples in study area. 24 Maps of potential distribution of Lantana camara based on 89 MAXENT model. Predictions are based on 39 samples in study area. 25 Maps of potential distribution of Lantana camara based on 90 MAXENT model. Predictions are based on 48 samples in study area. 26 Maps of potential distribution of Lantana camara based on 91 MAXENT model. Predictions are based on 48 samples in study area and 5 samples far away from study area. 27 Grpahs of potential distribution of Lantana camara based in 92 MAXENT model. 28 Distribution of Parthenium hysterophorus based on actual 104 observations in the study area 29 Dendrogram showing segregation of quadrats based on soil 105 parameters. Infected and non-infected sites with the same number are spatially very close to each other. 30 Herbs present in sites infested with C. odorata and adjacent non- 106 infested sites 31 Maps of potential distribution of Parthenium hysterophorus based 107 on MAXENT model. Predictions are based on 10 samples in study area. 32 Maps of potential distribution of Parthenium hysterophorus based 108 on MAXENT model. Predictions are based on 14 samples in study area. 33 Maps of potential distribution of Parthenium hysterophorus based 109 on MAXENT model. Predictions are based on 16 samples in study area. 34 Maps of potential distribution of Parthenium hysterophorus based 110 on MAXENT model. Predictions are based on 16 samples in study area and nine samples from far away areas.

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LIST OF TABLES

Table Name Page No. No. 1 Layers used in modeling the probable areas prone for invasion 23 2 Locality details of Chromolaena odorata sites in study area 34-39 3 Soil and habitat parameters of sites infested (I) with 40 Chromolaena odorata and adjacent non-infested (NI) sites 4 Correlation of soil, habitat parameters and area occupied by C. 40 odorata in infested sites 5 Correlation of soil, habitat parameters and area occupied by C. 41 odorata both in infested (I) and adjacent non-infested (NI) sites 6 Species that are exclusive to areas not infested with 42-45 Chromolaena odorata 7 Species that are exclusive to areas infested with Chromolaena 46-48 odorata. 8 Species that are common to areas infested with Chromolaena 49-53 odorata and outside. 9 Correlation of vegetation characters in relation to area occupied 54 by C. odorata 10 WorldClim environment variables that define the potential 54 distribution of obnoxious weed C. odorata in Northern Western Ghats. 11 Locality details of Lantana camara in study area 68-70 12 Soil and habitat parameters of sites infested (I) with Lantana 71 camara and adjacent non-infested (NI) sites 13 Correlation of soil, habitat parameters and area occupied by L. 72 camara both in infested (I) and adjacent non-infested (NI) sites 14 Species that are exclusive to areas not infested with Lantana 73-74 camara. 15 Species that are exclusive to areas infested with Lantana camara 75-76 16 Species that are common to areas infested with Lantana camara 77-78 and the adjacent areas 17 Correlation of vegetation characters in relation to area occupied 79 by Lantana camara 18 Correlation of vegetation characters including undergrowth in 79 relation to area occupied by Lantana camara 19 WorldClim environment variables that define the potential 80 distribution of obnoxious weed L. camara in Northern Western Ghats 20 WorldClim environment variables that define the potential 80 distribution of obnoxious weed L. camara in Northern Western Ghats with additional distribution data incorporated from outside study area. 21 Locality details of Parthenium hysterophorus in the study area 97-98 22 Soil and habitat parameters of sites infested (I) with Parthenium 99 hysterophorus and adjacent non-infested (NI) sites

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23 Correlation of soil and habitat parameters of Parthenium 100 hysterophorus in infested sites 24 Species that are exclusive to areas not infested with Parthenium 101 hysterophorus 25 Species that are exclusive to areas infested with Parthenium 102 hysterophorus 26 Species that are common to areas infested with Parthenium and 102 adjacent non-infested areas 27 WorldClim environment variables that define the potential 103 distribution of obnoxious weed P. hysterophorus in Northern Western Ghats 28 WorldClim environment variables that define the potential 103 distribution of obnoxious weed P. hysterophorus in Northern Western Ghats; additional distribution data incorporated from outside study area

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CONTENTS

SR.NO. CHAPTERS PAGE NO.

I INTRODUCTION 1-3

II REVIEW OF LITERATURE 4-14

III MATERIALS AND METHODS 15-26

IV RESULTS AND DISCUSSION Chromolaena odorata 27-62

Lantana camara 63-92

Parthenium hysterophorus 93-110

V CONCLUSION 111

VI SUMMARY 112-114

VII BIBLIOGRAPHY 115-139

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I. INTRODUCTION

India, one of the 12 megacentres of origin of cultivated plants is also one of the richest centers of biodiversity (Kohli et al., 2004) as two of the biodiversity hotspots

(now four) of total 25 (now 34) are present here (Myers et al., 2000). However, this diversity is under great pressure due to anthropogenic activities such as deforestation and habitat destruction. In addition, Invasive species are considered among the greatest threats to native biological diversity and functioning of natural ecosystems. Bioinvasion is homogenizing the worlds flora and fauna (McKinney and Lockwood, 1999; Baiser and Lockwood, 2011), altering the biogeochemical cycles (Strayer et al., 2006) and is recognized as a primary cause of global biodiversity loss (Czech and Krausman, 1997;

Wilcove et al., 1998) and species extinction (di Castri, 1989). Millennium Ecosystem

Assessment (2005) considers climate change along with invasive species as the most pervasive forms of ecosystem disturbance.

Ecologists have tried to define ‘Invasive species’ in various ways, but commonly they all refer to ‘non-indigenous species that have colonized natural areas’

(Burke and Grime, 1996). Invasive species have intrigued ecologists for long and in recent years, the establishment and spread of such species in areas where they do not occur naturally are receiving increasing importance from scientists, policy makers and the public. Numerous studies demonstrated the dramatic effect of invaders on recipient ecosystem (Mack et al., 2000).

Exotic species become successful invaders because of the invasibility of the recipient ecosystem to invasion. Invasibility has been defined as a measure of an ecosystem susceptibility to colonization by exotic species (Smallwood, 1994). It has been suggested that greater phenotypic plasticity confers greater invasiveness (Williams et al., 1995; Schweitzer & Larson, 1999). 1

In Indian context, there are several weeds that are known for invading different habitats and among them Chromolaena odorata (L.) R.M.King & H.Rob. (=Eupatorium odoratum L.), Lantana camara L. and Parthenium hysterophorus L. occupy top slots as the worst invaders. There are several studies on various aspects of these species carried throughout the globe and the results always varied with the ecosystem, geographical location of the study site and climatic conditions of the study area. Hence, region specific studies are a must if one has to understand their dynamics and subsequently their management.

Chromolaena odorata, belonging to Asteraceae, is considered one of the world’s worst invasive species (Joshi, 2006). This cryptic heliophyte originated from Central

America invaded the understorey of many tropical forest ecosystems throughout the world. It occurs predominantly in opened up forest with increased light intensity. It invades new areas by generative reproduction (wind dispersal of pappus bearing achenes) and subsequently clonal propagation through underground corms enhances further expansion of populations (Joshi, 2006). Population dynamics studies carried out on this species in successional environments following slash and burn agriculture by

Kushwaha et al. (1981) indicated that long cycles of ‘Jhum’ considerably brings down the infestation.

Lantana camara (Verbenaceae), an invasive plant species is considered one among the world’s 10 worst weeds (Sharma et al., 2005). It occurs in diverse habitats and on a variety of soil types and its spread is encouraged by animal activities and by human disturbances. The biological attributes contributing to the success of Lantana as an invader species include fitness homeostasis, phenotypic plasticity, dispersal benefits, widespread geographic range, vegetative reproduction, fire tolerance, better competitive ability compared to native flora, and allelopathy. Sharma and Raghubanshi (2011)

showed of proliferation of Lantana with high growth rate, results in changes in herbaceous species composition and soil properties. Also, results showed strong negative relationship between herbaceous cover and Lantana.

Parthenium hysterophorus (Asteraceae) is perhaps the most noxious weed of all, especially of urban India. It is a quick invader and has invaded almost all accessible land. It reduces biodiversity and affects landscape and soil quality. It is known to cause fodder and food scarcity. Parthenium is hazardous to human health as well as to live stock (Kohli et al., 2006). Batish et al. (2012) studied geographical distribution, ecology, invasiveness of Parthenium hysterophorus.

In the present study, an attempt has been made to understand the ecology of these three invasive species, viz. Chromolaena odorata (L.) R.M. King and H. Rob.

(Asteraceae) [Siam weed; Ranmari], Lantana camara L. (Verbenaceae) [Ghaneri] and

Parthenium hysterophorus L. (Asteraceae) [Congress grass].

OBJECTIVES

1. Identify the areas infested with the invasive weeds such as Lantana camara,

Chromolaena odorata and Parthenium hysterophorus.

2. To survey and identify the parameters in forest and grassland ecosystems that

distinguishes invaded ecosystems from intact ones.

3. To analyze the impact of invasive species on the biodiversity including recruitment

of native flora.

4. To build a model based on these parameters to predict invasions of these exotic

weeds in future.

5. To validate these models in the ground conditions.

II. REVIEW OF LITERATURE

The weeds such as Chromolaena odorata, Lantana camara, Parthenium hysterophorus have always figured on the top of the list of worst weeds (Kohli et al., 2012).

Chromolaena odorata:

There are several views with regard to introduction of C. odorata into Asia including India (Muniappan, 1996; Gautier, 1992; Voigt, 1845; Muniappan et al.,

2005). Generally considered to have been introduced in Calcutta during 1845 (Voigt,

1845; Muniappan & Bambam, 1999), later it spread in different directions including southern states.

In Nepal did not grow in areas which get less than 1200 mm rainfall (Norbu,

2004) and had been limited in its spread by cold northern areas and dry western areas.

One of the major features which made it a successful weed was its wide ecological amplitude (Joshi, 2001; Barik & Adhikari, 2012), though temperature and precipitation were generally considered as prime factors (Yadav & Tripathi, 1981). Its ability to produce enormous seeds (McFadyen, 1988) is considered as one of the factors for its spread. It is usually seen growing in areas which are disturbed and where the forest is degraded (Joshi, 2006). Kushwaha et al. (1981) established the favourable role of light and high light intensity for its success whereas Norbu (2004) links its success to medium light intensity. A survey in Malnad (Western Ghats) region of Karnataka

(Ambika, 2002) has shown that C. odorata grows well in open areas of humid forest regions, with good light intensity and soil moisture. Joshi et al. (2006) established high correlation between light intensity and cover of C. odorata and high negative correlation between forest canopy density and cover of the weed. Ye et al. (2004) have shown that in China C. odorata successfully invade inspite of poor genetic diversity; its

adaptability was considered more important for its invasion. Baker (1965) listed 12 characteristics of an ideal weed of which C. odorata possesses 10 characters (te Beest et al., 2010).

Infestation by C. odorata lead to several negative effects. In Karnataka, the forests of Shimoga and South Kanara districts have been infested by C. odorata affected the growth of soft wood and teak plantation (Ambika & Jayachandra, 1980). The habitats of animals including Rhino were destroyed by the invasive species, including

C. ododrata, etc. in grasslands of Assam (Lahkar et al. 2011). In South Africa Nile crocodile habitats were threatened by C. odorata (Leslie & Spolita, 2001). Other ill effects reported include suppression of growth of young pine and Eucalyptus trees

(Matthews, 2004; Matthews & Brand, 2004), promotion of wildland fires (Moore, 2004) and cause of skin problems and asthma to human (Koutika & Rainey, 2010), and threat to ecotourism (MacDonald, 1983; Goodall & Erasmus, 1996; Matthews & Brand, 2004;

Koutika & Rainey, 2010).

While several workers considered C. odorata a threat for biodiversity conservation (Goodall & Erasmus 1996; Mathews & Brand, 2004), Koutika and Rainey

(2010) considered it a fallow plant with several positive aspects. The soil studies of C. odorata infested sites by quadrat method were carried out by Amiolemen et al. (2012) in Nigeria and reported maximum leaf litter, soil bacteria, fungi and microorganisms which decompose the organic matter, that resulted resulted in the formation of nutrient rich soil in the sites infested by C. odorata. On the other hand there are publications

(Bamikole et al. 2004) advocating its potential as feed for Rabbits.

While enormous literature established the effect of soil, canopy gap and other environmental factors for its establishment, Foxcroft and Martin (2002) noted that the potential distribution of the weed depends on the area which is not water stressed. de

Rouw (1991) in the long term studies carried out in south-west Cote d’Ivoire concluded that forest cover delayed the invasion of C. odorata and found it a hurdle for agriculture and forestry, wherever it has established.

Its allelopathic effect on various cultivated plants is well known (Onwugbuta-

Enyi, 2001; Suwal et al., 2010). Its chemical constituents of aerial parts include both volatile and non-volatile compounds of at least 22 different compounds in major quantities (Pisutthanan et al., 2006). Though this chemical composition may be responsible for its success, mechanism of action is not known. Zachariades and Goodall

(2002) analyzed negative impact of it in South Africa through (i) physical smothering and allelopathy, and (ii) susceptibility to fire due to its higher dry biomass.

Soil parameters seem to be influencing the establishment of C. odorata and in return the latter proved to be changing the soil parameters. It is also noted that C. odorata grows in different types of soils having different characteristics and also in the soil pH ranging between 4 – 8 in Zamboanga Peninsula, Philippines. It was also concluded that C. odorata prefers open sunny habitats than the dense shady forests

(Ambika, 2007; Codilla & Metillo. 2011).

Euston-Brown et al. (2007) found out that very high temperature and prolonged dry conditions limit the spread of C. odorata in sudan Savanna. It is also reported that high salinity soils in the mangroves swamp soil also limits the growth and spread of C. odorata (Castel, 2012). During their survey in parts of Karnataka, Doddamani et al.,

(1998) concluded that saline soil along the coast limits the growth of C. odorata.

Soil seed bank of C. odorata seem to be influenced by sun and shade wherein higher seed bank percentage of ten month after production is higher in shade

(Witkowski & Wilson, 2001). They have also shown that C. odorata has shown positive growth and higher seed production for the first ten years and declined greatly after 15

years. C. odorata was observed to be occupying different land use areas, altitudes and range of pH (Roder et al., 1995). A comparative study of fellow fields, wherein three different plant species including C. odorata and two legume species, have shown that C. odorata is better than one of the legume when it comes to accumulation of particulate organic matter, nitrogen, phosphorous etc. (Koutika et al., 2004).

Studies have shown that high timber stand density (TSD) had negative effect on weed biomass though concentration of nutrients in the leaves was high showing that nutrient uptake is not affected by high TSD (Norgrove et al., 2000). C. odorata has deleterious effect on tree seedlings and their growth in the forest ecosystem. In an experiment conducted in degraded forests of Ghana, it is shown that removal of the weed increased the height, number of leaves and species composition of the tree seedling (Honu & Dang, 2000). A study wherein soil pre-cultured with species other than C. odorata increased the performance of the weed (te Beest et al., 2009). Studies on gap dynamics in the forests of Kerala have shown that C. odorata occupied the areas created by selection felling (Chandrashekara & Ramakrishnan, 1994). The same study has shown that there is a gradual increase in soil nutrient with gap age whereas it was higher in one year old selection felled gap than in intact site due to sudden increase in leaf litter. Germination response of seeds was better in warm fluctuating temperature and in light (Chauhan & Johnson, 2008).

It grew better in high light conditions though other conditions did not significantly affect its performance (Zhang et al., 2009). Studies in the laboratory as well as in the field established that 60-70% RH, 90-100% soil moisture, 3000-3500 lux light intensity is very favorable for seedling growth of C. odorata (Ambika, 2002).

Singh et al. (1995) have shown that the field dominated by weeds including C. odorata

is poor in N, P, OC, exchangeable CA, Mg and K as compared to bamboo and natural forest.

Lantana camara:

Lantana camara L. has been introduced as an ornamental in 1800s (Aravind, et al., 2010; Priyanka & Joshi, 2013). Throughout the world L. camara is reported in more than 60 countries (Parsons et al. 2001; Goncalves et al. 2014). Three weeds viz. L. camara, P. hysterophorus and Ageratum conyzoides are considered as most problematic weeds in North-Western Himalayas (Kohli et al., 2004; Dogra et al., 2009a,b; Dobhal et al., 2011). Among them L. camara figures as one of the top 10 worst weeds in the world

(Sharma et al., 2005; Dobhal et al., 2011). In India it has invaded throughout the forests, pastures and wastelands (Dobhal et al., 2010, 2011). There is depletion of native trees in

Lantana invaded sites (Sharma & Raghubanshi 2007; Dobhal et al., 2011). It is concluded that in Himalayas, the Lantana invaded sites negatively affected species density, frequency, abundance and basal area, and biomass of other species (Bhatt et al.,

1994; Dobhal et al., 2011).

Lantana camara figures among the world’s obnoxious weed (Holm et al., 1991), and considered one of the weeds of national significance in Australia (Clark et al.,

2004). It has been attributed with several characters such as disturbance of succession, decrease in biodiversity, decrease in species richness in the areas wherever it is occupied and considered potential threat for about 60 species of conservation concern

(Sankaran, 2007).

Invasive species affect ecosystems including its processes and species diversity

(Vitousek et al., 1996; Wilcover et al., 1998; Gurevitch & Padilla, 2004). In addition, recruitment is slowed down by interfering with germination, growth and pollination of native species (Vranjic et al., 2000; Gorchov & Trisel, 2003; Yurkonis et al., 2005;

Bjerkens et al., 2007). Ultimately the composition of native species changed in invaded areas (Grice, 2004; Jackson 2005; Mason & French, 2007; Gerber et al., 2008).

The studies carried out by Dobhal et al. (2011) in Pauri Garhwal region of

Uttarakhand, reported that there was 28.4% reduction in species richness in the sites infested by L. camara than the non-infested sites. However, the species such as

Eragrostis tenella and Pyrus pashia were growing luxuriously in the infested areas throughout the year.

Experimental studies in Australian forest have shown that removal of shrub layer increased the biomass of L. camara along with its germination and survival.

Removal of over story increased its biomass as it is coupled with increase in light.

(Duggin & Gentle, 1998). Availability of N in the soil beneath Lantana significantly increased due to increased litter input from the weed and its decomposition (Sharma &

Raghubanshi, 2009).

Soil properties such as pH, available P, available N increased in the soil beneath

Lantana as compared to the edge and outside the individuals and accordingly biomass of Italian Ryegrass, Mungbean and Radish have shown marked increase in the soil beneath Lantana (Fan et al. 2010). Gap size and its positive relation to population size, plant size, reproduction in Lantana camara was noticed by Totland et al. (2005) in

Uganda.

Lantana camara also reduced the bird diversity, richness and abundance with high infestation of Lantana in Reserve Forest of South India (Arvind et al., 2010).

Wilson et al. (2013) predicted negative effect on Elephant by Lantana camara in

Mudumalai Tiger Reserve of South India. Gooden et al. (2009) in their study in New

South Wales, Australia found that L. camara invaded areas not show reduction in species richness below 75% Lantana cover. But above 75% threshold level it shows

reduction in a number of species. Studies in dry forests of Mudumalai by Ramaswami and Sukumar (2011), reported that species such as Phyllanthus emblica, Kydia calycina are less in L. camara affected sites and predicted alteration of species composition in the future.

A study by Dobhal et al. (2010) in Pauri Garhwal region of Uttarakhand in

Himalayas reported reduced level of Potassium, Calcium, Magnesium, Nitrogen and

Phosphorus in the soil invaded by L. camara as compared to adjacent non-invaded soil.

Osunkoya and Perrett (2011) noticed improved soil fertility in the sites invaded with L. camara including pH, Carbon and total N. Water seems to be an important factor and found to be abundant close to streams in Mudumalai forest of South India (Ramaswami

& Sukumar, 2014).

From the crude aqueous extract of L. camara 13 phenolic compounds are extracted (Singh et al.1989; Jain et al.; 1989; Hussain et al. 2011) hinting to its possible allelopathic effects. In infested sites of L. camara higher Organic Carbon and pH have been recorded, though N was generally low and P and K did not differ in infested and non-infested sites (Osunkoya & Perrett, 2011) in four sites of west Brishane, SE

Australia. Similar results were reported earlier by Fan et al. (2010) from China and by

Sharma and Raghubanshi (2009) from India. In contrast, Rawat et al. (1994) reported higher N in sites infested with Lantana than habitats of other native species. The leaves of invasive species reportedly contain higher percentage of N (Vitousek et al., 1987;

Nagel & Griffin, 2001; Ashton et al. 2005) and rapid decomposition of leaves add more

N to the soil as compared to the native species. Sharma and Raghubanshi (2009) concluded that the percentage of N is higher beneath the Lantana canopy and lower beneath the forest canopy from the beginning up to the end of the experiment.

Parthenium hysterophorus:

Several reviews have appeared decades ago on the morphology, taxonomy, distribution and various other aspects of Parthenium hysterophorus (Nath

1988; Kohli & Rani 1994; Evans 1997). Its negative impact on various aspects including human (Towers & Rao 1992), Sheep (Rajkumar et al. 1988), seed germination (Srivastava et al. 1985), biodiversity (Oudhia, 1998), general toxicity

(Narasimhan et al., 1977), cattle industry (Chippendale & Panetta 1994), blood chemistry (Ahmad et al. 1988) were brought to the fore. Negative effect of Cassia uniflora on the germination and establishment of P. hysterophorus is well discussed

(Joshi, 1991). Pollen allelopathy on the stigmas of adjacent plants was shown as a new way of this weed controlling the native flora (Kanchan & Jayachandra 1980).

Parthenium is a threat to natural as well as agro ecosystems in more than 30 countries

(Adkins & Shabbir, 2014). Seed bank of grasses in the sites infested with Parthenium in

South-East Ethiopia has reduced from 81.7% to 6.1% along with decrease in evenness index of the vegetation (Ayele et al., 2013).

Ever since it’s ill effects are documented, lots of efforts are on to control this obnoxious weed (Adkins et al., 1996). There are also efforts to control this weed using biological agents including plants such as Imperata cylindrica (Anjum et al. 2005) and

Cassia sericea (Syamasundar & Mahadevappa, 1986). It is also noticed that certain fodder species such as Setaria incrussata, Panicum maxicum and Cenchrus ciliaris suppress the growth of this weed (Khan et al. 2013, 2014).

It’s fast spreading nature has been well documented in various parts of the world

(Haseler, 1976; Khalid, 2000; Javaid & Anjum, 2005; Javaid & Riaz, 2007; Riaz &

Javaid 2010; Anonymous, 2010). Since allelopathy of the weed is considered a major driver, differential allelopathic effects of P. hysterophorus in Australia (Adkins &

Sowerby, 1996), on Eragrostis tef in Ethiopia (Tefera, 2002) and on Wheat cultivars by

Khan et al. (2012) have been studied. Leaf and stem extract of P. hysterophorous was reduced seed germination with 91.6% inhibition and incorporating biomass into the soil significantly reduced germination and growth of maize (Devi et al., 2014). A study on

European crop fields has shown P. hysterophorus as the second frequent weed in 54% of the fields studied (Tamado & Milberg, 2000)

Parthenium hysterophorus has been reported from India for the first time by

William Roxburgh (1814) in ‘Hortus Bengalensis’; it accidentally came to Royal

Botanic Garden, Howrah (now known as Acharya J. C. Bose Indian Botanic Garden) in

1810 (Paul, 2010). In Maharashtra, though P. hysterophorus was reported for the first time in 1951 (Raghubanshi et al. 2005), it’s fast spread in cultivated fields and grasslands had already been reported in 1960s (Vartak, 1968; Jayachandra, 1971).

Raghubanshi et al (2005) reported it’s ability to colonize the areas with poor ground cover and exposed soil and established inverse relationship between existing plant cover and native weed density. Timsina et al. (2011) recorded change in species composition of plants and soil properties in areas invaded with P. hysterophorus. This change is basically towards increase in species composition as well as few chemical parameters.

Vegetation and species association has been a focus of study. Etana et al. (2011) in their study in Awash National Park of Ethiopia, found that 40% of the herbs in the sites belong to Poaceae and Fabaceae. They also found that species are more diverse in non-infested sites, and a dominant species, viz. Tetrapogon tenellus, in non-infested site had been relegated to second spot in weed infested site. P. hysterophorus invaded sites have been shown to harbour less species richness as compared to non invaded sites

(Kumari et al., 2014). The latest and comprehensive review on biological control of

Parthenium (Sushilkumar, 2009) deals with various aspects of its interaction with other species and vegetation.

Raizada et al. (2008) and Sharma and Raghubanshi (2009) reported increase in nitrogen content of soil invaded by P. hysterophorus and L. camara. Alteration of other soil conditions varies with invading species (Srivastava et al., 2013). Singh et al. (2011) demonstrated the improved growth rate of P. hysterophorus with the application of nitrogen in experimental conditions.

MAXENT-

Several species distribution models have been used and their performance in predicting the distribution of various species including weeds have been compared. Some studies concluded that MAXENT is better modeler as compared to GARP (Wang et al., 2007;

Bo et al., 2009). Yang et al. (2013) while predicting Justicia adhatoda concluded that the MAXENT model was highly accurate in its prediction. Adhikari et al. (2012) used it to model the potential distribution of Ilex khasiana, an endemic tree in Meghalaya.

Padalia et al. (2014) modeled an invasive species Hyptis suvaeolanes using both

MAXENT and GARP and again proved that MAXENT is better than GARP in prediction.

In Tanzania 58 occurrence records of P. hysterophorus were used and the

MAXENT model was run by using 15 bioclimatic variables to study the habitat distribution. The MAXENT model contributed up to 68.6% by two variables, viz. precipitation of wettest month and mean temperature of coldest quarter. The study also predicted the suitable environmental habitats for C. odorata in Tanzania (Kija et al.,

2013).

Ray and Ray (2014) studied L. camara in relation to the distribution in India and micro satellites and by integrating this data with niche modeling have shown that 13

temperature and precipitation played an important role in the distribution and concluded that there is an emergence of ecotype in the form of two genetic clusters.

Taylor & Kumar (2012) predicted distribution of L camara using CLIMEX under current and future climate scenarios wherein the resulting map depicted the distribution of Lantana along the Western Ghats while predicting across the globe.

Using bioclimatic models, Vardien et al. (2012) predicted considerable expansion of L. camara in South Africa considering its wide ecological amplitude.

Kannan et al. (2013) used GIS platform to answer several question regarding

Lantana and its spread in the Western Ghat region and interestingly suitability in northern and southern Western Ghat was shown to be more as compared to central

Western Ghats and predicted less towards the western side of the Western Ghats.

The model suggested that due to the suitable habitats in India, L. camara spread very fast as compared to Australia and Africa in which there are less suitable habitats

(Bhagwat et al. 2012; Goncalves et al. 2014).

III. MATERIALS AND METHODS

Study area

The study area consists of parts of Northern-Western Ghats covering the state of

Goa, north-Western Karnataka and south-Western Maharashtra. The area extends from

Deccan plateau in the east to the west coast through the mountains of the Western

Ghats. The sampling area includes districts of North Goa and South Goa of Goa,

Sindhudurg and Kolhapur districts of Maharashtra, and Belgaum and Uttara Kannada districts of Karnataka (Fig. 1). The sampling area lies between 73.7o - 74.48o E longitude and 14.9o - 16.32o N latitude. Major vegetation types include moist deciduous forests, semi-evergreen forests, evergreen forests, grasslands and savanna type vegetation on lateritic plateaus.

Field work

Field trips have been carried out from June 2007 to May 2011 to record the distribution of populations of three weeds, viz. Chromolaena odorata, Lantana camara and Parthenium hysterophorus (Fig. 2). GARMIN GPS 12 handheld receiver has been used to record the co-ordinates. Totally 104 occurrences were recorded for C. odorata,

48 for L. camara and 19 for P. hysterophorus. Additional distributional data points for

L. camara (5 points) and P. hysterophorus (6 points) were collected from outside the study area and incorporated for analysis to test the effectiveness of local vs. additional data from outside study area in predicting potential distribution of weeds.

Quadrats

1. A reconnaissance survey to identify forest patches infested with invasive species has been carried out in forests and grasslands along the northern Western Ghats restricting to Goa and adjacent districts of Maharashtra and Karnataka.

2. Quadrats of 10 × 10 m have been laid using nylon ropes and measuring tape. While laying quadrats in infested areas the weed patches have been included within them; quadrat of similar size in non-infested area very close to each infested patch served as control.

3. Habit and phenology of weed is noted.

4. Each quadrat pair was given a number, e.g. I-1, NI-1, I-2, NI-2, I-3, NI-3, I-4, NI-4,

I-5, NI-5 etc., wherein ‘I’ refers to quadrat infested with weed and ‘NI’ refers to non- infested quadrat.

5. The vegetation type such as evergreen, semi-evergreen, dry-deciduous, moist- deciduous, grassland etc. is noted.

6. The details such as name of the district, taluka and place/village and location details such as plateau, base of the hill, survey no., range, compartment, beat no. etc. have been recorded from local sources and the Forest Department.

7. The angle of the slope was measured by keeping the Clinometer on 2 m long wooden ruler that was laid along the slope.

8. Aspect of the slope of the quadrat was found using magnetic compass.

9. The topographic details such as plain, hill slope, hill top, rocky area, lateritic rocks and lateritic plateau is noted in the field.

10. The type of soil and other details such as rocky, sandy, moist, marshy, etc. noted in the field by observing the color of the soil (such as red, brown, black, off white).

11. The date of study of each quadrat is noted.

12. The altitude, latitude and longitude of each quadrat is recorded using GPS receiver as mentioned earlier.

13. Additional details such as visual signs of disturbance such as lopping, grazing, fire, grasses chopped for cattle feed in grasslands, burnt tree, dried bamboo bush, grazing by

Indian Gaur, Barking and Spotted Deer, cattle, etc. were noted in the field.

14. Size of each weed patch in quadrat has been obtained by measuring its length and breadth from the middle of the patch and applying the following formula:

Weed Area = (length × breadth) * π / 4

If the whole quadrat is covered with the weed then the area of the quadrat (100 sq. m) is considered as the size of the weed patch.

15. The photographs of canopy against the sky were taken with Nikon Coolpix P5100 camera using wide angle and the canopy gap percentage was calculated using grids and applying following formula:

Canopy Gap% = (X/Total No. of squares on a graph paper on the photo area) * 100

Where X = Total no. of squares of the grid on photograph - Total no. of squares with canopy.

16. Herbarium sheets of plant specimens encountered during the study have been made and identified by referring to the Flora of Maharashtra state (Singh, et al., 2001; Singh

& Karthikeyan, 2000; Sharma et al., 1996), Flora of Goa, Diu, Daman, Dadra &

Nagarhaveli by Rao (vol. I: 1985; vol. II: 1986) and Forest Flora of Goa by Naithani et al. (1997). Identifications were confirmed by comparing them with herbarium specimens deposited at Goa University herbarium and Shivaji University herbarium

(SUK).

17. The GBH of each tree in quadrat was measured using measuring tape.

18. All the data collected on individual quadrats have been entered into excel spreadsheet for further analysis.

19. Plants forming undergrowth have been identified and their area of occupancy was calculated in %. Only for tree and shrub seedlings numbers have been counted.

Soil Samples

The soil samples from four corners and the centre of each quadrat were collected by digging up to 30 cm below ground level, mixed together and one 1 kg of it was packed in polythene bags. Later on it was sun dried and stored till analysis. The soil samples were collected from 120 infested and 120 adjacent non-Infested quadrats. The soil was analyzed for pH, E.C. (m/mhos), O.C. (%), N (%), P (g/ha) and K (kg/ha).

I. Soil pH:

Taken 20 g air dried soil in a beaker and added 50 mL distilled water. Stirred at regular intervals for one hour. In the mean time turned the pH meter (Equip-Tronics Mumbai

Micro Controller pH meter. Model EQ- 621) on, allowed it to warm up, and standardized the glass electrode using standard buffer solutions of pH 4.0, 7.0 & 9.2.

Measured the pH of the sample suspension, stirring the suspension well just before introducing the electrodes. The electrodes are rinsed after each determination and carefully blotted them dry with filter paper before the next determination. The glass electrode was standardized after every ten determinations.

II. Soil Electrical Conductivity- E.C. (m/mhos):

Ions, like metals, allow the electric current to pass through them. Hence, the electrical conductivity (EC) of the soil-water system rises with increasing content of soluble salts in the soil. Thus, the measurement of EC will give the concentration of soluble salts in the soil at any particular temperature.

0.01N Potassium chloride solution: Dried a small quantity of AR grade potassium chloride at 60°C for 2 h. Weighed 0.7456 g of it and dissolved in freshly prepared

distilled water and made to one litre. This solution gives an electrical conductivity of

1411.8 × 10-3, i.e., 1.41 dS m-1 at 25°C.

Procedure: Weighed 20 g of soil sample in a 100 mL beaker. Added 40 mL of distilled water and kept for 1 h on a shaker. Allowed to stand until clear supernatant liquid is obtained. Calibrated the conductivity bridge with the help of standard KCI solution and determined the cell constant. Determined the conductivity of the supernatant liquid with the help of conductivity bridge.

III. Soil Organic Carbon- O.C. (%):

Walkley and Black method (Walkley and Black, 1934):

Reagents: 1N Potassium dichromate: Dissolved 49.04 g of AR grade K2Cr2O7 in about

500 mL of distilled water and made the volume to one liter. Conc. sulphuric acid. 0.5N

Ferrous ammonium sulphate: Dissolved 196 g of ferrous ammonium sulphate in distilled water, added 20 mL of conc. H2SO4 and made volume to one litre.

Diphenylamine indicator: Dissolved 0.5 g of the dye in a mixture of 20 mL of distilled water and 100 mL of conc. H2SO4. Orthophosphoric acid (85%) or sodium floride.

Procedure: Weighed 1g of soil sample into 500 mL dry (corning/ borosil) Conical flask.

Added 10 mL of 1N K2Cr2O7 and 10 mL of conc. H2SO4. Swirled a little and kept on an asbestos sheet for 30 minutes. Added slowly 200 mL of distilled water and 10 mL of orthophosphoric acid. Added 1 mL of diphenylamine indicator. Taken 0.5N ferrous ammonium sulphate solution in 50 mL burette. Titrated the contents until green colour starts appearing. If the titrate value is ≤ 6, repeated taking 0.2 to 0.5 g of soil sample.

IV. Soil Nitrogen- N (%):

By estimating organic carbon, the amount of Nitrogen released can be known. For instance, if soil has 1% Organic Carbon, the total nitrogen content of the soil will be approximately 0.1% since the C:N ratio is generally 10:1.

V. Soil Phosphorus- P (Kg/ha):

Bray’s P-1 (Bray and Kurtz, 1945)

Instruments: Mechanical shaker and spectrophotometer

Reagents: Bray’s P-1 extractant: Dissolved 1.110 g of AR grade ammonium fluoride in one litre of 0.025N HCl. 1.5% Dickman and Bray’s reagent: Dissolved 15 g of AR grade ammonium molybdate in 300 mL of warm water, cool and added exact 350 mL of

10N HCl. Made the volume to 1 litre. 40% SnCl2 stock solution: Weighed 10 g of stannous chloride in a 100 mL glass beaker. Added 25 mL of conc. HCl and dissolved by heating. Cooled and stored in an amber colored bottle in dark, after adding a small piece of Zn metal (AR grade) to prevent oxidation. From this, prepared a dilute SnCl2 solution (0.5 mL diluted to 66 mL) immediately before use. 100 mg P L-1 stock solution:

Weighed 0.439 g of AR grade KH2PO4 dried in oven at 60°C for 1 h in a one litre beaker, added about 500 mL of distilled water and dissolved. Added 25 mL of approx.

-1 7N H2SO4 and made the volume to one litre. 2 mg P L solution: Diluted a suitable volume of 100 mg P L-1 solution by 50 times to get 2 mg P L-1 solution.

Procedure: Weighed 5 g of soil sample in a 150 mL conical flask. Added 50 mL of

Bray’s P-1 extractant and shaked for 5 min. Filtered through Whatman no.1 filter paper quickly so as to collect the filtrate within 10 minutes. Transferred to 5 mL volumetric flask. Added 5 mL of ammonium molybdate solution, shaked a little and diluted to about 22 mL. Added 1 mL of diluted SnCl2 (0.5 mL diluted to 66 mL), mixed by shaking a little, and made up the volume. Run a blank without soil under identical conditions. Measured the intensity of the blue color developed using 660 nm wave length using spectrophotometer (Shimadzu UV-1800).

VI. Soil Potassium- K (Kg/ha):

Ammonium acetate method of K determination (Hanway and Heidel, 1952) has been used.

Instruments: Flame photometer, mechanical shaker and pH meter.

Reagents: 1N Ammonium acetate: Dissolved 77.08 g of ammonium acetate in about 500 mL of distilled water and made the volume to one litre. Adjusted the pH to 7.0 with glacial acetic acid or ammonia solution which was prepared by taking 800 mL of distilled water and adding to it 57 mL of glacial acetic acid and 68 mL of ammonia solution (sp. gr. 0.91) followed by dilution to 1 litre and adjusting pH at 7.0 after cooling. Standard K solution: Prepared 1000 mg L-1 K solution by dissolving 1.908 of dried (in oven) KCl salt per litre solution. Diluted suitable volumes of this solution to get 100 mL of working standards containing 10, 15, 20, 25 30 and 40 mg K L-1. The working standards should be prepared in the medium of extraction.

Procedure: Weighed 5 g of soil sample in 100 mL conical flask. Added 25 mL of the neutral 1N ammonium acetate solution and shaked for 5 minutes. Filtered through

Whatman no.1 filter paper, measured K concentration in the filtrate using flame photometer. Preparation of standard curve for K: flame photometer readings recorded for each of the working standards of K adjusting blank to zero. Standard curve drawn by plotting the readings against K concentrations.

Calculations:

Available K (kg ha-1) = C *11.2

Where, C stands for the concentration of potassium in the sample obtained on X- axis, against the reading.

Predictive Models- MAXENT:

MAXENT software (version 3.3.3e) was used for modeling. Environmental variables at

30 arc-seconds resolution (~1 km) were downloaded from WorldClim website

(http://www.worldclim.org). One altitude layer, 12 monthly precipitation layers and the

19 bioclim variables were used in the study (Table 1). Coordinates collected from the field using GPS for the presence of weeds was used as sample file. MAXENT modeler was run using random seed with random test percentage of 30. Five replicates with replicated run type as Boot-strap and 500 as maximum iterations were used for the model. Output format was set as cumulative and output file type saved as .asc. Percent contribution and permutation importance of each variable and map generated for minimum prediction (to avoid over estimation) were considered for interpretation.

Potential areas predicted by the model were checked for their presence to validate the prediction. DIVA-GIS was used for further analysis and representation by importing the resulting files of MAXENT analysis.

Validation in the field: MAXENT has been used taking to in consideration the present only data for predicting the areas suitable for invasion of Chromolaena odorata.

Initially 63 samples were collected and modeler was run as per the parameters/conditions described as above. Areas with maximum prediction have been surveyed and additional 21 localities were recorded for their presence from the predicted areas; data have been incorporated to rerun the model. Further 20 confirmed locations from refined predictions have been incorporated and the model was rerun. For

Lantana camara, the modeler was run with 29, 39 and 48 samples in each step as per the validation procedure described above. The same procedure was repeated for

Parthenium hysterophorus with 10, 16 and 19 samples. Additional data points from outside the study area have been incorporated in case of L. camara (5) and P.

hysterophorus (6) to check and interpret the effectiveness of local data in predicting the distribution outside the study area.

Table 1: Layers used in modeling the probable areas prone for invasion.

S. No. Bioclim Variables 1 BIO1 [Annual Mean Temperature] 2 BIO2 [Mean Diurnal Range (Mean of monthly (max temp – min temp))] 3 BIO3 [Isothermality (BIO2/BIO7) (* 100)] 4 BIO4 [Temperature Seasonality (standard deviation *100)] 5 BIO5 [Max Temperature of Warmest Month] 6 BIO6 [Min Temperature of Coldest Month] 7 BIO7 [Temperature Annual Range (NIO5-BIO6)] 8 BIO8 [Mean Temperature of Wettest Quarter] 9 BIO9 [Mean Temperature of Driest Quarter] 10 BIO10 [Mean Temperature of Warmest Quarter] 11 BIO11 [Mean Temperature of Coldest Quarter] 12 BIO12 [Annual Precipitation] 13 BIO13 [Precipitation of Wettest Month] 14 BIO14 [Precipitation of Driest Month] 15 BIO15 [Precipitation Seasonality (Coefficient of Variation)] 16 BIO16 [Precipitation of Wettest Quarter] 17 BIO17 [Precipitation of Driest Quarter] 18 BIO18 [Precipitation of Warmest Quarter] 19 BIO19 [Precipitation of Coldest Quarter] 20 PREC1 [Precipitation in the month of January] 21 PREC2 [Precipitation in the month of February] 22 PREC3 [Precipitation in the month of March] 23 PREC4 [Precipitation in the month of April] 24 PREC5 [Precipitation in the month of May] 25 PREC6 [Precipitation in the month of June] 26 PREC7 [Precipitation in the month of July] 27 PREC8 [Precipitation in the month of August] 28 PREC9 [Precipitation in the month of September] 29 PREC10 [Precipitation in the month of October] 30 PREC11 [Precipitation in the month of November] 31 PREC12 [Precipitation in the month of December] 32 ALT [Altitude of the place]

Statistical analysis and graphs:

Correlation studies and descriptive statistics have been carried out using Gnumeric spreadsheet ver. 1.12.15, a open source free software. Graphs have been constructed using Microsoft Excel.

Fig. 1: Study area consisting of North and South Goa districts of Goa, Kolhapur and Sindhudurg districts of Maharashtra and Belgaum and Uttara Kannada districts of Karnataka

A B

C D

E F

Fig.2: A-B: Chromolaena odorata; C-D: Lantana camara; E-F: Parthenium hysterophorus

IV. RESULTS AND DISCUSSION (i) Chromolaena odorata Area of Infestation: Through intensive field trips throughout the study area, 104 localities have been found to be infested with Chromoleana odorata. For this purpose areas with stray individuals (which are not of infested proportion) have not been considered. The details of localities including co-ordinates are given in Table 2. The co-ordinates plotted on map are shown in Fig. 3. The distribution pattern shows that the infestation is seen on the western side of the Western Ghats which receives very high rainfall. Vegetation is mostly moist-deciduous forests, followed by semi-evergreen and evergreen forests. It’s occurrence is mostly seen in disturbed areas especially roadsides, periphery of the forests or within the forest wherein the trees are either cut or burnt. As it is associated with forest it is locally known as “Ranmari” meaning “Killer of the forest”. Out of 104 sites, 44 sites are located in Goa, 31 in Karnataka and 29 sites in Maharashtra. These areas receive >3000 mm rainfall. It’s presence in these high rainfall receiving areas is as expected, as temperature and precipitation were considered prime factors for its presence (Yadav & Tripathi, 1981) and <1200 rainfall mm is a limiting factor (Norbu,

2004). The availability of moisture in these areas is probably important for its growth and establishment as water stress affects its distribution (Foxcroft & Martin, 2002).

Several soil parameters such as pH, EC, OC, N, P and K have been studied from

84 quadrats infested with the weed and additional 84 quadrats in adjacent non-infested sites to know whether any of these parameters influence spread of the weed. In addition slope, altitude and canopy gap parameters to correlate with weed area have been measured (Table 3). Descriptive statistics of these parameters indicate a higher mean values of phosphorus (P) and canopy gap in infested sites as compared to adjacent non- infested sites. Correlation of soil, habitat parameters and area occupied by C. odorata in

27 infested sites indicate a positive correlation between pH and weed area (0.559) (Table 4;

Fig. 4) as well as slope and pH (0.394). Comparable trends are seen in L. camara infested sites wherein increase in pH is seen beneath the weed (Fan et al., 2010;

Osunkoya & Perrett, 2011). As C. odorata can grow in soil with pH ranging from 4-8

(Codilla & Metillo, 2011), it doesn’t appear that pH decides the distribution, though there must optimum pH for its growth. Negative correlation between canopy gap and weed area (-0.293) (Table 4; Fig. 5) as well as altitude and weed area (-0.159) (Table 4), though weak appear to be important. Several reports exist with regard to light requirement and canopy cover for the establishment of C. cordata in forest. Open sunny habitats than the dense shady forests (Ambika, 2007; Codilla & Metillo, 2011), forest cover delaying the invasion (de Rouw, 1991) and open sunny areas with good moisture

(Ambika, 2002) are some of them. High correlation between light intensity and cover of

C. odorata and high negative correlation between forest canopy density and cover of the weed are highlights of these findings. However, from the present study it appears that negative correlation between the canopy gap and weed patch size is due to evaporation of moisture from the soil in the larger canopy gaps, which limits its growth. However, further studies are required to conclusively prove this.

As the indicate positive or negative correlation between the parameters as indicated in the previous paragraph, does not rule out similar correlation in non-infested sites, combined data of infested as well as non-infested sites have been analyzed for their correlation which is provided in Table 5. Similar correlation is seen between the weed area and pH of adjacent non-infested sites indicating that pH increase may not necessarily due to the presence of the weed. However, it can be concluded that higher pH (but <6.5) is conducive to the establishment and spread of weed. Kourtev et al.

(1998) demonstrated that pH of soils invaded with two exotic species (Berberis

28 thunbergii and Microstegium vinnimeum) is significantly higher than the non-infested areas. In certain cases such as Acacia dealbata in mediterranean ecosystem it is found that soil has become acidic with increased nitrification and carbon combined with lower richness of under story plant communities Lazzaro et al. (2014).

There is a negative correlation between canopy gap and pH and canopy gap and weed area both within the infested sites and between infested and non-infested sites.

Though canopy gap has been a positive parameter for the spread of weed (Scherrer,

1998), the reduction of pH with the opening up of canopy might be negating the effect as there is positive correlation between pH and weed areas (Fig. 4) and negative correlation between pH and canopy gap. There is a weak negative correlation between altitude and weed area which also indicates that at lower altitudes the weed spread is due to the conducive conditions in the form of high precipitation and humidity, a fact iterated by Yadav and Tripathi (1981), Ambika (2002) and Foxcroft & Martin (2002).

It is also seen that in mid altitude of study area (200-500 m), generally the infestation is not seen or is very minimal (Fig. 6). Most of the infested sites are present at lower altitudes (<200 m MSL) and the remaining sites above 500 m, thus showing a bimodal distribution (Fig. 6). This may be due to the mid altitude falling in Goa region wherein forests are mostly protected and form core part of series of Wild Life

Sanctuaries or National Parks. The localities above 600 m generally come under

Maharashtra or Karnataka wherein the disturbance to certain extent is seen. It appears that forest degradation (Joshi, 2006) is an important factor for its establishment and forest cover a hurdle (de Rouw, 1991).

Effect of spread of C. odorata on native flora is well discussed in literature (de

Rouw, 1991; Honu & Dang, 2000; Onwugbuta-Enyi, 2001; Suwal et al., 2010) and references therein. Data from 84 quadrats have been gathered to analyze the impact of

29 weed on the vegetation as well as possible effects of components of vegetation on the weed establishment. Study shows that 23 species of trees, 33 shrubs, 58 herbs and 12 climbers/creepers and seedlings/saplings of 21 species totaling 147 plant species are present exclusively in quadrats not infested with C. odorata (Table 6). However, their role in not allowing the establishment of C. odorata is not ascertained and needs further studies. Similarly there are 133 plant species that were recorded exclusively from the quadrats infested with C. odorata (Table 7). These include 16 trees, 30 shrubs, 15 seedlings, 4 climbers, 1 creeper and 67 herbs, though this does not provide any clue whether these species favor the establishment and growth of C. odorata or they are co- invaders. In the third category, there are 192 plant species that include 74 herbs, 53 trees, 33 shrubs, 11 climbers and seedlings and saplings of 21 species that are common to both infested and non-infested sites (Table 8).

Correlation of vegetation characters in relation to area occupied by C. odorata is shown in Table 9. There is a strong negative correlation between weed area and canopy gap as well as weed area and number of herbs and saplings, indicating that number of herbs and saplings go down with increasing weed patch size in each quadrat, thus severely affecting the recruitment. It may be due to its allelopathic effect (Onwugbuta-

Enyi, 2001; Suwal, 2010) or in addition through physical smothering (Zachariades &

Goodall, 2002). However, the canopy gap negatively correlating with weed area shows that canopy gap is an important parameter for the weed establishment, on the other hand the canopy gap also brings down the pH as shown earlier. As the pH is positively correlated with weed area, these three form delicate balance in nature. Another observation is that there is a positive but weak correlation between weed area and total number of trees as well as total number of tree species, if combined with negative correlation between canopy gap and weed area indicates that some amount of shade is

30 preferred by weed to establish well. Its success in establishing is linked to medium light intensity (Norbu, 2004) which is provided by intermediate disturbance through small canopy gaps. There is a positive correlation (0.757) between number of tree species and total number of trees on one side (Table 9), and number of species of herbs and saplings on the other side, suggesting that with the opening up of the canopy number of herbs and saplings come up, and in the process an opportunity also arises for C. odorata to establish. A weak negative correlation is seen between weed area and number of species

(herbs and saplings) (Fig. 7) though cause and effect is not established.

As mere presence or absence of species would not give true picture of their interaction with weed, their occupancy has been worked out. At least seven species, viz.

Pseudanthistiria heteroclite, Smithia setulosa, Aristida mutabilis, Fimbristylis nagpurensis, Paspalum paspaloides, Cymbopogon martini and Tricholepis glaberrima occupy on an average 20% are more in the non-infested quadrats (Fig. 8). Of these former two species occupy more than 50% of the area independently, however whether their presence is the reason for non-establishment of C. odorata could not be ascertained. Among the quadrats infested with C. odorata, at least each of nine species show an average occupancy of 20% or more. Passiflora foetida shows 100% occupancy wherever it is present. It is observed that exclusive species, either in infested or non- infested quadrats are mostly grasses. Herbs and saplings predominantly present in sites not infested with C. odorata are shown in Fig. 9. It shows that most of the herbs that occupy 20% or more are grasses and rest of them belongs to other larger families such as Fabaceae, Cyperaceae and Asteraceae.

Species such as Iseilema holei, Arundinella tuberculata, Aristida redacta,

Heteropogon contortus, Arundinella nepalensis show marginal increase in their occupancy in infested sites (Fig. 10), though it is not clear whether it is the presence of

31 weed or other soil and vegetation characteristics that are responsible for this marginal increase. Herbs and saplings predominantly present in sites infested with C. odorata based on their occupancy is shown in (Fig. 10). As in non-infested sites most of the herb layer consists of grasses.

The soil and other parameters as shown and discussed above are not providing any conclusive evidence to build a model to predict invasion of the weed. As it’s absence also depends on the lack of opportunities for propagules to spread around inspite of presence of conducive environment for the establishment and growth of the weed. Hence Ecological Niche Models (ENM) come handy to build the models based on climatic data and environmental data. MAXENT has been used taking into consideration the present only data for predicting the areas suitable for invasion and its superior ability (Wang et al., 2007; Bo et al., 2009; Yang et al., 2013; Patalia et al.,

2014). Initially 63 samples were collected and modelar was run as per the parameters/conditions as described under methodology. The results (Fig. 11 a,d,g) show the prediction at three levels. The frequency of predicted occurrence is shown in Fig.12

(a-c), wherein each pixel is equivalent to 1 sq. km. The average prediction shows 3767 sq. km. with probability value 53-70% and 525 sq. km. area with probability of 70-88%

(Fig. 12d) and at maximum level no area is predicted above 80% probability whereas

3781 sq. km. area with 60-80% probability. Field trips to the areas were conducted to validate the data in the field. An additional 21 localities were identified with infestation from the predicted areas and incorporated to rerun the model. The refined result is shown in Fig. 11 &12 (b,e,h). The predicted areas improved with 1525 sq. km. between

81-100% probability. Under maximum conditions 5871 sq. km. area has been predicted for 60-80% probability and 6511 sq. km. between 80-100% probability (Fig. 12h). The process was repeated to validate the prediction in field conditions by searching their

32 presence in the areas predicted with 60% or more probability. An additional 20 samples collected from such predictions have been incorporated and model was rerun wherein, it has slightly brought down the area predicted at average level but at maximum level prediction substantially increased the area at 80-100% probability. It indicated that more number of samples refine the prediction. However the validation was found extremely difficult as most of these predicted areas were seen along the mid-altitudes which are basically Wild Life Sanctuaries wherein the disturbance is almost nil.

Environmental variables that contribute and defined its distribution are precipitation (or lack of it) in the month of January, July and May (Prec. 1, Prec.7 and

Prec.5) which is shown in Table 10. In January, it is basically in the form of dew, in

July the heaviest rains and May it is dry hot month. Precipitation in January (Prec.1) and temperature seasonality (BIO4) provide permutation importance.

Potential distribution of C. odorata obtained through MAXENT modeler (Fig.

11) shows that majority of the sites are on the western side of the Western Ghats, which happen to be in Goa at low altitude which are disturbed moist-deciduous forests.

Table 2: Locality details of Chromolaena odorata sites in study area S.No. Q. No. Locality District State Longitude Latitude Stawanidhi Ghat To 1 1 Ramlingeshwar B KA 74° 23.904’ 16° 21.294’ Forest ,Tal-Chikodi. Stawanidhi Ghat To 2 2 Ramlingeshwar B KA 74° 23.899’ 16° 21.171’ Forest ,Tal-Chikodi. Stawanidhi Ghat To 3 3 Ramlingeshwar B KA 74° 23.822’ 16° 21.097’ Forest ,Tal-Chikodi. Stawanidhi Ghat To 4 5 Ramlingeshwar B KA 74° 22.812’ 16° 20.988’ Forest ,Tal-Chikodi. Kuditek To Rasai Hills 5 7 K MH 74° 19.099’ 16° 21.191’ Forest, Tal-Kagal. Matgaon Forest- Adwan, 6 16 K MH 73° 59.432’ 16° 06.619’ Tal-Bhudargad. Matgaon Forest- Adwan, 7 17 K MH 73° 59.559’ 16° 06.379’ Tal-Bhudargad. Matgaon Forest- Adwan, 8 18 K MH 73° 59.586’ 16° 06.363’ Tal-Bhudargad. Mhasve to Bhatiwde 9 22 K MH 74° 06.834’ 16° 19.574’ Forest Tal-Bhudargad Mhasve to Bhatiwde 10 23 K MH 74° 06.935’ 16° 19.643’ Forest Tal-Bhudargad Mhasve to Bhatiwde 11 24 K MH 74° 06.914’ 16° 19.612’ Forest Tal-Bhudargad Majgaon Forest, 12 31 S MH 74° 50.399’ 15° 52.110’ Tal-Sawantwadi. Mangaon Forest, 13 32 S MH 73° 47.119’ 15° 57.655’ Tal- Sawantwadi. Kattawadi Forest, 14 33 S MH 73° 46.329’ 15°56.357’ Tal- Sawantwadi. Kattawadi Forest, 15 34 S MH 73° 47.181’ 15° 57.550’ Tal- Sawantwadi. Nemle Forest, 16 35 S MH 73° 45.841’ 15° 55.020’ Tal- Sawantwadi. Foujdarwadi Forest, 17 36 S MH 73° 45.915’ 15° 54.998’ Tal- Sawantwadi. Foujdarwadi Forest, 18 37 S MH 73° 45.940’ 15° 54.925’ Tal- Sawantwadi. Insuli Ghat Forest, 19 38 S MH 73° 50.389’ 15° 52.104’ Tal- Sawantwadi. Bondla Wildlife 20 39 Sanctuary, Tower site, NG GA 74° 06.101’ 15° 26.699’ Tal-Ponda Bondla Wildlife 21 40 NG GA 74° 06.635’ 15° 26.214’ Sanctuary, Tower site,

Tal-Ponda Bondla WLS, Tower site, 22 41 NG GA 74° 06.715’ 15° 26.180’ Tal-Ponda Bondla Wildlife 23 42 Sanctuary, Tower site, NG GA 74° 06.741’ 15° 26.188’ Tal-Ponda Paikul Village Forest 24 43 NG GA 74° 06.817’ 15° 25.967’ Tal-Ponda Paikul Village Forest 25 44 NG GA 74° 06.814’ 15° 25.990’ Tal-Ponda Paikul Village Forest 26 45 NG GA 74° 06.730’ 15° 26.069’ Tal-Ponda Bhagwan Mahaveer WLS- Molem,Awarde 27 46 SG GA 74° 14.837’ 15° 23.986’ Mal Forest, Tal- Sanguem. Bhagwan Mahaveer WLS- Molem,Awarde SG 28 47 GA 74° 14.888’ 15° 24.040’ Mal Forest, Tal- Sanguem. Bhagwan Mahaveer WLS- Molem,Awarde 29 48 SG GA 74° 14.490’ 15° 23.717’ Mal Forest, Tal- Sanguem. Bhagwan Mahaveer 15° 26.109’ 30 49 WLS-Molem, Bolkone SG GA 74° 11.653’

Forest, Tal-Sanguem. Bhagwan Mahaveer 31 50 WLS-Molem, Bolkone SG GA 74° 12.462’ 15° 26.347’ Forest, Tal-Sanguem. Bhagwan Mahaveer 32 51 WLS-Molem, Bolkone SG GA 74° 11.768’ 15° 26.168’ Forest, Tal-Sanguem. Cotigaon WLS, Near 33 52 Tulsimal Watch Tower. SG GA 74° 11.144’ 14° 57.256’ Tal-Cancona. Cotigaon WLS, 34 53 Near Endre Village, SG GA 74° 11.129’ 14° 57.995’ Tal-Cancona Cotigaon WLS, 35 54 Near Endre Village, SG GA 74° 09.137’ 14° 57.746’ Tal-Cancona Cotigaon WLS, 36 55 Near Bella Lake, SG GA 74° 09.140’ 14° 57.190’ Tal-Cancona. Cotigaon WLS, 37 56 Near Bella Lake, SG GA 74° 09.200’ 14° 56.982’ Tal-Cancona. Cotigaon WLS, 38 57 SG GA 74° 09.183’ 14° 57.112’ Near Bella Lake,

Tal-Cancona. Nagargali Forest, Diggeli 39 58 B KA 74° 38.013’ 15° 23.097’ Bit. Tal-Khanapur Nagargali Forest, 40 59 Chakramaddi. B KA 74° 36.804’ 15° 23.744’ Tal-Khanapur Nagargali Forest, 41 60 Chakramaddi. B KA 74° 36.948’ 15° 23.969’ Tal-Khanapur Nagargali Forest, 42 61 Wajra Poha Water B KA 74° 36.558’ 15° 24.376’ Falls. Tal-Khanapur Nagargali Forest, 43 62 Wajra Poha Water B KA 74° 36.454’ 15° 24.380’ Falls. Tal-Khanapur Nagargali Forest, 44 63 Wajra Poha Water B KA 74° 36.406’ 15° 24.445’ Falls. Tal-Khanapur Ramnagar Forest, Usoda 45 65 B KA 74° 32.683’ 15° 19.203’ Village, Tal-Khanapur Shingargaon Forest, 46 66 Shidicha Dongar U KA 74° 33.472’ 15° 20.428’ Tal-Haliyal. Shingargaon Forest, 47 67 U KA 74° 33.799’ 15° 20.819’ Nagzari, Tal-Haliyal. Valpoi Forest, Karanjol 48 68 NG GA 74° 13.572’ 15° 29.979’ Village, Tal-Sattari Valpoi Forest, Karanjol 49 69 NG GA 74° 13.583’ 15° 29.990 Villa39ge, Tal-Sattari Madai Forest, Shigne 50 70 Village, Kodal Region NG GA 94° 47.941’ 38° 51.334’ Tal-Sattari Madai Forest, Shigne 51 71 Village, Kodal Region NG GA 74° 10.788’ 15° 35.094’ Tal-Sattari Netrawali Forest 52 72 Pattrem Village Site, SG GA 74° 15.766’ 15° 12.570 Tal-Sanguem. Netrawali Forest 53 73 Pattrem Village Site, SG GA 74° 15.801’ 15° 12.749’ Tal-Sanguem. Netruli-Curdi Range 54 74 Forest, Matoni Mountain SG GA 74° 13.960’ 15° 04.385’ Tal-Sanguem. Savari Village Forest, 55 75 Savari Water Falls. SG GA 74° 13.678’ 15° 03.802’ Tal-Sanguem. Ibrampur Forest, Survey 56 76 NG GA 73° 55.317’ 15° 43.190’ No-41-1, Tal-Pernem.

Ibrampur Forest, Survey 57 77 NG GA 73° 55.321’ 15° 43.183’ No-41-1, Tal-Pernem. Ibrampur Forest 58 78 Karmatali- Below NG GA 73° 56.026’ 15° 43.043’ Ghoddev, Tal-Pernem. Chandel Forest 59 79 NG GA 73° 52.341’ 15° 43.029’ Tal-Pernem. Chandel Forest 60 80 NG GA 73° 52.313’ 15° 43.031’ Tal-Pernem. Chandel Forest 61 81 NG GA 73° 52.339’ 15° 43.015’ Tal-Pernem. Mhasve Grassland, 62 85 Mahar Pathar. K MH 74° 07.630’ 16° 20.015’ Tal-Bhudargad Mhasve Grassland, 63 88 Bhatawade Plateau, Tal- K MH 74° 06.965’ 16° 19.689’ Bhudargad. Dandeli Wild Life Sanctuary, Kulgi Tiger 64 89 U KA 74° 38.044’ 15° 09.590’ Reserve Forest-Nagzari Valley. Tal-Haliyal. Dandeli Wild Life Sanctuary, Kulgi Tiger 65 90 Reserve Forest-Survey U KA 74° 37.971’ 15° 09.669’ No.-4-1 Compartment Tal-Haliyal. Dandeli Wild Life Sanctuary, Kulgi Tiger 66 91 Reserve Forest-Pansoli- U KA 74° 37.270’ 15° 09.820’ Upper Part Of Nagzari Valley. Tal-Haliyal. Dandeli Wild Life Sanctuary, Kulgi Tiger 67 92 U KA 74° 34.535’ 15° 09.629’ Reserve Forest-Ernoli Range. Tal-Haliyal. Dandeli Wild Life Sanctuary, Kulgi Tiger 68 93 U KA 74° 34.542’ 15° 09.644’ Reserve Forest-Potoli Range. Tal-Haliyal. Anshi National Park Grassland, Barpoli- 69 94 U KA 74° 22.515’ 14° 56.658’ Survey No.-135 Tal-Joyda, Anshi National Park 70 95 Forest, Barpoli-Survey U KA 74° 22.477’ 14° 56.633’ No.-135 Tal-Joyda, Anshi National Park 71 96 Forest, Badkoli-Survey U KA 74° 20.912’ 15° 00.360’ No.-135 Tal-Joyda, 72 97 Anshi National Park U KA 74° 20.912’ 15° 00.358’

Forest, Badkoli-Survey No.-135 Tal-Joyda, Ramnagar Forest, Londha Range-Abnali 73 98 B KA 74° 24.405’ 15° 32.763’ Site,Survey No.-40 Tal-Khanapur. Ramnagar Forest, Londha Range, Shiroli 74 99 B KA 74° 26.386’ 15° 34.258’ Bit- Gunji Site Tal-Khanapur. Ramnagar Forest, Londha Range, Shiroli 75 100 B KA 74° 26.739’ 15° 34.185’ Bit- Gunji Site Tal-Khanapur. Kankumbi Forest, 76 101 Grassland, Hulan Site B KA 74° 10.854’ 15° 42.082’ Tal-Khanapur. Kankumbi Forest, 77 102 Grassland, Hulan Site B KA 74° 10.880’ 15° 42.050’ Tal-Khanapur. Kankumbi Forest, Grassland,Chigula 78 103 B KA 74° 12.350’ 15° 45.592’ Range-Nival, Tal-Khanapur. Kankumbi Forest, Grassland,Chigula 79 104 B KA 74° 12.302’ 15° 45.666’ Range-Nival, Tal-Khanapur. Verna –Grassland, 80 109 Lateritic Plateau SG GA 73° 56.132’ 15° 22.673’ Tal-Mormugao Verna –Grassland, 81 110 Lateritic Plateau SG GA 73° 56.136’ 15° 22.710’ Tal-Mormugao Verna –Grassland, 82 111 Lateritic Plateau SG GA 73° 56.108’ 15° 22.855’ Tal-Mormugao Taleigao Plateau 83 113 Grassland, Lateritic NG GA 73° 50.427’ 15° 27.475’ Plateau. Tal-Tiswadi Taleigao Plateau 84 115 NG GA 73° 50.337’ 15° 27.534’ Grassland, Tal-Tiswadi 74° 1.194’ 15° 34.77’ 85 ### Pareye Tal- Sattari NG GA

Anjunem Ghat Tal- 74° 5.056’ 15° 36.9’ 86 ### NG GA Sattari 74° 5.999’ 15° 36.571’ 87 ### Gule Tal- Sattari NG GA

Valley In Chorlem –Ghat 74°6.765’ 15° 38.527’ 88 ### NG GA Tal- Sattari

Kankumbi Ghat Tal- 74° 14.759’ 15° 42.856’ 89 ### B KA Khanapur 15Km.after Talawade 74° 16.921’ 15° 43.091’ 90 ### B KA Tal- Khanapur Sulambi Tal- 74° 3.446’ 16°24.867’ 91 ### K MH Radhanagari Solankur Ghat Tal- 74° 2.835’ 16° 25.188’ 92 ### K MH Radhanagari Radhanagari Ghat Tal- 74° 0.482’ 16° 24.768’ 93 ### K MH Radhanagari 73° 43.459’ 16°19.332’ 94 ### Humbrat,Tal-Kankawali S MH

Wagde-5Km.after- Tal- 73° 42.449’ 16°14.164’ 95 ### S MH Kankawali 2 km.after Sindhudurg 73° 42.518’ 16° 5.161’ 96 ### S MH Tal-Kankawali 5Km.before Kudal Tal- 73° 42.162’ 16° 2.518’ 97 ### S MH Kudal 73° 45.488’ 15° 56.216’ 98 ### Zarap Tal- Sawantwadi S MH

73° 47.209’ 15° 55.597’ 99 ### Akeri Ghat Tal- Kudal S MH

Near Banda Tal- 73° 50.439’ 15° 51.217’ 100 ### S MH Sawantwadi Patradevi Tal- 73° 51.79’ 15° 47.55’ 101 ### S MH Sawantwadi Patradevi-Ghat Tal- 73° 50.116’ 15° 45.454’ 102 ### S MH Sawantwadi After Pernem Tal- 73° 49.424’ 15° 41.848’ 103 ### NG GA Pernem Chapora River Tal- 73° 50.253’ 15° 39.127’ 104 ### NG GA Bardez

WLS = Wildlife Sanctuary States: Districts: GA – Goa B - Belgaum KA – Karnataka K - Kolhapur MH – Maharashtra NG – North Goa S – Sindhudurg SG – South Goa U – Uttara Kannada

### Qudrats are not intended for vegetation and soil analysis

1.000

.03

0.52 0.05 0.28 0 6.72 8.94 31.22 (NI)

SD 158.47 293.55

Weed area Weed

sites (NI) infested -

0.51 0.05 0.34 0.03 8.93 p

11.05 25.69 SD (I) 155.38 292.64

1.000 0.293 0 0 - 56 4.7 0.03 0.78 0.08 13.7 (NI)

Low 44.19 jacent non jacent

0 0 20

4.8

Canopy Ga 0.03 0.07 0.01 67.2 Low (I) 49.68

and ad and

34 6.8

100

0.29 2.32 0.23 in infested sites infested in 24.53 918.4

(NI)

1.000 0.224 0.159 High 875.08

Altitude

40 7.3

840 100 0.33 2.28 0.23 44.7

(I) High 815.95

34 C.odorata 1.000 0.084 0.089 0.170 Slope 6.8 100 0.29 2.32 0.23 24.53 918.4

875.08

(NI)

Maximum Chromolaena odorata Chromolaena 1.000 0.099 0.412 0.040 0.032

K -

40 7.3

840 100 0.33 2.28 0.23 44.7 815.95

1.000 0.004 0.198 0.178 0.067 0.062

Maximum (I) P - - - -

0 0

56 4.7

0.03 0.78 0.08 13.7 44.19

1.000 0.126 0.089 0.127 0.195 0.064 0.221

N - - (NI) Minimum

0 20

4.8

0.03 0.07 0.01 0.00 67.20 49.68

1.000 1.000 0.126 0.089 0.127 0.195 0.064 0.221

OC - - (I)

Minimum

6.07 0.08 1.82 0.18 4.96

11.15 50.19 1.000 0.094 0.094 0.224 0.199 0.1 0.281 0.247 0.121 (NI)

Mean 287.21 358.78 EC -

6.07 0.09 1.83 0.18 6.34 12.2

60.76 Mean (I) 289.96 354.61 1.000 0.173 0.122 0.122 0.003 0.024 0.394 0.040 0.324 0.559

pH - - - -

Soil and habitat parameters of sites infested (I) infested sites with (I) of parameters Soilhabitat and occupied by andarea parameters soil, of habitat Correlation

: :

pH EC(m/mhos) (%) OC (%) N (kg/ha) P (kg/ha) K Slope(degrees) (m) Altitude (%) CanopyGap

pH EC OC N P K Slope Altitude Canopy Gap Area Weed

Table4 Table3

Weed area (I) area Weed

1.00

Canopy Gap (NI) Gap Canopy

1.00

0.48

Canopy Gap (I) Gap Canopy

0.67

1.00

0.29

sted (NI) sites sted(NI)

Altitude (NI) Altitude

infe

0.24

1.00

0.14

Altitude Altitude (I)

0.25

0.22

0.98

1.00

0.16

Slope (NI) Slope

0.31

0.00

0.08

0.06

1.00

Slope (I) Slope

0.17

0.11

0.09

0.08

0.72

1.00

K (NI) K

.17

0.01

0.39

0.35

0.18

0.08

1.00 0.02

K (I) K

0.04

0.43

0.41

0.13

0.10

0.62

1.00

0.03

0.02

P (NI) P

non adjacent and (I) in both infested

0.18

0.02

0.03

1.00

0.03

0.06

0.04

0.03

0.06

(I)

0.06

0.00

0.45

1.00

0.14

0.07

0.20

0.18

0.04

0.20

C.odorata

N (NI) N

0.08

0.00

0.09

0.08

0.13

0.01

1.00

0.03

0.04

0.01 0.11

0.16

N (I) N

0.22

0.20

0.08

0.09

0.19

0.13

0.32

1.00

0.14

0.06

0.08

0.13

OC (NI) OC

0.08

0.00

0.09

0.08

0.13

0.01

1.00

0.32

1.00

0.03

0.04

0.01

0.11

0.16

OC (I) OC

0.22

0.20

0.08

0.09

0.19

0.13

0.32

1.00

0.32

1.00

0.14

0.06

0.08

0.13

EC (NI) EC

0.26

0.27

0.28

0.25

0.11

0.18

0.06

0.12

0.09

0.14

0.16

0.03

0.16

0.03 1.00

0.15

EC (I) EC

0.20

0.25

0.29

0.28

0.12

0.16

0.09

0.20

0.22

0.13

0.09

0.13

0.09

0.91

1.00

0.12

pH (NI) pH

0.53

0.48

0.36

0.15

0.21

0.15

0.03

0.09

1.00

0.35

0.38

0.10

0.09

0.01

0.10

- -

pH (I) pH

0.56

0.50

0.39

0.01

0.20

0.00

0.12

0.10

0.17

0.89

1.00

0.26

0.32

0.07

0.04

0.02

0.13 0.13

- - -

Correlation of soil, habitat parameters and area occupied by andarea parameters soil, of habitat Correlation

(I)

Weed area (I)

Canopy Gap (NI)

Canopy Gap (I)

Altitude (NI) Altitude

Altitude (I) Altitude

Slope (NI)

Slope (I)

K (NI)

K (I)

P (NI) P

N (NI)

N (I)

OC (NI)

OC (I)

EC (NI) EC

EC (I) EC

pH (NI)

pH (I)

Table5

Table 6: Species that are exclusive to areas not infested with Chromolaena odorata. Sr. Habit/ No. stage Species 1 T Acacia jacquemontii Benth 2 T Actinodaphne angustifolia Nees 3 T Anogeissus latifolia (Roxb.ex DC.) Wall. ex Guill. & Perr. 4 T Aporosa lindleyana(Wight)Baill. 5 T Bauhinia grandiceps Jacq. 6 T Beilschmiedia dalzellii (Meissn.) Kosterm. 7 T Bridelia crenulata Roxb. 8 T Bridelia squamosa (Lam.) Gehrm. 9 T Casuarina glauca Sieber ex A. DC. 10 T Chloroxylon swietenia DC. 11 T Dolichandrone falcata (Wall. ex DC.) Seem. 12 T Emblica officinalis Gaertn. 13 T Ficus nervosa Heyne ex Roth 14 T Ficus racemosa L. 15 T Glochidion ellipticum Wight 16 T Grewia tiliifolia Vahl 17 T Holarrhena pubescens (Buch.-Ham.) Wall. 18 T Mammea suriga (Buch.-Ham. ex Roxb.) Kosterm. 19 T Mangifera indica L. 20 T Nothopegia castaneifolia (Roth) Ding Hou 21 T Phoenix acaulis Roxb. ex Buch.-Ham. 22 T Strychnos nux-vomica L. 23 T Walsura trifoliate (A. Juss.) Harms 24 Sd Albizia odoratissima (L.f.) Benth 25 Sd Alysicarpus longifolius (Rottl.ex spreng.)Wight &Arn. 26 Sd Azadirachta indica A.Juss. 27 Sd Bauhinia scandens L. 28 Sd Calamus rotang L. 29 Sd Canthium dicoccum (Gaertn.)Teijson.& Binn 30 Sd Caryota urens L. 31 Sd Dalbergia sissoo Roxb. 32 Sd Embelia basaal (R&S.) A.DC. 33 Sd Ficus geniculata Kurz 34 Sd Ficus religosa L. 35 Sd Grewia rothii DC. 36 Sd Holoptelea integrifolia (Roxb.) Planch. 37 Sd Hymenodictyon orixense (Roxb.) Mabb. 38 Sd Ixora brachiata Roxb. 39 Sd Meyna laxiflora Robyns 40 Sd Murraya paniculata (L.)Jack syn. M.exotica L. 41 Sd Ochna obtusata DC 42 Sd Tabernaemontana alternifolia (Roxb.) Nicols. & Suresh

43 Sd Terminalia catappa L. 44 Sd Trichosanthes dioica Roxb. 45 S Albizia lebbeck (L.) Benth 46 S Anisochilus carnosus (L.) Wall. 47 S Bambusa vulgaris Schrad. 48 S Blumea eriantha DC. 49 S Boehmeria glomerulifera Miq. 50 S Bridelia scandens (Roxb.) Willd. 51 S Canthium coromandelicum (N. Burm.) Alston 52 S Casearia graveolens Dalzell 53 S Cissampelos pareira L. 54 S Derris macrocarpa Thoth. 55 S Desmodium styracifolium (Osb.)Merr. 56 S Dracaena terniflora Roxb. 57 S Glochidion malabaricum Bedd. 58 S Grewia flavescens A.Juss. 59 S Grewia sclerophylla Roxb. 60 S Ixora nigricans R.Br. ex Wight. & Arn. 61 S Jasminum auriculatum Vahl 62 S Leea guineensis D. Don 63 S Ligustrum perrottetii A.DC. 64 S Mallotus aureo-punctatus (Dalzell) Muell.-Arg. 65 S Mallotus philippensis (Lam.) Muell.-Arg. 66 S Mallotus repandus (Willd.) Muell.-Arg. 67 S Neuracanthus trinervius Wight 68 S Olax psittacorum (Willd.)Vahl 69 S Pavetta tomentosa Roxb. ex Sm. 70 S Phyllocephalum scabridum (DC.) Kirkman 71 S Poreynia retusa (Dennst.)Alston 72 S Quirivelia frutescens (L.) M.R.Almeida 73 S Streptolirion ixiocephala Benth 74 S Xantolis tomentosa (Roxb.) Raf. 75 S Zanthoxylum rhesta (Roxb.)DC. 76 S Ziziphus oenoplia (L.) Mill. 77 R Dioscorea belophylla Haines 78 H Aristida adscensionis L. 79 H Aristida mutabilis Trin. & Rupr. 80 H Arundinella ciliata (Roxb.) Nees ex Miq. 81 H Arundinella pumila (Hochst.ex A. Rich.) Steud. 82 H Asystasia mysurensis (Rolh.) T. Anderson 83 H Canscora decurrens Dalzell 84 H Chrysopogon polyphyllus (Hack. ex Hook. f.) Blatt.& McCann 85 H Chrysopogon serrulatus Trin. 86 H Clematis gouriana Roxb.ex DC. 87 H Clematis heynei M.A.Rau

88 H Crotalaria triquetra Dalzell 89 H Curculigo orchioides Gaertn. 90 H Curcuma purpurea Blatt. 91 H Cymbopogon martinii (Roxb.) Wats. 92 H Cyperus decumbens Govind 93 H Desmodium laxiflorum DC. 94 H Desmodium neomexicanum A. Gray 95 H Desmodium pulchellum (L.) Benth 96 H Dichanthium filiculme (Hook.f.) Jain & Deshpande 97 H Diospyros buxifolia (Blume) Hiern 98 H Eragrostis gangetica (Roxb.)Steud. 99 H Fimbristylis nagpurensis Prasad & N.P Singh 100 H Impatiens lawii Hook. f. & Thoms. 101 H Isachne gracilis C.E.Hubb. 102 H Ischaemum barbatum Retz. 103 H Ischaemum dalzellii Stapf ex Bor 104 H Ischaemum mangaluricum (Hack.) Stapf ex C.E.C. Fischer 105 H Ischaemum pilosum (Klein ex Willd.) Wight 106 H Kyllinga brevifolia Rotth. 107 H Lamprachaenium microcephalum (Dalzell) Benth 108 H Lepidagathis cristata Willd. 109 H Malvastrum coromandelianum (L.) Gorcke 110 H Mitreola petiolata (J.F.Gamel.) Torr. & A.Gray 111 H Naregamia alata Wight & Arn. 112 H Ophiopogon intermedius D.Don var.pauciflora Hook. 113 H Paspalum canarae (Steud.) Veldk. 114 H Paspalum paspalodes (Michx.) Scribner. 115 H Phyllanthus debilis Klein ex Willd. 116 H Piper nigrum L. 117 H Pseudanthistiria heteroclita Hook. f. 118 H Pseudarthria viscida (L.)Wight & Arn. 119 H Pueraria montana (Lour.) Merr. 120 H Rostellularia diffusa var. prostrata (Roxb. ex C.B.Clarke) Ellis 121 H Rungia crenata T. And. 122 H Rungia repens (L.) Nees 123 H Scleria poklii Wad. Khan 124 H Selaginella involvens (Sw.)Spring 125 H Selaginella radicata (Hook. & Grev.) Spring 126 H Sida cordata (Burm. f.) Borss. 127 H Sida cordifolia L. 128 H Sida spinosa L. 129 H Smithia agharkarii Hem. 130 H Smithia setulosa Dalzell 131 H Spilanthus paniculata Wall.ex DC. 132 H Tricholepis glaberrima DC.

133 H Tylophora dalzellii Hook. f. 134 H Utricularia reticulata Sm. 135 H Vernonia cinerea (L.) Less. 136 Cr Blachia denudata Benth 137 Cr Tragia plukenetii L. Radcliffe-Smith 138 Cl Acacia canescens Graham 139 Cl Argyreia cymosa (Roxb.) Sweet 140 Cl Cissus gigantean (Bedd) Planch. 141 Cl Cissus repanda Vahl 142 Cl Entada rheedei Spreng. 143 Cl Entada rheedei Spreng. 144 Cl Gymnema sylvestre (Retz.) R. Br. ex Schult. 145 Cl Pterospermum diversifolium Blume 146 Cl Sida cordifolia L. 147 Cl Smilax ovolifolia Roxb.

Habit/ stage: T = Tree; S = Shrub; H = Herb; Cl = Climber; Cr = Creeper; Sd = Seedling/Sapling

Table 7: Species that are exclusive to areas infested with Chromolaena odorata. S.No Habit/Stage Species 1 T Acacia canescens Graham 2 T Acacia verticillata Willd. 3 T Albizia amara (Roxb.) Boiv. 4 T Alseodaphne semicarpifolia Nees 5 T Boswellia serrata Roxb. ex Colebr. 6 T Celtis timorensis Spanoghe 7 T Cissus woodrowii (Stapf ex Cooke) Santapau 8 T Ficus amplissima J.E.Sm. 9 T Flacourtia latifolia (Hook. f. & Thoms.)T. Cooke 10 T Hydnocarpus pentandra (Buch.-Hum.) Oken 11 T Lagerstroemia floribunda Jacq. 12 T Prosopis cineraria (L.) Druce. 13 T Santalum album L. 14 T Sapium insigne Benth 15 T Trema orientalis (L.) Blume 16 T Ziziphus mauritiana Lam. 17 Sd Aphanamixis polystachya (Wall.)Parker 18 Sd Aporosa lindleyana (Wight) Baill. 19 Sd Callicarpa tomentosa (L.) Murr. 20 Sd Canavalia africana Dunn 21 Sd Gnetum ula Brongn 22 Sd Grewia eriocarapa A.L.Juss. 23 Sd Heterophragma quadriloculare (Roxb.) K. Schum. 24 Sd Maesa indica (Roxb.) A. DC. 25 Sd Maytenus senegalensis (Lam.) Excell. 26 Sd Melastoma malabathricum L. 27 Sd Memecylon umbellatum Burm. 28 Sd Nothapodytes nimmoniana (Graham) Mabberley 29 Sd Radermachera xylocarpa (Roxb.) K. Schum. 30 Sd Tabernaemontana divaricata (L.) R. Br. 31 Sd Woodfordia fruticosa (L.) Kurz 32 S Argyreia cuneata (Willd.) Ker-Gawl. 33 S Barleria prionitis.L. 34 S Cajanus sericeus (Benth ex Baker.) Maesen 35 S Calamus thwaitesii Becc. & Hook.f. 36 S Strobilanthes callosus Nees 37 S Catunaregam spinosa (Thunb.) Tirveng. 38 S Clerodendrum viscosum Vent. 39 S Costus speciosus (Koen.) J.E.Sm. 40 S Cyperus digitatus Roxb. 41 S Dalbergia horrida (Dennst.) Mabb. 42 S Diospyros exculpta Buch.-Ham. 43 S Justicia betonica L.

44 S Leea asiatica (L.) Ridsd. 45 S Lobelia nicotianaefolia Roth ex Roem. & Schult. 46 S Maesa indica (Roxb.) A. DC. 47 S Maytenus ovata (Wall. ex Wight & Arn.) Loes. 48 S Phyllanthus reticulatus Poir. 49 S Pogostemon benghalensis (Burm.f.) Kuntze 50 S Pogostemon quadrifolius (Benth) Kuntze 51 S Securinega leucopyrus (Willd.) Muell.-Arg. 52 S Strychnos potatorum L. 53 S Trewia nudiflora L. 54 S Triumfetta annua L. 55 S Triumfetta malabarica Koen. 56 S Woodfordia fruticosa (L.) Kurz 57 S Zingiber neesanum (Grah.) Ramam. 58 S Ziziphus jujuba Mill. 59 S Ziziphus nummularia (Burm. f.) Wight & Arn. 60 H Adiantum lunulatum Burm. 61 H Amorphophallus paeoniifolius (Dennst.) Nicols. 62 H Aristida hystrix L. f. 63 H Arthraxon hispidus (Thunb.) Makino 64 H Arthraxon lanceolatus (Roxb.) Hochst. 65 H Arundinella metzii Hochst. ex Miq. 66 H Arundinella spicata Dalzell 67 H Asparagus laevissimus Steud. 68 H Bidens biternata (Lour.) Merr. & Sherff. 69 H Biophytum sensitivum (L.) DC. 70 H Blumea bifoliata (L.) DC. 71 H Centotheca lappacea (L.) Desv. 72 H Chlorophytum arundinaceum Baker 73 H Clematis wightiana Wall. 74 H Curcuma pseudomontana Graham 75 H Cyanotis papilionaceae (L.) R.& S.var. vaginata Fischer 76 H Cyathula prostrata (L.) Blume 77 H Cynodon dactylon (L.) Pers. 78 H Cyperus halpan L. 79 H Cyrtococcum patens (L.)A. Camus 80 H Desmodium gangeticum (L.) DC. 81 H Dichanthium annulatum (Forssk.) Stapf 82 H Dicliptera foetida (Forssk.) Blatt. 83 H Dimeria ornithopoda Trin. 84 H Diplazium polypodioides Blume 85 H Dodonea viscosa (L.) Jacq. 86 H Emilia sonchifolia(L.)DC. 87 H Eragrostis unioloides (Retz.) Nees ex Steud. 88 H Eriocaulon eurypeplon Koern.

89 H Eulalia fastigiata (Nees ex Steud.) Haines 90 H Fimbristylis cinnamometorum (Vahl) Kunth 91 H Fimbristylis sieberiana Kunth 92 H Glyphochloa goaensis (Rao & Hemadri) W.D. Clayton 93 H Glyphochloa mysurensis (Jain & Hemadri) W.D. Clayton 94 H Impatiens dalzellii Hook.f.& Thoms 95 H Isachne bicolor Naik & Patunkar 96 H Ischaemum semisagittatum Roxb. 97 H Iseilema laxum Hack. 98 H Justicia trinervia Vahl 99 H Kalanchoe pinnata (Lam.) Pres. 100 H Lepidagathis incurve Buch- Ham. ex D.Don 101 H Leucas biflora (Vahl) R. Br. 102 H Leucas ciliata Benth 103 H Leucas indica (L.) R.Br. ex Vatke 104 H Linum mysurense Heyne ex Benth 105 H Lygodium microphyllum (Cav.)R.Br. 106 H Mussaenda glabrata (Hook.f.) Hutch. ex Gamble 107 H Nelsonia canescens (Lam.) Spreng. 108 H Ophiorrhiza rugosa Wall. 109 H Osbeckia muralis Naud. 110 H Panicum antidotale Retz. 111 H Paspalidium flavidum (Retz.) A. Camus 112 H Passiflora foetida L. 113 H Pennisetum alopecuroides (L.) Spr. 114 H Phyllanthus virgatus Forst. 115 H Pogostemon purpurascens Dalzell 116 H Pseudanthistiria hispida Hook. f. 117 H Pteris longipes D.Don 118 H Rostellularia japonica (Thunb.) Ellis 119 H Rungia linifolia Nees 120 H Selaginella repanda (Desv.)Spring 121 H Selginella inaequalifolia (Hook & Grev.) Spring 122 H Sida acuta Burm.f. 123 H Spermacoce articularis L.f. 124 H Syzygium caryophyllatum (L.) Alst. 125 H Teramnus repens (Taub.) Baker f. 126 H Tridax procumbens L. 127 Cr Indigofera dalzellii T.Cooke 128 Cl Cissus latifolia Lam. 129 Cl Derris heyneana (Wight & Arn.) Benth 130 Cl Jasminum azoricum L. 131 Cr Paracalyx scariosus (Rooxb.) Ali 132 S Crotalaria clavata Wight & Arn. 133 H Cyperus rotundus L.

Table 8: Species that are common to areas infested with Chromolaena odorata and outside. Sr. Habit / Species No Stage 1 T Acacia concinna (Willd.) DC. 2 T Alseodaphne semicarpifolia Nees 3 T Alstonia scholaris (L.) R.Br. 4 T Aporosa lindleyana (Wight) Baill. 5 T Bombax ceiba L. 6 T Bridelia retusa (L.) Spreng. 7 T Buchanania cochinchinensis (Lour.) M.R.Almeida. 8 T Butea monosperma (Lam.) Taub. 9 T Caesalpinia pulcherrima (L.) Sw. 10 T Careya arborea Roxb. 11 T Cassia fistula L. 12 T Cassia surattensis Burm. f. subsp. glauca (Lam.) K.S. Larsen 13 T Catunaregam spinosa (Thunb.) Tirveng. 14 T Catunaregam spinosa (Thunb.) Tirveng. 15 T Dalbergia lanceolaria L. f. 16 T Dalbergia latifolia Roxb. 17 T Desmodium triquetrum (L.) DC. 18 T Dillenia pentagyna Roxb. 19 T Dillenia pentagyna Roxb. 20 T Diospyros candolleana Wight 21 T Diospyros montana Roxb. 22 T Ficus exasperate Vahl 23 T Ficus hispida L. f. 24 T Grewia abutilifolia Vent. 25 T Grewia nervosa (Lour.) Panigr. 26 T Grewia tilifolia Vahl,(tiliaefolia) 27 T Kydia calycina Roxb. 28 T Lagerstroemia microcarpa Wight 29 T Lagerstroemia microcarpa Wight 30 T Lagerstroemia parviflora Roxb. 31 T Lannea coromandelica (Hautt.) Merr. 32 T Memecylon umbellatum Burm. f. 33 T Miliusa tomentosa (Roxb.) Sinclair 34 T Olea dioica Roxb. 35 T Pavetta crassicaulis Bremek. 36 T Schleichera oleosa (Lour.) Oken 37 T Sida rhombifolia L. 38 T Sterculia foetida L. 39 T Syzygium cumini (L.) Skeels 40 T Syzygium zeylanicum (L.) DC. 41 T Tabernaemontana alternifolia (Roxb.)Nicols. & Suresh

42 T Tectona grandis L. 43 T Tectona grandis L. f. 44 T Terminalia bellirica (Gaertn.) Roxb. 45 T Terminalia chebula Retz. 46 T Terminalia cuneata Roth 47 T Terminalia elliptica Willd. 48 T Terminalia paniculata Roth 49 T Vitex altissima L.f. 50 T Wendlandia thyrsoidea (Roem. & Schult.) Steud. 51 T Wrightia tinctoria R. Br. 52 T Xylia xylocarpa (Roxb.) Taub. 53 T Ziziphus rugosa Lam. 54 Sd Allophylus cominia (L.) Sw. 55 Sd Atalantia racemosa Wight 56 Sd Bambusa arundinacea (Retz.)Willd. 57 Sd Bambusa glaucescens (Willd.) Sieb. 58 Sd Caesalpinia cucullata Roxb. 59 Sd Calamus pseudo-tenuis Becc. & Hook. f. 60 Sd Catunaregam spinosa (Thunb.) Tirveng. 61 Sd Colebrookea oppositifolia Sm. 62 Sd Diospyros candolleana Wight 63 Sd Garcinia indica (Du Petit-Thou.) Choisy 64 Sd Glochidion lanceolarium Voigt 65 Sd Glycosmis pentaphylla (Retz.) DC. 66 Sd Grewia asiatica L. 67 Sd Grewia heterotricha Mast. 68 Sd Hopea ponga (Dennst.) Mabb. 69 Sd Ixora brachiata Roxb. 70 Sd Leea setuligera C.B.Clarke 71 Sd Moullava spicata (Dalzell) Nicols. 72 Sd Paramignya monophylla Wight 73 Sd Psychotria dalzellii Hook. f. 74 Sd Tabernaemontana alternifolia (Roxb.) Nicols. & Suresh 75 S Allophylus cobbe (L.) Raeusch. 76 S Arundinella tuberculata Munro ex Lisboa 77 S Asystasia dalzelliana Santpau 78 S Breynia retusa (Dennst.) Alston 79 S Caesalpinia cucullata Roxb. 80 S Carissa congesta Wight 81 S Strobilanthes callosa Nees 82 S Clerodendrum serratum (L.) Moon. 83 S Clerodendrum viscosum Vent. 84 S Crotalaria retusa L. 85 S Dalbergia candenatensis (Dennst.) Prain 86 S Desmodium umbellatum (L.) DC.

87 S Desmodium umbellatum (L.) DC. 88 S Eranthemum roseum (Vahl) R. Br. 89 S Eulalia trispicata (Schult.) Henr. 90 S Flacourtia indica (Burm.f.) Merr.var. latifolia 91 S Gnidia glauca (Fresen.) Gilg. 92 S Microcos paniculata L. 93 S Grewia orbiculata Rottl. 94 S Helicteres isora L. 95 S Ixora coccinea L. 96 S Ixora pavetta Andr. 97 S Leea compactiflora Kurz. 98 S Leea indica (Burm.f.)Merr. 99 S Leea robusta Roxb. 100 S Macaranga peltata (Roxb.) Muell.-Arg. 101 S Murraya koenigii (L.) Spreng 102 S Rungia pectinata (L.) Nees 103 S Senecio belgaumensis (Wight) C.B.Clarke 104 S Sida schimperiana Hochst. 105 S Solanum virginianum L. 106 S Thespesia lampas (Cav.) Dalzell & Gibs. 107 S Urena lobata L. 108 H Adiantum zollingeri Mett ex Kuhn 109 H Ageratum conyzoides L. 110 H Apluda mutica L. 111 H Aristida redacta Stapf 112 H Arthraxon lancifolius (Thrin.) Hochst. 113 H Arundinella nepalensis Trin. 114 H Arundinella purpurea Hochst. ex Steud. 115 H Asparagus gonoclados Baker 116 H Blumea malcolmii (C.B.Clarke) Hook. 117 H Boehmeria macrophylla Hornem. 118 H Canscora diffusa (Vahl) R.Br. ex Roem. & Schult. 119 H Canscora pauciflora Dalzell 120 H Celosia argentea L. 121 H Crotalaria filipes Benth 122 H Crotalaria vestita Baker 123 H Curcuma aromatica Salisb. 124 H Curcuma decipiens Dalzell 125 H Cyclea peltata (Lam.) Hook. f. Thoms. 126 H Cymbopogon flexuosus (Nees ex Steud.) Wats. 127 H Cynarospermum asperrimum (Nees) Vollesen 128 H Cyrtococcum acrescens (Trin.) Stapf 129 H Cyrtococcum deccanense Bor 130 H Cyrtococcum trigonum (Retz.) A. Camus 131 H Cyrtooccum oxyphyllum (Steud.) Stapf.

132 H Desmodium triflorum (L.)DC. 133 H Dicliptera cuneata Nees 134 H Dicliptera spinulosa Hochst. ex K. Balkwill 135 H Dioscorea pentaphylla L. 136 H Elephantopus scaber L. 137 H Eranthemum capense.L. 138 H Exacum tetragonum Roxb. 139 H Fimbristylis ambavanensis Prasad & N.P.Singh 140 H Flemingia strobilifera (L.) Ait. f 141 H Hemigraphis latebrosa (Heyne ex Roth) Nees 142 H Heteropogon contortus (L.) P. Beauv. ex Roem. & Schult. 143 H Heteropogon polystachyos (Roxb.) Schult. 144 H Hyptis suaveolens (L.) Poit. 145 H Impatiens kleiniformis Sedgw. 146 H Ischaemum diplopogon Hook. f. 147 H Ischaemum indicum (Houtt.) Merr. 148 H Ischaemum indicum (Houtt.) Merr. 149 H Iseilema holei Haines 150 H Justcia glauca Rottl. 151 H Justcia orbiculata Wall. ex T. Anders. 152 H Justicia latispica (C.B.Clarke) Gamble 153 H Leea macrophylla Roxb. 154 H Leucas cephalotes (Roth) Spr. 155 H Leucas stelligera Wall. 156 H Lygodium flexuosum(L.) Sw. 157 H Mimosa pudica L. 158 H Oplismenus burmannii (Retz.)P.Beauv. 159 H Oplismenus compositus (L.) P. Beauv. 160 H Phaulopsis imbricata (Forssk.) Sw. 161 H Phyllanthus tenellus Roxb. 162 H Phyllanthus urinaria L. 163 H Pimpinella wallichiana (Miq.ex Hohen.) Gandhi 164 H Pogostemon benghalensis (Burm.f.) Kuntze 165 H Rungia parviflora (Retz.) Nees 166 H Scleria africana Benth 167 H Selaginella delicatula (Desv.)Alston 168 H Setaria pumila (Poir.) Roem. & Schult. 169 H Smilax zeylanica L. 170 H Smithia conferta J.E.Sm. 171 H Spermacoce ocymoides Burm.f. 172 H Spermacoce pusilla Wall. 173 H Spodiopogon rhizophorus (Steud.) Pilger 174 H Teramnus labialis (L.f.) Spreng. 175 H Themeda cymbaria Hack. 176 H Themeda laxa (Anders.)A.Camus

177 H Themeda strigosa (Buch.-Ham. ex Hook. f.) A. Camus 178 H Themeda triandra Forssk. 179 H Trichodesma inaequale Edgew. 180 H Triumfetta rhomboidea Jacq. 181 H Vetiveria lawsonii (Hook. f.) Blatt.& McCann 182 Cl Butea superba Roxb. ex Willd. 183 Cl Calycopteris floribunda (Roxb.) Poir. 184 Cl Celastrus paniculatus Willd. 185 Cl Cocculus hirsutus (L.) Theob. 186 Cl Dioscorea bulbifera L. 187 Cl Gloriosa superba L. 188 Cl Hemidesmus indicus (L.) Schult 189 Cl Jasminum malabaricum Wight 190 Cl Moullava spicata (Dalzell) Nicols. 191 Cl Solena amplexicaulis (Lam.) Gandhi 192 Cl Ventilago maderaspatana Gaertn.

Habit/ stage: T = Tree; S = Shrub; H = Herb; Cl = Climber; Sd = Seedling/Sapling

Table 9: Correlation of vegetation characters in relation to area occupied by C. odorata

& &

f tree tree f

f trees f

Herbs

Canopy Gap no. Total o No. spp. spp of No. ( saplings) area Weed Weed area 1.000 Canopy Gap -0.302 1.000 Total no. of trees 0.132 -0.070 1.000 No. of tree spp. 0.190 -0.046 0.757 1.000 No. of spp (Herbs & saplings) -0.457 0.083 -0.303 -0.239 1.000

Table 10: WorldClim environment variables that define the potential distribution of obnoxious weed C. odorata in Northern Western Ghats

Variable Percent contribution Permutation importance Prec1 41.3 22.3 Prec7 27.7 1.7 Prec5 21.6 0.3 Bio19 2.5 0.1 Prec12 2.2 0.6 Bio4 1.3 61.1 Bio14 0.4 9.3

Kolhapur

Sindhudurg

Belgaum

N. Goa

S. Goa

Uttara Kannada

Chromolaena odorata

Western Ghats

Fig. 3: Distribution of Chromolaena odorata based on actual observations in the study area

Fig. 4: Positive correlation between pH of the soil and Chromolaena odorata patch size

Fig. 5: Slight negative correlation between canopy gap and and Chromolaena odorata patch size

Fig. 6: Bimodal distribution of Chromolaena odorata along the altitudinal gradient with its absence in mid altitudes of Northern Western Ghats

Fig. 7: Scatter diagram showing slight decrease in number of herbs with increasing area of occupancy by C. odorata

(The % average is by calculated

C. odorata

infested infested with

s s infested / non

Herbs and saplings exclusively present in site :

working out the percentage of their occupancy in each quadrat divided by the no. of quadrats in which they are present) divided quadrat are they in the by quadrats which of no. occupancy each in their of out percentage the working Fig. 8

= = % of

(The average % is calculated by working out

C. odorata

ot infested with

Herbs and saplings predominantly present in sites n

the the percentage of their occupancy in each quadrat divided by the no. of quadrats in which they are present; studied) quadrats total / of no. occupancy overall average Fig. 9:

(The average % is calculated by

C. odorata

studied) quadrats total / of no. ccupancy

Herbs and saplings predominantly present in sites infested with :

working working out the percentage of their occupancy in each quadrat of % = average o divided overall by the no. of quadrats in which they are present; Fig. 10

a b c

d e f

g h i

Fig. 11: Maps of potential distribution of Chromolaena odorata based in MAXENT model. (a-c) Minimum, (d-f) Average, (g-i) Maximum. Samples 63 (a,d,g), 84 (b,e,h) and 104 (c,f,i).

i) .

based in MAXENT model. Samples: 63 (a,d,g), 84

Chromolaena Chromolaena odorata

otential distribution of

Graphs Graphs of p

: : (b,e,h) and 104 (c,f,i). Minimum (a,b,c) ; Average (d,e,f ); Maximum (g,h, ); (d,e,f Average 104 ; and (c,f,i).Minimum (a,b,c) (b,e,h) Fig. 12

(ii) Lantana camara:

The studies made by Bhagwat et al. (2012), Goncalves et al. (2014) suggested that in India the habitats are more suitable for Lantana camara, hence its spreads very fast as compared to Australia and Africa.

A total of 48 localities infested with L. camara have been located in the study area. These localities are found in the states of Maharashtra and Karnataka (Fig. 13;

Table 11) generally along the hilly areas at higher altitudes of study area in the Western

Ghats. The rainfall here is lesser than western slopes of Western Ghats and slight overlap in seen with Chromoleana odorata. Two localities reported from Goa are on the plateaus and populations are of not invasive proportion.

Soil analysis shows that there is no significant difference among the soil parameters between infested and non-infested sites (Table12) with high standard deviation. The mean value of certain parameters such as pH is slightly higher in infested sites than the non-infested sites. The results follow the observations of Osunkoya et al.

(2010) in four sites of west Brishane, SE Australia wherein localities infested with L. camara showed higher pH. Similar results were reported earlier by Fan et al. (2010) in

China and by Sharma and Raghubanshi (2009) in India. As compared to the non- infested sites, P and K are slightly higher in infested sites.

There is a positive correlation between pH of the soil and weed patch size of L. camara in infested sites; positive correlation between pH of non-infested sites and weed size in adjacent sites (Tables 13) show that they are also probably prone for establishment.

The presence of L. camara and its influence on native flora and the occupancy of local species both in infested and adjacent non-infested sites have been studied. It is observed that 67 species of herbs and saplings are exclusively present in non-infested sites (Table 14) in which Dichanthium annulatum is showing the highest percentage

(80%) which is followed by the species such as Heteropogon ritchei and Triumfetta annua (Fig. 20). In the studies carried out by Dobhal et al. (2011) in Pauri Garhwal region of Uttarakhand, Eragrostis tenella was seen growing luxuriously in L. camara infested areas throughout the year. Earlier studies wherein invasive species affecting ecosystem including species diversity (Vitousek et al., 1996; Wilcov et al., 1998) and composition of native species (Grice, 2004; Jackson, 2005; Mason & French, 2007;

Gerber et al., 2008) are conclusive. Dobhal et al. (2011) reported 28.4% reduction in species richness in sites infested with L. camara. In the present study 76 species of herbs and saplings are exclusively present in infested sites (Table 15) in which

Schleichera oleosa is showing 50% average occurrence, followed by Hemigraphis hirta and Blumea laciniata (Fig. 20). The number of herbs and saplings with high percentage of occupancy in infested sites is higher than the non-infested sites (Fig. 22). Gooden et al. (2009) in their study in New South Wales, Australia found that L. camara invaded areas did not show reduction in species richness below 75% Lantana cover, but above

75% threshold level it shows reduction in number of species.

The average percentage of occupancy of herbs and saplings predominantly present in sites non-infested with L. camara shows that Dichanthium annualtum is with highest occupancy (80%) followed by Aristida redacta (60%) and Arundinella metzii

(50%) (Fig. 21). In the infested sites the occupancy percentage of Arundinella pumila is highest (70%) followed by Aristida redacta (60%) (Fig.22). The herbs such as

Arundinella pumila shows increase in occupancy in infested sites as compared to non- 64

infested sites, whereas A. metzii shows reduction in occupancy. Some species such as

Hemigraphis hirta, Blumea laciniata, Alternanthera sessilis show higher occupancy than their counterparts and are exclusively present in infested sites. It is clear that either some species are eliminated during the establishment of L. camara or their absence encourage L. camara to establish.

The correlation of vegetation characters in relation to area occupied by L. camara is studied (Table 17, 18). There is a negative correlation between weed area and canopy gap percentage (-0.22). There is a weak positive correlation between weed area and total number of trees. There is a positive correlation between total number of trees and number of tree species which is expected. The results clearly show negative correlation between number of species of herbs and saplings and (i) weed area (-0.56),

(ii) total number of tree species (-0.34) and (iii) number of tree species (-0.25), whereas there is a positive correlation between number of species of herbs and saplings and canopy gap (0.35) (Table 17). There is a negative correlation existing between weed area and number of herbs and sampling seedlings (Table 17, 18; Fig. 19). This aspect has been well documented in the literature and the present observations corroborate the findings of Sankaran (2007). There is a weak negative correlation between number of species in seedling and number of tree species (-0.08) (Table 18). As discussed by various authors canopy cover and canopy gaps depending on the size has either positive or negative impact on various parameters of vegetation including weeds and herbs.

Correlation of various soil parameters, slope, altitude, weed area and canopy gap of both infested and non-infested sites are shown in Table 13. Weed area shows positive correlation with pH, EC, OC, N, K, slope and altitude. However, slightly increased correlation of weed area with OC and N shows that infested sites tend to

accumulate organic content as reported by Rawat et al. (1994), Sharma and

Raghubanshi (2009), Fan et al. (2010) and Osunkoya and Perrett (2011), though Dobhal et al. (2010) reported the reverse.

Positive correlation is seen between pH and weed area (Fig. 14), slope and weed area (Fig. 15) and altitude and weed area (Fig. 16). Earlier reports (Fan et al., 2010;

Osunkoya & Perrett, 2011) also suggest positive correlation between pH and infestation though similar pH in adjacent non-infested sites as reported has not been discussed by them. The positive correlation between slope and weed area (Fig. 15), and altitude and weed area (Fig. 16) though weak suggest that higher altitudes with lesser rainfall than western part of the Western Ghats and slopes that drains the water are better suited for

L. camara. It is seen that L. camara is mostly recorded above 600 m MSL (Fig.18).

Negative correlation between canopy gap and weed area suggest that some amount of canopy is conducive for the growth of L. camara. However, this is in contrast to earlier observations (Duggin & Gentle, 1998; Totland et al., 2005), suggesting that there must be other parameters other than canopy gap that also play role in its growth.

Priyanka and Joshi (2013) investigated potential distribution of L. camara in two

National Parks of Western Himalayas using three different models, viz. BIOMAPER,

GARP and MAXENT, of which the first one under predicted, the second one over predicted and the third one predicted successfully. The potential distribution of L. camara using MAXENT modeler based on 29, 39, 48 samples from study area and based on 48 samples in study area and 5 samples far away from study area are shown in

Fig. 23-26 respectively. The area predicted with different sample sizes under minimum, average and maximum prediction values are shown in Fig. 27. While considering the average prediction with addition of samples the predicted area has come down at or

above 60% indicating to several variables defining its distribution. Five samples collected from areas far away from the study area marginally increased the predicted area. Overall it is concluded that in northern Western Ghats L. camara is not a serious weed yet and the prediction shows that potential area is very limited. However, Kannan et al. (2013) used GIS platform and predicted that the northern and southern Western

Ghats are more suitable than the Central Western Ghats and less towards western side of the Western Ghats. Less predicted area in the present stuydy may be due to the study area bordering Central Western Ghats which is not suitable.

Ray and Ray (2014) studied L. camara in relation to the distribution in India and integrated micro-satellites with niche modeling and shown that temperature and precipitation play an important role in the distribution and concluded that there is an emergence of ecotype in the form of two genetic clusters. Taylor and Kumar (2012) predicted distribution of L. camara using CLIMEX under current and future climate scenarios, wherein the resulting map also predicted the distribution of Lantana along the

Western Ghats. Using bioclimatic models, Vardian et al. (2012) predicted considerable expansion of L. camara in South Africa considering its wide ecological amplitude which may also be true in the Western Ghats.

The variables that defined the potential distribution of L. camara (Table 19, 20) show that as in C. odorata precipitation in the months of January, July and May are very important contributors and BIO14 (Precipitation of driest month) is of high permutation importance along with BIO4 [Temperature seasonality (standard deviation

*100)]. As these factors are the same for both C. odorata and L. camara, the difference in these factors define their area of spread with slight overlap.

Table 11: Locality details of Lantana camara in study area.

Sr. Q. Locality District State Longitude Latitude No. No.

Stawanidhi Ghat To 1 4 Ramlingeshwar B KA 74° 23.361’ 16° 20.823’ Forest, Tal-Chikodi. Kuditek To Rasai 2 6 Hills Forest, Tal- K MH 74° 19.128’ 16° 21.308’ Kagal. Bolawi to Pali Forest, 3 8 Tal-Kagal & K MH 74° 12.494’ 16° 19.910’ Tal-Bhudargad. Bolawi to Pali Forest, 4 9 Tal-Kagal & K MH 74° 12.185’ 16° 19.905’ Tal-Bhudargad. Bolawi to Pali Forest, 5 10 Tal-Kagal & K MH 74° 12.293’ 16° 19.866’ Tal-Bhudargad. Bolawi to Nangargaon Forest, 6 11 K MH 74° 12.376’ 16° 19.877’ Tal-Kagal & Tal-Bhudargad. Bolawi to Nangargaon Forest, 7 12 K MH 74° 12.306’ 16° 19.883’ Tal-Kagal & Tal-Bhudargad. Bolawi to Nangargaon Forest, 8 13 K MH 74° 12.332’ 16° 19.871’ Tal-Kagal & Tal-Bhudargad. Bhatwadi Forest- 9 14 Ruitala, Tal- K MH 73° 52.714’ 16° 06.348’ Bhudargad. Shivdav Forest- 10 15 Bhikyacha Tem, K MH 73° 58.193’ 16° 09.773’ Tal-Bhudargad Mhasve to Bhatiwde 11 19 K MH 74° 07.461’ 16° 19.877’ Forest Tal-Bhudargad Mhasve to Bhatiwde 12 20 K MH 74° 07.164’ 16° 19.868’ Forest Tal-Bhudargad Mhasve to Bhatiwde 13 21 K MH 74° 07.206’ 16° 19.538’ Forest Tal-Bhudargad Mhasve to Bhatiwde 14 25 K MH 74° 06.951’ 16° 19.698’ Forest Tal-Bhudargad Bediv to Savatwadi 15 26 K MH 74° 09.316’ 16° 12.542’ Forest (Chimne)-

Tal-Ajara Bediv to Savatwadi 16 27 Forest (Chimne)- K MH 74° 08.789’ 16° 12.415’ Tal-Ajara Bediv to Savatwadi 17 28 Forest (Chimne)- K MH 74° 08.790’ 16° 12.377’ Tal-Ajara Bediv to Savatwadi 18 29 Forest (Chimne)- K MH 74° 08.772’ 16° 12.287’ Tal-Ajara Bediv to Savatwadi 19 30 Forest (Chimne)- K MH 74° 08.389’ 16° 11.849’ Tal-Ajara Ramnagar Forest, 15° 19.029’ 20 64 Usoda Village, B KA 74° 32.723’

Tal-Khanapur Tawandi Grassland- 21 82 Ghol Plateau, B KA 74° 25.153’ 16° 20.825’ Tal-Chikodi Tawandi Grassland- 22 83 Ghol Plateau, B KA 74° 25.216’ 16° 20.803’ Tal-Chikodi Tawandi Grassland- 23 84 Ghol Plateau, B KA 74° 24.102’ 16° 21.534’ Tal-Chikodi Mhasve Grassland, 24 86 Bhatawade Plateau, K MH 74° 06.957’ 16° 19.782’ Tal-Bhudargad. Mhasve Grassland, 25 87 Bhatawade Plateau, K MH. 74° 06.958’ 16° 19.780’ Tal-Bhudargad. Verna –Grassland, 26 112 Lateritic Plateau SG GA 73° 56.117’ 15° 22.862’ Tal-Mormugao Taleigao Plateau Grassland, Lateritic 27 114 NG GA 73° 50.338’ 15° 27.539’ Plateau. Tal-Tiswadi Kankumbi Ghat Tal- 28 ### B KA 74°13.456’ 15° 42.56’ Khanapur Kankumbi Ghat Tal- 29 ### B KA 74°13.699’ 15° 42.596’ Khanapur Kankumbi Forest Tal- 30 ### B KA 74°15.253’ 15° 42.976’ Khanapur Kankumbi Forest Tal- 31 ### B KA 74° 15.714’ 15° 42.967’ Khanapur Talawade-Tal- 32 ### B KA 74° 16.079’ 15° 43.113’ Khanapur 33 ### 10 km after Talawade B KA 74° 16.891’ 15° 43.095’

Tal- Khanapur 2 km before Golyali 34 ### B KA 74° 17.47’ 15° 43.507’ Tal- Khanapur 1 km before Golyali 35 ### B KA 74° 17.657’ 15° 43.544’ Tal- Khanapur Golyali Tal- 36 ### B KA 74° 17.994’ 15° 43.497’ Khanapur Golyali Tal- 37 ### B KA 74° 18.122’ 15° 43.644’ Khanapur 3 km after Golyali 38 ### B KA 74° 19.254’ 15° 44.682’ Tal- Khanapur 4 km after Golyali 39 ### B KA 74° 19.254’ 15° 44.716’ Tal- Khanapur 10 km after Golyali 40 ### B KA 74° 19.343’ 15° 44.941’ Tal- Khanapur 15 km after Golyali 41 ### B KA 74° 19.709’ 15° 45.067’ Tal- Khanapur After Betgeri Tal- 42 ### B KA 74° 20.3’ 15° 45.338’ Khanapur 10 km after Betgeri 43 ### B KA 74° 22.332’ 15° 45.913’ Tal- Khanapur Belvatti Tal- 44 ### B KA 74° 22.874’ 15° 46.853’ Khanapur 2 km after Belvatti 45 ### B KA 74° 23.259’ 15° 47.906’ Tal- Khanapur Bijgarni Tal- 46 ### B KA 74° 23.722’ 15° 48.916’ Khanapur Bijgarni Tal- 47 ### B KA 74° 24.046’ 15° 49.34’ Khanapur Radhanagari Ghat 48 ### K MH 74° 1.543’ 16° 24.865’ Tal-Radhanagari 49 ### Nashik N MH 73° 45.379’ 19° 59.231’ 50 ### Panvel Tal- Panvel R MH 73°7.358’ 18° 59.417’ 51 ### Igatpuri N MH 73°33.11’ 19° 41`53.12’ 52 ### Nashik N MH 73°46.06’ 19° 57`12.87’ 53 ### Igatpuri N MH 73°33.776’ 19° 41.65’

States: Districts: GA – Goa B – Belgaum N – Nashik KA – Karnataka K – Kolhapur R – Raigad MH – Maharashtra NG – North Goa S – Sindhudurg SG – South Goa U – Uttara Kannada

### Qudrats are not intended for vegetation and soil analysis

SD (NI) 0.49 0.04 0.39 0.04 5.85 189.30 12.67 227.14 26.64

SD (I) 0.50 0.05 0.46 0.05 12.91 224.96 12.81 229.22 19.58

infestedsites (NI)

Maximum (NI) Maximum 7.20 0.19 2.49 0.25 20.80 792.96 45.00 981.76 100.00

and adjacent non adjacent and

Maximum (I) Maximum 7.10 0.24 2.42 0.24 66.74 1021.44 43.00 979.01 100.00

Lantanacamara

Minimum (NI) Minimum 5.20 0.04 0.96 0.10 0.00 123.20 0.00 49.68 15.96

Minimum (I) Minimum 5.00 0.03 0.32 0.03 0.00 145.60 0.20 49.68 24.05

Mean (NI) Mean 6.17 0.12 1.73 0.17 6.14 389.51 14.36 737.79 80.12

Mean (I) Mean 6.21 0.14 1.67 0.17 7.75 401.13 19.84 737.49 83.77

and habitat parameters of sites infested (I) infested sites with (I) of parameters habitat and

hos)

Soil

(kg/ha)

pH EC (m/m OC (%) N (%) P (kg/ha) K Slope (deg) (m) Altitude Canopy Gap

Table

Weed area (I) area Weed

1.00

infested

Canopy Gap (%) (NI) (%) Gap Canopy

0.32

1.00

Canopy Gap (%) (I) (%) Gap Canopy

0.53

0.67

1.00

adjacent adjacent non

Altitude (m) (NI) (m) Altitude

0.29

0.11

0.22

1.00

Altitude (m) (I) (m) Altitude

0.30

0.11

0.20

1.00

Slope

(deg) (NI) (deg)

0.05

0.41

0.15

0.29

0.28

1.00

Slope (deg) (I) (deg) Slope

0.05

0.47

0.19

0.15

0.69

1.00

K (kg/ha) (NI) (kg/ha) K

both both in infested (I) and

0.48

0.24

0.17

0.47

0.49

0.07

0.24

1.00

K (kg/ha) (I) (kg/ha) K

0.44

0.19

0.11

0.09

0.53

0.54

0.08

0.68

1.00

P (kg/ha) (NI) (kg/ha) P

L. camara

0.07

0.11

0.06

0.16

0.03

0.42

0.16

0.14

1.00

P (kg/ha) (I) (kg/ha) P

0.02

0.01

0.30

0.03

0.07

0.27

0.16

0.14

0.05

1.00

N (%)(NI) N

0.24

0.26

0.32

0.30

0.25

0.02

0.23

0.41

0.13

0.16

0.28

1.00

N (%)(I) N

0.02

0.17

0.20

0.11

0.30

0.27

0.12

0.10

0.21

0.11

0.12

0.73

1.00

OC (%) (NI) (%) OC

0.24

0.26

0.32

0.30

0.25

0.02

0.23

0.41

0.13

0.16

0.28

1.00

0.73

1.00

OC (%) (I) (%) OC

0.02

0.17

0.20

0.11

0.30

0.27

0.12

0.10

0.21

0.11

0.12

0.73

1.00

0.73

1.00

9 EC (m/mhos) (NI) (m/mhos) EC

0.15

0.10

0.30

0.03

0.54

0.39

0.43

0.4

0.39

0.23

0.14

0.12

1.00

EC (m/mhos) (I) (m/mhos) EC

0.27

0.13

0.15

0.32

0.49

0.30

0.28

0.32

0.40

0.17

0.33

0.14

0.86

1.00

pH (NI) pH

0.17

0.06

0.23

0.62

0.00

0.36

0.34

0.72

0.47

0.08

0.07

0.31

0.46

0.31

0.46

0.45

0.43

1.00

pH (I) pH

0.05

0.27

0.04

0.55

0.48

0.46

0.74

0.55

0.03

0.15

0.35

0.26

0.42

0.26

0.42

0.51

0.55

0.92

1.00

Correlation Correlation of soil, habitat parameters and area occupied by

Weed area (I) area Weed

Canopy Gap (%) (NI) Gap (%) Canopy

Canopy Gap (%) (I) Gap (%) Canopy

Altitude (m) (NI) Altitude (m)

Altitude (m) (I) Altitude (m)

Slope (deg) Slope(NI) (deg)

Slope (deg) Slope(I) (deg)

K (kg/ha) (NI) K (kg/ha)

K (kg/ha) (I) K (kg/ha)

P (kg/ha) (NI) P (kg/ha)

P (kg/ha) (I) P (kg/ha)

N (%)(NI)

N (%)(I)

OC (%) (NI) OC (%)

OC (%) (I) OC (%)

EC (m/mhos) (NI) EC (m/mhos)

EC (m/mhos) (I) EC (m/mhos)

pH (NI)

pH (I)

Table 13 sites (NI)

Table 14: Species that are exclusive to areas not infested with Lantana camara.

S. No. Species 1 Ageratum conyzoides L. 2 Albizia odoratissima (L.f.) Benth. 3 Allophylus cobbe (L.) Raeusch. 4 Amorphophallus paeoniifolius (Dennst.) Nicols. 5 Argyreia cuneata (Willd.) Ker-Gawl. 6 Argyreia nervosa (Burm.f.) Bojer 7 Arthraxon hispidus (Thunb.) Makino 8 Asparagus racemosus Willd. 9 Aspidopterys canarensis Dalzell 10 Aspidopterys cordata (Heyne ex Wall.) A.Juss. 11 Bauhinia scandens L. 12 Bidens biternata (Lour.) Merr. & Sherff. 13 Blumea eriantha DC. 14 Blumea malcolmii (C.B. Clarke) Hook. 15 Bridelia crenulata Roxb. 16 Canscora diffusa (Vahl) R.Br. ex R.& S. 17 Catunaregam spinosa (Thunb.) Tirveng. 18 Celosia argentea L. 19 Chloroxylon swietenia DC. 20 Clematis hedysarifolia DC. 21 Curcuma aromatica Salisb. 22 Cymbopogon flexuosus (Nees ex Steud.) Will. Watson 23 Desmodium velutinum (Willd.) DC. 24 Dichanthium annulatum (Forssk.) Stapf 25 Dichanthium filiculme (Hook.f.) Jain & Deshpande 26 Dichanthium tuberculatum (Hack.) T.A. Cope 27 Dicliptera foetida (Forssk.) Blatt. 28 Diospyros montana Roxb. 29 Erythrina arborescens Roxb. 30 Evolvulus alsinoides (L.)L. 31 Glycosmis pentaphylla (Retz.) DC. 32 Glyphochloa acuminata (Hack.) Clayton 33 Glyphochloa goaensis (Rolla Rao & Hemadri) Clayton 34 Grewia asiatica L. 35 Hemidesmus indicus (L.) R.Br. 36 Hemigraphis latebrosa (Heyne ex Roth) Nees 37 Heteropogon polystachyos (Roxb.) Schult. 38 Heteropogon ritchiei (Hook. f.) Blatt & McCann 39 Jasminum auriculatum Vahl 40 Jasminum malabaricum Wight 41 Jasminum rottlerianum Wall. ex A. DC.

42 Justicia latispica (C.B.Clarke) Gamble 43 Lagerstroemia floribunda Jacq. 44 Lagerstroemia microcarpa Wight 45 Leea asiatica (L.) Ridsdale 46 Lepidagathis incurva Buch.-Ham. ex Don 47 Macaranga peltata (Roxb.) Muell.-Arg. 48 Maytenus ovata (Wall. ex Wight & Arn.) Loes. 49 Morinda pubescens Sm. 50 Osyris quadripartita Salzm.ex Decne. 51 Pogostemon quadrifolius (Benth.) Kuntze 52 Polygala furcata Royle 53 Rungia crenata T. Anderson 54 Rungia repens (L.) Nees 55 Santalum album L. 56 Scleria poklei Wad. Khan 57 Sehima nervosum (Rottl.) Stapf 58 Senecio hewrensis (Dalzell) Hook. f. 59 Setaria pumila (Poir.) Roem & Schult 60 Setaria verticillata (L.) P. Beauv. 61 Smilax zeylanica L. 62 Smithia agharkarii Hemadri 63 Terminalia bellirica (Gaertn.) Roxb. 64 Themeda strigosa (Buch.-Ham. ex Hook. f.) A. Camus 65 Triumfetta annua L. 66 Ziziphus rugosa Lam. 67 Zornia gibbosa Span.

Table 15: Species that are exclusive to areas infested with Lantana camara

S. No. Species 1 Acacia caneccens Graham ex Gamble 2 Acacia jacquemontii Benth 3 Alternanthera sessilis (L.) R.Br. ex DC. 4 Alysicarpus monilifer (L.) DC. 5 Alysicarpus vaginalis (L.) DC. 6 Anisomeles indica (L.) Kuntze 7 Aphanamixis polystachya (Wall.) Parker 8 Argyreia hookeri C.B.Clarke 9 Argyreia sericea Dalzell & Gibs. 10 Azadirachta indica A.Juss. 11 Blumea fistulosa (Roxb.) Kurz. 12 Blumea laciniata (Roxb.) DC. 13 Blumea mollis (D.Don) Merr. 14 Breynia retusa (Dennst.) Alston 15 Buchnera hispida Buch.-Ham.ex D. Don 16 Capparis sepiaria L. 17 Chionachne koenigii (Spr.) Thw. 18 Cissus latifolia Lam. 19 Cissus woodrowii (Stapf ex Cooke) Santapau 20 Crotalaria pallida Aiton 21 Cyamopsis tetragonolobus (L.)Taub. 22 Cyperus digitatus Roxb. 23 Desmodium triquetrum (L.) DC. 24 Desmodium umbellatum (L.) DC. 25 Dichanthium assimile (Steud) Deshpande 26 Dioscorea bulbifera L. 27 Diospyros montana Roxb. 28 Diploclisia glaucescens (Blume) Diels 29 Dodonea viscosa (L.) Jacq. 30 Elephantopus scaber L. 31 Embelia basaal (Roem & Schult) DC. 32 Emblica officinalis Gaertn. 33 Eulalia fastigiata (Nees ex Steud.) Haines 34 Ficus amplissima J.E.Sm. 35 Ficus microcarpa L.f. 36 Flacourtia latifolia (Hook. f. & Thoms.) Cooke 37 Glochidion hirsutum (Roxb.) Voigk 38 Microcos paniculata L. 39 Gymnema sylvestre (Retz.) R.Br. 40 Haplanthodes tentaculata (L.) R.B.Majumdar 41 Hemigraphis hirta (Vahl) T. Anderson 42 Heterophragma quadriloculare (Roxb.) K. Schum.

43 Ischaemum indicum (Houtt.) Merr. 44 Iseilema holei Haines 45 Justicia betonica L. 46 Kydia calycina Roxb. 47 Lagerstroemia parviflora Roxb. 48 Leucas ciliata Benth. 49 Leucas eriostoma Hook. f. 50 Memecylon umbellatum Burm. 51 Moullava spicata (Dalzell) Nicolson 53 Naregamia alata Wight & Arn. 54 Oplismenus burmannii (Retz.)P.Beauv. 55 Pimpinella adscendens Dalzell 56 Pseudanthistiria umbellata (Hack.) Hook. f. 57 Rungia pectinata (L.) Nees 58 Securinega leucopyrus (Willd.) Muell.-Arg. 59 Senecio bombayensis N.P.Balakr. 60 Sida cordata (Burm. f.) Borss. 61 Sida rhombifolia L. 62 Smithia conferta Sm. 63 Spodiopogon rhizophorus (Steud.) Pilger 64 Stephania japonica (Thunb.) Miers 65 Syzygium zeylanicum (L.) DC. 66 Themeda tremula (Nees ex Steud.) Hack. 67 Thespesia lampas (Cav.) Dalzell & Gibs. 68 Trewia nudiflora L. 69 Tricholepis glaberrima DC. 70 Triumfetta rhomboidea Jacq. 71 Woodfordia fruticosa (L.) Kurz 72 Wrightia tinctoria R. Br. 73 Xenostegia tridentata (L.) Austin & Staples 74 Xylia xylocarpa (Roxb.) Taub. 75 Zingiber neesanum (J. Graham) Ramamoorthy 76 Ziziphus xylopyra (Retz.) Willd.

Table 16: Species that are common to areas infested with Lantana camara and the adjacent areas

Sr. No. Species 1 Anogeissus latifolia (Roxb.ex DC.) Wall. ex Guillem. & Perr. 2 Bombax ceiba L. 3 Bridelia retusa (L.) Spreng. 4 Bridelia squamosa (Lam.) Gehrm. 5 Butea monosperma (Lam.) Taub. 6 Careya arborea Roxb. 7 Cassia fistula L. 8 Catunaregam spinosa (Thunb.) Tirveng. 9 Dalbergia lanceolaria L. f. 10 Dalbergia latifolia Roxb. 11 Glochidion ellipticum Wight 12 Microcos paniculata L. 13 Holarrhena pubescens (Buch.-Ham.) Wall. 14 Lannea coromandelica (Hautt.) Merr. 15 Miliusa tomentosa (Roxb.) Sinclair. 16 Olea dioica Roxb. 17 Pavetta crassicaulis Bremek. 18 Prunus ceylanica (Wight) Miq. 19 Sapium insigne Bth. 20 Schleichera oleosa (Lour.) Oken 21 Sterculia urens Roxb. 22 Syzygium cumini (L.) Skeels 23 Tabernaemontana alternifolia (Roxb.) Nicols. & Suresh 24 Tectona grandis L. 25 Terminalia chebula Retz. 26 Terminalia elliptica Willd. 27 Terminalia paniculata Roth 28 Pseudanthistiria stocksii (Muntro.) Naithani. 29 Alangium salvifolium subsp. hexapetalum (Lam.) Wang. 30 Arundinella tuberculata Munro ex Lisboa 31 Bridelia scandens (Roxb.) Willd. 32 Carissa congesta Wight 33 Clerodendrum serratum (L.) Moon. 34 Clerodendrum viscosum Vent. 35 Eranthemum roseum (Vahl) R. Br. 36 Eulalia trispicata (Schult.) Henr. 37 Gnidia glauca (Fresen.) Gilg. 38 Helicteres isora L. 39 Mallotus philippensis (Lam.) Muell.-Arg. 40 Neuracanthus trinervius Wight

41 Phyllocephalum scabridum (DC.) Kirkman 42 Pimpinella adscendens Dalzell 43 Pogostemon benghalensis (Burm.f.) Kuntze 44 Senecio belgaumensis (Wight) C.B.Clarke 45 Thunbergia grandiflora (Roxb. ex Rottle.) Roxb. 46 Ziziphus nummularia (Burm. f.) Wight & Arn. 47 Alysicarpus glumaceus (Vahl) DC. 48 Apluda mutica L. 49 Aristida redacta Stapf 50 Arthraxon lanceolatus (Roxb.) Hochst. 51 Arthraxon lancifolius (Thrin.) Hochst. 52 Arundinella metzii Hochst. ex Miq. 53 Arundinella pumila (Hochst.ex A. Rich.) Steud. 54 Chrysopogon orientalis (Desv.) A. Camus 55 Crotalaria filipes Benth 56 Cynarospermum asperrimum (Nees) Vollesen 57 Dioscorea pentaphylla L. 58 Eranthemum capense L. 59 Haplanthodes verticillata (Roxb.) R.B.Majumadar 60 Heteropogon contortus (L.) P. Beauv. ex Roem. & Schult. 61 Impatiens scapiflora Heyne ex Roxb. 62 Ischaemum barbatum Retz. 63 Ischaemum bolei M.R.Almeida 64 Justcia glauca Rottl. 65 Lavandula bipinnata Kuntze 66 Lepidagathis cristata Willd. 67 Leucas stelligera Wall. 68 Malva sylvestris L. 69 Mnesithea granularis (L.) Koning & Sosef 70 Ophiuros bombaiensis Bor 71 Pennisetum alopecuroides (L.) Spr. 72 Scleria lithosperma (L.) Swartz var.linearis Benth. 73 Spermacoce pusilla Wall. 74 Spodiopogon rhizophorus (Steud.) Pilger 75 Thelepogon elegans Roth ex Roem &Schult. 76 Trichodesma inaequale Edgew. 77 Vetiveria lawsonii (Hook. f.) Blatt.& McCann 78 Celastrus paniculatus Willd. 79 Cocculus hirsutus (L.) Theob. 80 Tragia muelleriana Pax et Hoffm.

Table 17: Correlation of vegetation characters in relation to area occupied by Lantana camara

Weed area Canopy Total no. No. of tree No. of spp. (sq. m) Gap (%) of trees spp. (Herbs & Saplings) Weed area 1.00 (sq. m) Canopy -0.22 1.00 Gap (%) Total no. 0.17 0.07 1.00 of trees No. of tree 0.13 -0.01 0.94 1.00 spp. No. of spp. -0.56 0.35 -0.34 -0.25 1 (Herbs & Saplings)

Table 18: Correlation of vegetation characters including undergrowth in relation to area occupied by Lantana camara

No. of Tree Tree of No. spp. of No. Individul GBH Total of No. Seedlingspp. Shrub of No. Spp. Herb of No. Spp. of No. Spp. Climber area Weed No. of Tree spp. 1 No. of Individulas 0.97 1 Total GBH 0.89 0.91 1 No. of Seedling spp. -0.08 -0.08 0.01 1 No. of Shrub Spp. 0.41 0.34 0.34 0.19 1 No. of Herb Spp. -0.57 -0.56 -0.58 -0.11 -0.55 1 No. of Climber Spp. 0.17 0.11 0.3 0.27 0.15 -0.29 1 Weed area 0.16 0.19 0.2 -0.05 0.08 -0.6 0.1 1

Table 19: WorldClim environment variables that define the potential distribution of obnoxious weed L. camara in Northern Western Ghats

Variable Percent contribution Permutation importance Prec1 39 18.8 Prec7 21 0.4 Prec5 14 0.5 Prec11 8 0.5 Prec2 6.6 0 Alt 2.5 0 Bio19 2.4 0.1 Prec12 1.5 0.6 Bio14 1.3 54.1 Bio17 1.2 3.2 Prec4 1.2 0.1 Bio4 0.1 18.3

Table 20: WorldClim environment variables that define the potential distribution of obnoxious weed L. camara in Northern Western Ghats with additional distribution data incorporated from outside study area.

Variable Percent contribution Permutation importance Prec1 30.5 2.2 Prec7 27.1 1.4 Prec11 10.5 0 Bio14 8.8 56.4 Alt 7.6 0 Prec2 5.1 0.1 Prec4 3.2 0 Bio19 2.5 0 Prec12 1.9 1 Bio4 0.3 34.4

Fig. 13: Distribution of Lantana camara based on actual observations in the study area

Fig. 14: Positive correlation between pH of the soil and Lantana camara patch size

Fig. 15: Positive correlation between slope and Lantana camara patch size

Fig. 16: Positive correlation between altitude and Lantana camara patch size

Fig. 17: Negative correlation between canopy gap and Lantana camara patch size

Fig. 18: Distribution of Lantana camara along the altitudinal gradient

Fig. 19: Area occupied by Lantana camara and the number of herbs (species)

(The average % is

ana camara

Lant

infested with -

Herbs and saplings exclusively present in sites infested / non

calculated by calculated working out the of percentage their occupancy in quadrateach divided by the no. of quadrats in which they are present) Fig. 20:

ll ll

(The (The average % is calculated by

Lantana cama

Herbs and saplings predominantly present in sites not infested with

Fig. Fig. 21: working out the percentage of their occupancy in each studied) = quadrats total / occupancy of of no. % average quadrat divided by the no. of quadrats in which they are present; overa

(The (The average % is calculated by

L. camara

drats studied) drats

minantly present in sites infested with

Herbs Herbs and saplings predo

Fig. Fig. 22: working out the percentage of their occupancy in each quadrat divided by of % = qua total / average occupancy of no. overall the no. of quadrats in which they are present;

based on MAXENT model. (a) Minimum, (b) Average, (c)

Lantana Lantana camara

otential distribution of

Maps of p

Maximum. based on study in area. samples 29 are Predictions Fig. 23:

AXENT model. (a) Minimum, (a) model. (b) AXENT

based on M on based

Lantana camara camara Lantana

otentialdistribution of

Maps of p of Maps

Fig.24: Fig.24: Maximum. (c) Average, based on study in area. samples 39 are Predictions

camara Lantana

study in les area.

distribution otential of

p of Maps

Predictions are based on samp 48 are Predictions 25: Fig.

based on MAXENT model. (a) Minimum, (b) Average, (c) Average, (a) model. Minimum, (b) MAXENT on based

Lantana camara camara Lantana

otential distribution otential of

Maps of p of Maps

Fig. 26 : : 26 Fig. Maximum. from far based away samples 5 on area. study and study in area samples 48 are Predictions

based in MAXENT model. Samples: 29 (a,b,c), 39 (d,e,f), 48 (g,h,i) and

Lantana Lantana camara

tial tial distribution of

oten

Grpahs Grpahs of p

7: 7:

Fig. Fig. Maximum(c,f,i,l) k), . Average(b,e,h, outside from samples 5 Prediction: (j,k,l). + 48 area study Minimum (a,d,g,j),

(iii) Parthenium hysterophorus:

A total of 19 localities infested with Parthenium hysterophorus have been recorded from the study area. Some of these localities are very close to each other and are found in Karnataka and Maharashtra (Fig. 28; Table. 21). These localities are generally seen on the eastern side of the Western Ghats in the transition gone to

Deccan Plateau and rain shadow region of the Western Ghats and receive very less rainfall as compared to the western slopes. It is also seen that P. hysterophorus occurs in grasslands and fallow fields rather than forested areas. It corroborates earlier observation made by Raghubanshi et al. (2005), regarding its colonization in areas with poor ground cover and exposed soil. Its fast spread in cultivated fields and grassland was reported nearly half a century ago (Vartak, 1968; Jayachandra, 1971).

In Europe it is reported as second frequent weed in 54% of crop fields studied

(Tamado et al., 2000). Not a single locality with Parthenium infestation has been located in Goa probably due to heavy rainfall, though occasional stray individuals are seen growing.

Soil parameters for sites infested with P. hysterophorus and adjacent non- infested sites are provided in Table 22. The mean values of certain parameters such as pH, P and K show some differences between infested and non-infested sites though high variability as shown by high standard deviation (SD) make them insignificant.

However, mean values of pH and K are lesser in non-infested sites whereas P is less in infested site. Based on the soil parameters studied for nine pairs of quadrats, a dendrogram was constructed which generally separates infested and non-infested sites though with some exceptions (Fig. 29), indicating differences in soil parameters.

Correlation of soil and habitat parameters between infested and non-infested sites is shown in Table 23. Between infested and non-infested sites there is a negative 93

correlation (-0.33) existing with increase in pH in infested sites (Table 23). This shows that P. hysterophorus certainly changes the soil characteristics as established by Timsina et al. (2011).

Presence of P. hysterophorus and its influence on native flora has been studied. It is found that the area is without any canopy cover as noticed by

Raghubanshi et al. (2005). The canopy gap is 100% and it is found that all the associated species are herbaceous. Occupancy of local species both in infested and adjacent non-infested sites show that 15 species are exclusive to areas not infested with P. hysterophorus (Table 24) though it is not clear whether they disappear after the infestation. Similarly 22 species are exclusive to areas infested with P. hysterophorus (Table 25). Some of these species such as Acanthospermum hispidum,

Ageratum conyzoides, Ocimum americanum, Pennisetum alopecuroides and Urena lobata are also considered as weeds. It is not clear whether these herbs pave way for

P. hysterophorus or along with the latter they invade the area.

Some 14 herbs (Table 26) have found to be common to both infested and non- infested sites and some of them such as Hyptis suaveolens and Tridax procumbens are also common weeds. The occupancy percentage of these herbs show that herbs such as Alternanthera bettzichiana, Aristida mutabilis, Eragrostis gangetica, Eragrostis tenella occupy more area in infested sites (Fig. 30) as compared to Aristida stocksii,

Heteropogon contortus, Lophopogon tridentatus which clearly go down in their occupancy in infested areas as compared to non-infested sites. These change in species composition of plants had been recorded by Timsina et al. (2011). In a similar study in Awash National Park of Ethiopia, Etana et al. (2011) recorded 40% of the herbs belonging to Poaceae and Fabaceae and more diverse in non-infested sites. In

the present study, more species ae recorded from infested sites than the non-infested sites. However, certain species such as Aristida stocksii, Dichanthium annulatum and

Lophopogon tridentatus (Fig. 30) loosing their occupancy area in infested sites is similar to the observation of Etana et al. (2011) where in the dominant Tetrapogon tenellus in non-infested sites in Ethiopia getting relegated to second spot after the weed taking the top spot. As found by these authors Poaceae dominates these areas.

As noticed by Timsina et al. (2011) more number of species are noticed in infested areas than the non-infested sites. Herbs such as Heteropogon polystachyos,

Chloris virgata, Chrysopogon serrulatus, Digitaria longiflora, Eulalia fastigiata and

Rhynchelytrum repens which are present only in non-infested sites with good occupancy may be preventing the establishment of P. hysterophorus. Fodder species such as Setaria incrussata, Panicum maxicum and Cenchrus ciliaris were reportedly found to suppress the growth of this weed (Khan et al., 2013, 2014). Whether these exclusive species reported in this study really control the establishment of the weed or otherwise need to be established, though the negative effect of Cassia uniflora on the germination of P. hysterophorus had been established (Joshi, 1991a,b). These species along with C. uniflora can be used as biological agents as seen from earlier efforts using Imperata cylindrical (Anjum et al., 2005) and Cassia sericea (Syamasundar and

Mahadevappa, 1986). In addition fodder species such as Setaria incrussata, Panicum maximum and Cenchrus ciliaris had been found to suppress the growth of Parthenium

(Khan et al., 2013, 2014). P. hysterophorus has also shown to control the native flora with pollen allelopathy on the stigmas (Kanchan & Jaychandra, 1980). However, more number of herbaceous species associated with infested sites in the present study needs further elaborated study.

Based on 10 samples in the study area potential distribution of P. hysterophorus has been modeled using MAXENT as described under materials and methods. The results have been obtained (Fig. 31) and areas with 60% and more probability have been visited to validate the data in the field. Six additional sample sites obtained during the process have been added to the data and further analyzed

(Fig. 32). Process has been repeated and an additional 3 samples thus obtained have been incorporated for the analysis to refine the results (Fig. 33). However, additional six Parthenium sites incorporated from outside the study area for modeling have increased the probable distribution areas enormously (Fig. 34). As discussed by Patil and Janarthanam (2013), the restricted local data will be useful for prediction only at local level (Fig. 31-33). The predictions (Fig. 34a-c-insets) show that at average level only 508 sq. km. has been predicted at 73-91% probability level, that too after incorporating the data from far away areas. It is due to very limited suitable area of

Parthenium comes under the study area and the samples sites are very close by.

Environment variables that contribute to its prediction and permutation importance are (i) BIO4 [Temperature sensitivity (Standard Deviation*100)] and (ii) precipitation in the month of January (Prec1). In addition the larger BIO14

(precipitation of driest month) adds to the permutation importance. Precipitation in the month of January is only in the form of dew and January is the coldest month which are more prominent in Deccan region which is east of the Western Ghats. However, in the studies carried out in Tanzania ( Kija et al., 2013) it is concluded that precipitation of wettest month and mean temperature of coldest quarter as driving factors. It suggests enormous adaptability of weeds to different climatic and environmental conditions.

Table 21: Locality details of Parthenium hysterophorus in the study area Sr. Q. No. Locality District State Longitude Latitude No Adimallayya 1 105 Hill-Grassland, B KA 74°19.830’ 16°31.004’ Tal-Chikodi Adimallayya 2 106 Hill-Grassland, B KA 74°19.843’ 16°30.993’ Tal-Chikodi Adimallayya 3 107 Hill-Grassland, B KA 74°20.351’ 16°30.974’ Tal-Chikodi Adimallayya 4 108 Hill-Grassland, B KA 74°19.964’ 16°31.468’ Tal-Chikodi Adimallayya 5 116 Hill-Grassland, B KA 74°19.567’ 16°31.686’ Tal-Chikodi Adimallayya 6 117 Hill-Grassland, B KA 74°19.958’ 16°30.472’ Tal-Chikodi Adimallayya 7 118 Hill-Grassland, B KA 74°19.851’ 16°31.227’ Tal-Chikodi Adimallayya 8 119 Hill-Grassland, B KA 74°19.450’ 16°30.969’ Tal-Chikodi Adimallayya 9 120 Hill-Grassland, B KA 74°20.054’ 16°31.965’ Tal-Chikodi Kurni Cross Near 10 ### Sankeshwar B KA 74°31.09 16°12.312 Tal- Hukkeri Aralgundi-After 11 Hiranyakeshi ### B KA 74°31.014 16°12.852 River Tal- Hukkeri Near Sankeshwar 12 ### Sugar Factory B KA 74°30.515 16°13.892 Tal- Hukkeri Near Sankeshwar 13 ### Sugar Factory B KA 74°30.133 16°14.313 Tal- Hukkeri Near Wallabhgad 14 ### Sankeshwar B KA 74°27.72 16°16.224 Tal- Hukkeri

Lingnur ### K MH 74°18.479 16°24.919 15 Tal-Kagal

Bastawade ### K MH 74°16.52 16°25.66 16 Tal-Kagal Kurukli ### K MH 74°15.464 16°25.535 17 Tal-Kagal Nidhori ### K MH 74°10.684 16°24.149 18 Tal- Kagal Next to Mudhal 19 ### Titta K MH 74°8.129 16°24.692 Tal- Bhudargad 20 ### Nashik* N MH 73°45.379 19°59.231 21 ### Panvel* R MH 73° 7.358 18°59.417 22 ### Vaitarna* T MH 73°31`52.43 19°47`38.31 23 ### Igatpuri* N MH 73°33`11.27 19°41`53.12 24 ### Nashik* N MH 73°46`06.47 19°57`12.87 25 ### Igatpuri* N MH 73°33.776 19°41.65

*Six sites from outside the study area were used only for comparative prediction using MAXENT KA- Karnataka MH- Maharashtra B- Belgaum K- Kolhapur N- Nasik R- Raigad T- Thane

### Quadrats were not studied for vegetation and soil analysis

Table 22: Soil and habitat parameters of sites infested (I) with Parthenium hysterophorus and adjacent non-infested (NI) sites

Mean (I) Mean (NI) Mean Minimum (I) Minimum (NI) Maximum (I) Maximum (NI) (I) SD (NI) SD pH 7.02 6.41 6.2 6 7.6 6.7 0.51 0.25 EC 0.18 0.16 0.09 0.11 0.25 0.23 0.05 0.04 OC 0.9 0.94 0.52 0.52 1.67 1.55 0.41 0.44 N 0.09 0.09 0.05 0.05 0.17 0.15 0.04 0.04 P 12.54 20.16 0 0 23.8 53.6 9.72 15.74 K 385.78 333.51 212.8 156.8 604.8 560 153.37 155.77 Slope 3.74 5.47 0.1 0.3 12 14 5.01 6.11 Alt 573.08 574.83 560.34 562.96 584.6 582.9 11.15 7.42 Canopy 100 100 100 100 100 100 0 0

1 Alt (NI)

0.33 Alt (I)

0.66 0.61 - - Slope (NI)

1 0.1

- 0.02 0.03 Slope (I)

0.67 0.48 0.18 0.02 - - - K (NI)

0.4 - 0.56 0.15 0.19 0.02 K (I) - -

in infested sites infested in 1

0.2 0.29 0.32 0.44 0.49 0.64 - - - P (NI)

0.2 0.1 - - 0.18 0.16 0.49 0.21 0.25 P (I) - -

0.5 - 0.36 0.16 0.03 0.61 0.31 0.63 0.07 - - - - - N (NI)

0.2 - 0.48 0.37 0.12 0.23 0.79 0.42 0.31 0.27

N (I) - - - Parthenium hysterophorus Parthenium

1 1

0.5 - 0.48 0.36 0.16 0.03 0.61 0.31 0.63 0.07 - - - - - OC (NI)

1 1 1 0.2

- 0.48 0.48 0.37 0.12 0.23 0.79 0.42 0.3 0.27 - - - OC (I)

1 0.3 0.74 0.07 0.74 0.07 0.14 0.33 0.48 0.73 0.16 0.26 0.23 ------EC (NI)

0.45 0.77 0.48 0.77 0.48 0.16 0.21 0.01 0.61 0.47 0.51 0.54 0.35 ------EC (I)

0.1 0.03 0.73 0.47 0.24 0.47 0.24 0.25 0.19 0.32 0.14 0.02 0.37 0.39 ------pH (NI)

1 5 0.2 0.2

- - 0.33 0.68 0.42 0.48 0.1 0.48 0.15 0.12 0.29 0.48 0.17 0.12 0.21 ------

pH (I) Correlation of soil and habitat parameters of of of parameters habitat soilCorrelation and

Table Correlations pH (I) pH (NI) EC(I) EC(NI) OC(I) OC(NI) N(I) N(NI) P(I) P(NI) K(I) K(NI) Slope(I) Slope(NI) Alt(I) Alt(NI)

100

Table 24: Species that are exclusive to areas not infested with Parthenium hysterophorus

1 Aristida redacta Stapf 2 Blumea venkataramanii Rolla Rao & Hemadri 3 Boerhavia erecta L. 4 Boerhavia repens L. 5 Buchnera hispida Buch.-Ham.ex D. Don 6 Chloris virgata Swartz 7 Chrysopogon serrulatus Trin. 8 Cyanotis papilionacea R.&S. 9 Digitaria longiflora (Retz.) Pers. 10 Eragrostis tef (Zucc.) Trotter 11 Eulalia fastigiata (Nees ex Steud.) Haines 12 Heteropogon polystachyos (Roxb.) Schult. 13 Justicia trinervia Vahl 14 Pentanema indicum (L.) Ling 15 Rhynchelytrum repens (Willd.) C.B. Hubb.

101

Table 25: Species that are exclusive to areas infested with Parthenium hysterophorus

1 Acanthospermum hispidum DC. 2 Ageratum conyzoides L.

3 Aristida setacea Retz.

4 Arundinella metzii Hochst. ex Miq. 5 Arundinella spicata Dalz. 6 Corchorus deccanensis H.B.Singh & Vishwanathan 7 Crotolaria hebecarpa (DC.) Rudd 8 Cyperus niveus Retz. 9 Dichanthium pertusum (L.) W.D. Clayton 10 Eragrostis ciliaris (L.) R.Br.

11 Eragrostis tenuifolia Hochst. ex Steud.

12 Euphorbia heyneana Spr. 13 Euphorbia notoptera Boiss. 14 Herissantia crispa (L.) Medik. 15 Ipomoea obscura (L.) Ker-Gawl. 16 Isachne globosa (Thunb.) O. Ktze. 17 Ocimum americanum L. 18 Pennisetum alopecuroides (L.) Spr.

19 Phyllanthus maderaspatensis L.

20 Sida cordata (Burm. f.) Borss. 21 Triumfetta malabarica Koen. 22 Urena lobata L.

Table 26: Species that are common to areas infested with Parthenium and adjacent non- infested areas.

1 Alternanthera bettzichiana (Regel) Nicols. 2 Aristida stocksii (Hook. f.) Domin. 3 Aristida mutabilis Trin.& Rupr. 4 Celosia argentea L. 5 Dichanthium annulatum (Forssk.) Stapf 6 Eragrostis gangetica (Roxb.)Steud. 7 Eragrostis tenella (L.) P.Beauv. ex Roem. & Schult. 8 Evolvulus alsinoides (L.) L. 9 Heteropogon contortus (L.) P. Beauv. ex R. & S. 10 Hyptis suaveolens (L.) Poit. 11 Indigofera aspalathoides Vahl 12 Leucas biflora (Vahl) R. Br. 13 Lophopogon tridentatus (Roxb.) Hack. 14 Tridax procumbens L.

102

Table 27: WorldClim environment variables that define the potential distribution of obnoxious weed P. hysterophorus in Northern Western Ghats.

Variable Percent contribution Permutation importance Bio4 27.8 29 Prec1 26.9 17.2 Prec3 8.2 0.4 Bio2 7.4 0 Prec9 6.5 0 Prec6 6.1 1.3 Prec5 4.3 0 Prec7 2.1 0.5 Bio14 2.1 29.6 Bio19 1.3 0.7 Prec12 1.2 0.6 Bio17 1.1 12.2

Table 28: WorldClim environment variables that define the potential distribution of obnoxious weed P. hysterophorus in Northern Western Ghats; additional distribution data incorporated from outside study area

Variable Percent contribution Permutation importance Bio4 42.2 36.7 Prec1 19.1 23.4 Bio14 7.1 12.5 Bio2 5 1 Prec3 4.3 1.3 Prec2 2.9 3.6 Bio8 2.4 0 Prec7 2.2 0 Bio1 2 0 Prec6 1.8 1.2 Bio13 1.6 0.4 Bio16 1.6 0 Bio6 1.4 1.2 Prec12 1.4 1.2 Bio19 1.3 0 Bio12 0.3 6.5

103

Kolhapur

Sindhudurg

Belgaum

N. Goa

N. Goa S. Goa

S. Goa Uttara Kannada

Parthenium hysterophorus

Western Ghats

Fig. 28: Distribution of Parthenium hysterophorus based on actual observations in the study area

104

sites infected -

Dendrogram showing segregation of quadrats based on soil parameters. Infected and non quadrats showing and soil on of parameters. Infected based Dendrogram segregation

ig 29 ig

F to are close other. number each very spatially same the with

105

(The by calculated is % (The average

infested sites. infested

and adjacent non adjacent and

C.odorata

in with infested sites

Herbs present present Herbs

: :

ig. 30 ig.

F present) divided quadrat are they in the by quadrats which of no. occupancy each in their of out percentage the working

106

(b) m,

(a) model. Minimu based MAXENT on

study in area. samples

hysterophorus Parthenium

distribution otential of

p of Maps :

ig. 31 ig. Average, (c) Maximum. Predictions are based on Maximum. 10 are Predictions (c) Average, F

107

area.

(a) model. Minimum, (b) based MAXENT on

hysterophorus Parthenium

distribution otential of

p of Maps :

ig. 32 ig. Average, (c) Maximum . Predictions are based on Maximum study in samples 14 are Predictions . (c) Average, F

108

(a) model. Minimum, (b) based MAXENT on

hysterophorus Parthenium

distribution otential of

p of Maps

ig. 33: ig. Average, (c) Maximum . Predictions are based on 16 samples in study area. Inset shows the enlargement of study area. of based on enlargement the shows Maximum Inset study in area. samples 16 are Predictions . (c) Average, F

109

based on MAXENT model. (a) Minimum, (b)

Parthenium Parthenium hysterophorus

tial tial distribution of

oten

Maps Maps of p

: :

Fig. Fig. far from areas. based samples nine on away Maximum and study in area samples 16 are Predictions . (c) Average,

110

V. CONCLUSION

It is concluded that Chromolaena odorata, Lanatana camara and Parthenium hysterophorus are found in the study area but occupying different regions as their establishment is defined by difference in the moisture and temperature. Soil parameters are either slightly different or not significantly different between infested and adjacent non-infested sites suggesting that probably there are also other parameters acting on them. Similarly, canopy gap though a parameter, after a threshold level changes other parameters that may be hindering the growth of the weeds. Herbaceous flora both in infested and non-infested sites have unique species, at times more in infested sites. Infestation is found to be altering the species composition as well as their occupancy both positively and negatively depending on the species. MAXENT model is a valuable tool in predicting the potential distribution areas of these weeds with good validation in the field. As the weeds have high adaptability they could be predicted only for local areas with the data from study area. Series of local specific studies are recommended to understand the adaptability of weeds to varying ecological conditions. Series of local specific studies are recommended to understand the adaptability of weeds to varying ecological conditions.

111

VI. SUMMARY

Invasion of exotic species is considered as one of the greatest threats to native biodiversity. In Indian context, there are several weeds that are known for invading different habitats and among them Chromolaena odorata (L.) R.M.King & H.Rob.

(=Eupatorium odoratum L.), Lantana camara L. and Parthenium hysterophorus L. occupy top slots as the worst invaders. In the present study, an attempt has been made to understand the ecology of these three invasive species with regard to the areas of their invasion, parameters that distinguish infested sites from non-infested sites, impact of invasion on native biodiversity, build models based on these parameters to predict invasion in future and validate these models on ground.

The methodology involve intensive field work throughout the study area (Goa,

Maharashtra and Karnataka tri-junction in Western Ghats) for identifying the invaded localities and collecting co-ordinates using GPS, laying quadrats for quantitative vegetation studies, collection and analysis of soil samples for pH, Electrical conductivity (EC), Organic Carbon (OC), Nitrogen (N), Phosphorous (P) and Potassium

(K), study of plant biodiversity in relation to canopy gap, invasion and soil parameters, use of MAXENT modeller using WorldClim data and validation in the field.

Results have shown that:

a) Chromolaena odorata is mostly invading the area at lower altitudes (<200 m

alt.) of Western Ghats on western slopes and higher altitudes (> 600 m alt.).

High rainfall in Western slopes is considered as the main reason; absence of it in

mid altitudes (200 – 600 m alt.) is due to the least disturbance of the area as it

falls under series of Wildlife Sanctuaries. Parameters including that of soil are

112

not significantly different from infested and adjacent non-infested sites though

there is positive correlation between pH and Weed patch size and negative

correlation between pH and canopy gap, and weed area and canopy gap thus

forming a complex set of characters. Species are slightly less in infested sites

and some species have shown higher occupancy in infested sites proving that the

weed alters the biodiversity of invaded areas. MAXENT modeller improved the

prediction with additional data with validation in the field. Precipitation in

January, July and May along with BIO4 [Temperature Seasonality (standard

deviation *100)] are the major deciders of its invasion. The prediction of species

shows only within study area shows that the species has adopted very well for

varying environmental conditions. b) Lantana camara invasion is seen only at higher altitudes above 500 m altitude

with slight overlap with C. odorata. These are outside Goa and rainfall is

slightly lesser than Goa. Soil parameters are not significantly different between

infested and non-infested sites. There is a positive correlation between weed

area on one side and pH, slope and altitude on the other. However, canopy gap

and weed area show slight negative correlation indicating to complex

interactions happening under the canopy gap. Certain species are found to show

increased occupancy in infested sites as compared to non-infested sites.

Similarly there species that are exclusive to infested or non-infested sites.

Further studies are required to understand their role. MAXENT modeller

predicted its distribution only at higher altitude and for local areas. Inability to

predict outside the study area is linked to weeds adaptability to varying

ecological conditions outside the study area.

113 c) Parthenium hysterophorus is found to be occupying drier areas in the study area

which are to the east of Western Ghats. Infested sites are basically grasslands or

open areas and fallow fields. Overlap of P. hysterophorus with C. odorata is nil

and with L. camara is slight. There is a slight difference in soil parameters

between infested and non-infested sites especially in pH. Species richness is

more in infested sites as compared to non-infested sites. Some common species

either increased or decreased their occupancy in infested sites indicating to the

influence of weed on biodiversity. As the sites are very close to each other due

to limited dry area in study area MAXENT could predict only to the local level.

Additional sites far away from the study area improved the prediction of

probable area, again indicating as in earlier cases enormous adaptability of the

weeds.

Series of local specific studies are recommended to understand the adaptability of weeds to varying ecological conditions.

114

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PUBLICATION

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Indian Journal of Weed Science 45(4): 267–272, 2013

Distribution of some obnoxious weeds in north-western Ghats of India Bharat B. Patil and Malapati K. Janarthanam* Department of Botany, Goa University, Goa 403 206 Received: 8 September 2013; Revised: 11 December 2013 ABSTRACT Flora and fauna of Western Ghats, a biodiversity hot spot are under major threat due to various factors. Invasion of exotic species has been considered as one of the major threat in the area. In the present study, potential distribution of three obnoxious weeds, viz. Chromolaena odorata, Lantana camara and Parthenium hysterophorus was modeled using 32 environmental variables and MAXENT modeller. These three species showed distinct potential distribution patterns with only slight overlap between C. odorata and L. camara, and between L. camara and P. hysterophorus. Overlap of the former pair was seen mostly along the wet western slopes of Western Ghats, and latter along the eastern, rain shade dry areas. The environmental variables that contributed to the model showed that it was basically precipitation and temperature seasonality that defined their distribution. It was interpreted that the weeds might have adapted to different sets of environmental conditions throughout their distributional range; and hence, the variables operating in the study area contributing to the model may not be useful in predicting their presence elsewhere. It is concluded that to understand the full adaptability of these weeds, environmental variables can be studied at local levels and the results compiled for larger areas to get the full spectrum. Key words: Chromolaena odorata, Ecology amplitude, Lantana camara, Parthenium hysterophorus

Invasive species are considered among the greatest percentage have used models as predictive tool (Freckleton threats to native biological diversity and functioning of and Stephens 2009). Wang and Wang (2006) applied eco- natural ecosystems. Bioinvasion is homogenizing the logical niche models to predict potential invasion areas of worlds flora and fauna (McKinney and Lockwood 1999, Ageratina adenophora in China by indicating favourable Baiser and Lockwood 2011), altering the biogeochemical and less favourable areas. Mandle et al. (2010) developed cycles (Strayer et al. 2006) and is recognized as a pri- ecological niche models for both the native and introduced mary cause of global biodiversity loss (Czech and Krausman ranges using MAXENT and used them to explore the ques- 1997, Wilcove et al. 1998) and species extinction (di Castri tion of expansion in greater detail. In the present study, an 1989). Millenium Ecosystem Assessment (2003) consid- attempt has been made to understand the potential distri- ered climate change along with invasive species as the bution of three obnoxious weeds, viz. Chromolaena most pervasive forms of ecosystem disturbance. Foxcroft odorata, Lantana camara and Parthenium hysterophorus et al. (2009) gained insight into broad patterns of invasion using BIOCLIM data and MAXENT model. in Southern Kruger National Park and found that at that MATERIALS AND METHODS scale invasion was over-estimated, though it was useful for determining current and potential species distribution Western Ghats is a hill range that runs north to south over a wider land scale. As the understanding of geographic for about 1600 km parallel to the west coast of India. range is considered as an ecological challenge, important Along with Sri Lanka, it forms one of the 34 biodiversity tools such as bioclimatic models, ecological niche models hotspots. It is divided into northern, central and southern and species distribution models have been used in the study Western Ghats. The study area is north-western Ghats of their geographic range (Jeschke and Strayer 2008). but is restricted to the state of Goa and north-western Karnataka and south-western Maharashtra. The area ex- Usefulness of bioclimatic models has been well es- tends from Deccan plateau in the east to the west coast tablished in inferring the full geographic range when dis- through the mountains of Western Ghats. The sampling tributional information available is scanty (Walther et al. area was between 73.7º-74.9º E and 14.9º-16.7º N (Fig. 1). 2005, Pearson et al. 2007). In spite of proven values of applying models for the distribution of weeds, only a small Field trips were carried out from June 2007 to May 2011 to record the distribution of populations of three *Corresponding author: [email protected] weeds, viz. C. odorata, L. camara and P. hysterophorus

267 Distribution of some obnoxious weeds in north-western Ghats of India using GARMIN GPS 12. Total 104 occurrences were re- Kriticos et al. (2005) using CLIMEX. Based on earlier corded for C. odorata, 48 for L. camara and 20 for P. data and compilation the distribution of C. odorata has hysterophorus. Additional distributional data points for L. been shown all along the Western Ghats (McFadyen 2003, camara (4 points) and P. hysterophorus (6 points) were Kriticos et al. 2005, Muniappan et al. 2005). In the present collected from outside the study area and incorporated study distribution data has been collected from a small for analysis to test the effectiveness of local versus addi- segment of Western Ghats and using BIOCLIM layers and tional data in prediction of potential distribution of weeds. MAXENT potential distribution has been modelled. The MAXENT software (version 3.3.3e) was used for results showed that its potential distribution starts from modeling environmental variables at 30 arc-seconds reso- coastal areas and extends up to the hilly regions of West- lution (~1 km) were downloaded from WorldClim (http:// ern Ghats in Goa and in border areas of Karnataka and www.worldclim.org). One altitude layer, 12 monthly pre- Maharashtra states; the potential distribution is predicted cipitation layers and the following 19 bioclim variables were only to the hilly areas towards north and south (Fig. 2). used in the study: BIO1 [annual mean temperature], BIO2 As its presence has been well documented throughout the [mean diurnal range (mean of monthly (max temp - min Western Ghats, the model was not able to predict its pres- temp))], BIO3 [isothermality (BIO2/BIO7) (*100)], BIO4 ence for a larger area based on local data. Even the maxi- [temperature seasonality (standard deviation *100)], BIO5 mum distribution (not shown here) as predicted by the [max temperature of warmest month], BIO6 [min tem- model increased its potential distribution only marginally. perature of coldest month], BIO7 [temperature annual The skewed distribution towards the coast in Goa range (BIO5-BIO6)], BIO8 [mean temperature of wettest can be attributed to the hilly undulating terrain that ex- quarter], BIO9 [mean temperature of driest quarter], tends almost to the coast of Goa with good rainfall and BIO10 [mean temperature of warmest quarter], BIO11 vegetation. As C. odorata is known to have preferences [mean temperature of coldest quarter], BIO12 [annual for humid and wet conditions that is provided by mon- precipitation], BIO13 [precipitation of wettest month], soon with longer days (Zachariades et al. 2009), thus ex- BIO14 [precipitation of driest month], BIO15 [precipita- plaining its distribution towards the western side of West- tion seasonality (coefficient of variation)], BIO16 [pre- ern Ghats. This is reflected in the model as nearly 90% of cipitation of wettest quarter], BIO17 [precipitation of dri- the prediction is contributed by three parameters, viz. pre- est quarter], BIO18 [precipitation of warmest quarter], cipitation in the month of January, May and July (Table BIO19 [precipitation of coldest quarter]. MAXENT model 1). In January, the precipitation is only through dew, and was run using random seed with random test percentage July is the heaviest rainfall month in the region. In addi- of 30. Five replicates with replicated run type as Boot- tion, permutation importance of various layers showed strap and 500 as maximum iterations were used for the that BIO4 is 61.1% followed by precipitation in January model. Output format was set as cumulative and output and driest months (22.3 and 9.3%), respectively. How- file type saved as asc. Coordinates collected from the field ever, the rainfall requirement seemed to be different in using GPS for the presence of weeds was used as sample different geographical regions (Zachariades et al. 2009), file. Percent contribution and permutation importance of which suggested that the model used here may not be each variable and map generated for minimum prediction fitting elsewhere. (to avoid over estimation) were considered for interpreta- Lantana camara tion. Potential areas predicted by the model were checked for their presence to validate the prediction. The potential This is considered as a weed of international significance due to its impact on agriculture, forestry and distribution of invasive species has been modeled based on several modelling software (Chejara et al. 2010). biodiversity (Sharma et al. 2005). The potential distribution of L. camara in study area is predicted along the hilly RESULTS AND DISCUSSION areas (Fig. 3). The environmental variables that contrib- In the present study, we predicted the potential distri- uted to the model are basically precipitation in the months bution of three invasive weeds, viz. C. odorata, L. camara of January, May and July as in the case of C. odorata. and P. hysterophorus based on actual presence data of popu- Apart from precipitation of various months, altitude seems lations in the field and building models using MAXENT. to be contributing substantially. The permutation impor- Chromolaena odorata tance is basically provided by three layers of data, viz. BIO14 (precipitation of driest month), followed by pre- Potential distribution of C. odorata on large scale has cipitation in January and BIO4 (Table 1). These variables been attempted by McFadyen and Skarratt (1996) and

268 Bharat B. Patil and Malapati K. Janarthanam

Table 1. WorldClim environment variables that de- were generally in agreement with some of those used by fine the potential distribution of three ob- Li (2011) while predicting potential distribution of L. noxious weeds in Western Ghats camara in China.

Variable Percent contribution Permutation importance Additional four data points collected from outside the study area did not alter the potential distribution signifi- Chromolaena odorata Prec1 41.3 22.3 cantly (Fig. 4). However, precipitation in the month of Prec7 27.7 1.7 May (Prec 5) has not contributed to the model as com- Prec5 21.6 0.3 Bio19 2.5 0.1 pared to the data from study area alone. Permutation im- Prec12 2.2 0.6 portance has changed substantially in favour of BIO4 while Bio4 1.3 61.1 reducing that of Prec1 (Table 1). The model could not Bio14 0.4 9.3 Lantana camara predict its distribution in larger area, as in the case of C. Prec1 39 18.8 odorata. It is adapted to grow in wide climatic conditions Prec7 21 0.4 Prec5 14 0.5 (Day et al. 2003), hence it was not the same climatic Prec11 8 0.5 factors that were contributing to its distribution in differ- Prec2 6.6 0 ent areas, thus rendering the model only locally applicable. Alt 2.5 0 Bio19 2.4 0.1 Parthenium hysterophorus Prec12 1.5 0.6 Bio14 1.3 54.1 The resulting image showed that its distribution along Bio17 1.2 3.2 Prec4 1.2 0.1 the coastal area was almost nil and most of its potential Bio4 0.1 18.3 distribution was shown on the eastern side of Western Lantana camara (with additional distribution data incorporated Ghats (Fig. 5). Twelve environmental variables contrib- from outside study area) Prec1 30.5 2.2 uted 1% or more to the model of which BIO4 and precipi- Prec7 27.1 1.4 tation in January contributed 54.7% to the model. Permu- Prec11 10.5 0 Bio14 8.8 56.4 tation importance to the model was provided by BIO14 in Alt 7.6 0 addition to BIO4, Prec1 and BIO17 (Table 1). Prec2 5.1 0.1 Prec4 3.2 0 The prediction extends the potential distribution to- Bio19 2.5 0 wards east into drier areas and substantially into north and Prec12 1.9 1 Bio4 0.3 34.4 south with the addition of six data points from outside the Parthenium hysterophorus study area (Fig. 6). Dhileepan and Senaratne (2009) ear- Bio4 27.8 29 Prec1 26.9 17.2 lier documented 495 recorded sites for this species in In- Prec3 8.2 0.4 dia and used CLIMEX to develop a model in which heat Bio2 7.4 0 stress and temperature have rendered high value to the Prec9 6.5 0 Prec6 6.1 1.3 model. High Eco-climatic Index (EI) values have been Prec5 4.3 0 predicted for the Deccan plateau and east coast. Prec7 2.1 0.5 Bio14 2.1 29.6 McConnachie et al. (2011) in their study concluded that Bio19 1.3 0.7 modeled distribution in South Asia was in agreement with Prec12 1.2 0.6 the available distribution data. Comparison of environmental Bio17 1.1 12.2 Parthenium hysterophorus (with additional distribution data variables that contributed to them showed that BIO4 was incorporated from outside study area) the major factor followed by precipitation in January and Bio4 42.2 36.7 Prec1 19.1 23.4 of driest month. The same factors, especially the former Bio14 7.1 12.5 one in the form of heat stress and temperature have been Bio2 5 1 identified by Dhileepan and Senaratne (2009). Prec3 4.3 1.3 Prec2 2.9 3.6 Within the study area, slight overlapping in distribu- Bio8 2.4 0 Prec7 2.2 0 tion was predicted between C. odorata and L. camara at Bio1 2 0 one side and L. camara and P. hysterophorus on the other Prec6 1.8 1.2 Bio13 1.6 0.4 side, but not between C. odorata and P. hysterophorus. Bio16 1.6 0 In the cases of L. camara and P. hysterophorus, the dis- Bio6 1.4 1.2 tribution was not predicted along the western coastal Prec12 1.4 1.2 Bio19 1.3 0 regions. Bio12 0.3 6.5

269 Distribution of some obnoxious weeds in north-western Ghats of India

Fig. 1. Map of peninsular India showing the study Fig. 2. Potential distribution of Chromolaena odorata area predicted using MAXENT and distributional data from study area

Fig. 3. Potential distribution of Lantana camara pre- Fig. 4. Potential distribution of Lantana camara predicted using MAXENT and distributional data dicted using MAXENT and distributional data from study area from study area as well as from outside study area In all the above three cases, distribution was not pre- adapted to different environmental conditions. Hence, it dicted for south-western Ghats or the areas east of it was safe to work for smaller geographical areas and com- wherein their distribution is documented. This proved that pile the data to understand the environmental variables that local data may not be useful to predict the weeds even in contribute to their presence in different areas. the same phyto-geographical zone as these weeds are well

270 Bharat B. Patil and Malapati K. Janarthanam

Fig. 5. Potential distribution of Parthenium Fig. 6. Potential distribution of Parthenium hysterophorus predicted using MAXENT and hysterophorus predicted using MAXENT and distributional data from study area distributional data from study area as well as from outside study area

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