A STUDY ON BIODIVERSITY OF ARTHROPODS IN THE CROPLAND OF CENTRAL AND LOWER PUNJAB, PAKISTAN

By

TAHIRA RUBY M. Phil. (UAF)

A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY IN ZOOLOGY DEPARTMENT OF ZOOLOGY & FISHERIES FACULTY OF SCIENCES UNIVERSITY OF AGRICULTURE FAISALABAD PAKISTAN 2010

To

The Controller of Examinations, University of Agriculture, Faisalabad.

“We, the Supervisory Committee, certify that the content and form of thesis submitted by Miss Tahira Ruby, 2003-ag-378, have been found satisfactory and recommend that it be processed for evaluation by the external Examiner (s) for the award the degree”

Supervisory Committee

1. Chairman ------Prof. Dr. Shahnaz A. Rana

2. Member ------Dr. Muhammad Afzal

3. Member ------Dr. Mansoor Hameed

DEDICATED

To

My

DEAR “MOTHER”

DECLARATION

I hereby declare that the contents of the thesis, “A study on biodiversity of arthropods in the cropland of central and lower Punjab, Pakistan” are product of my own research and no part has been copied from any publishes source (except the references, standard mathematical or genetic model/ equations/ formulate/ protocols etc.). I further declare that this work has not been submitted for award of any other diploma/ degree. The

University may take action if the information provided is found incorrect at any stage, (In case of any default the scholar will be proceeded against as per HEC plagiarism policy).

Signature of the student

Name: Tahira Ruby

Regd. No. 2003-ag-378

ACKNOWLEDGEMENTS

With a deepest vehement of gratitude I regard vigorous tribute to Prof. Dr. Shahnaz Akhtar Rana, Dept. of Zoology and Fisheries, University of Agriculture, Faisalabad for her kind behaviour, dynamic supervision and propitious guidance. Even I am unable to find appropriate words for her. I feel much honour to express deep sense of gratitude to the members of supervisory committee Dr. Muhammad Afzal and Dr. Mansoor Hameed for their cooperative attitudes as well as conceptual and technical insights into my thesis work. My research work, thesis write up and many other technical formalities were incomplete without the help of Prof. Dr. Aleem Ahmad Khan, Institute of Pure and Applied Biology, Bahauddin Zakariya University, Multan; Prof. Dr. Munir Ahmad Shaikh, Dept. of Chemistry and Biochemistry, UAF and Dr. Safdar Ali, Dept. of Animal Breeding and Genetics, UAF. Thanks to all for their valuable share. Also I am grateful to Mrs. Naureen Rana, Lecturer, Dept. of Zoology & Fisheries, UAF and Dr. Javaid Iqbal Siddiqui, Associate Professor, Govt. Islamia Degree College, Fsd for their constructive criticism and moral support. I express my gratitude to all the teachers of the department for their help and cooperation to facilitate my tasks. It is a matter of great pride to pay tribute to all my friends especially, Nargis Naz, Mubashara Talal, Asifa Obaid, Asma Rashid, Munazza Naz, Beenish Mahboob, Ismat Bibi, Shaheera Batool and Farhana Noureen. They will remain with me forever in my heart. Special thanks to my class fellows, seniors, juniors, cousins and sir Ikram Naumani for lovely wishes and encouragement at each step. All my achievements were incomplete without prayers of Khuzema Tanveer, Rawa Khan, Safi Khan, Ahmad Mustafa and Usama Shehzore. May Allah bless them all with success and happiness. No words can match the actual ability, sincerity and care of my most loving friends Saba Tariq, Sadia Naveed and Misbah Tanveer. Last, but not any acknowledgement could never adequately express my obligation to loving Parents and family members for their spiritual inspiration and guidance, who always motivated me to carry myself through the noble ideas of life. Their support has been unconditional all these years. Much of what I am and a large amount of all I have done, are on their account.

Tahira Ruby

i TABLE OF CONTENTS

Chapter # Title Page # Acknowledgement i List of Tables ii List of Figures iii List of Appendices v 1 Introduction 1 2 Review of Literature 9 References 20 3 Biodiversity of foliage arthropods in the mixed-crop zone 30 and cotton wheat zone in Punjab province, Pakistan Abstract 30 Introduction 30 Materials and Methods 32 Results and Discussion 33 Conclusion 42 References 43 4 Weeds as life source for different arthropods in the cropland 88 of Punjab, Pakistan Abstract 88 Introduction 89 Materials and Methods 90 Results 91 Discussion 95 Conclusion 96 References 97 5 Predator-prey associations among the selected arthropods in 109 the cropland of Punjab, Pakistan Abstract 109 Introduction 110 Materials and Methods 112 Results 113 Discussion 114 Conclusion 116 References 117 6 Determination of genetic diversity in some selected 128 arthropod species using RAPD (Random Amplified Polymorphic DNA) and predator-prey relationship by PCR (Polymerase Chain Reaction, Gut Analysis) Abstract 128 Introduction 129 Materials and Methods 130 Results 132 Discussion 134 Conclusion 135 References 136 7 Summary 144 LIST OF TABLES

Table # Title Page # Chapter 3 1 Comparison of species abundance and richness of Odonata 45 in two zones 2 Comparison of species abundance and richness of 46 Orthoptera in two zones 3 Comparison of species abundance and richness of 49 Hemiptera in two zones 4 Comparison of species abundance and richness of 50 Coleoptera in two zones 5 Comparison of species abundance and richness of 52 Lepidoptera in two zones 6 Comparison of species abundance and richness of Diptera 54 in two zones 7 Comparison of species abundance and richness of 55 Hymenoptera in two zones 8 Comparison of species abundance and richness of Araneae 56 in two zones 9 Comparison of species abundance and richness of Others 58 in two zones 10 Shannon Diversity Index among different orders of mixed 59 crop zone and cotton-wheat zone 11 Multiple linear regression showing impact of 60 environmental factors on different faunal orders 12 List of major pests observed in the cropland of (Punjab, 61 Pakistan) 13 List of minor pests observed in the cropland of (Punjab, 62 Pakistan) Chapter 4 1 Comparison of weed species reported from four crops of 99 central Punjab 2 Comparison of weed species reported from four crops of 100 lower Punjab Chapter 5 1 Chi-square test showing the significance of horizontal 119 linear association of selected predator and prey species in the cropland of Punjab Chapter 6 1 List of RAPD primers applied on arthropods along with 138 their sequence, total number of bands, number of polymorphic bands and mean band frequency 2 Accession number, primer sequence and amplified 139 fragment size of species specific primers of different aphid species LIST OF FIGURES

Figure # Title Page # Chapter 3 1 Diversity of arthropod species in four crops of Faisalabad 64 (MCZ) and Multan(CWZ) 2 Cluster analysis based on Euclidean distances showing 65 habitat preferences by faunal orders in different crop combinations in Faisalabad (MCZ) and Mulatn (CWZ) 3 a. Trophic structure of fauna in the sugarcane crop (MCZ) 66 b. Trophic structure of fauna in the fodder crop (MCZ) 67 c. Trophic structure of fauna in the wheat crop (MCZ) 68 d. Trophic structure of fauna in the mustard crop (MCZ) 69 4 a. Trophic structure of fauna in the sugarcane crop (CWZ) 70 b. Trophic structure of fauna in the fodder crop ( CWZ) 71 c. Trophic structure of fauna in the wheat crop ( CWZ) 72 d. Trophic structure of fauna in the mustard crop ( CWZ) 73 Chapter 4 1 CCA ordination showing the distribution of arthropod 101 species on different weeds of sugarcane crop in Central Punjab 2 CCA ordination showing the distribution of arthropod 102 species on different weeds of fodder crop in Central Punjab 3 CCA ordination showing the distribution of arthropod 103 species on different weeds of wheat crop in Central Punjab 4 CCA ordination showing the distribution of arthropod 104 species on different weeds of mustard crop in Central Punjab 5 CCA ordination showing the distribution of arthropod 105 species on different weeds of sugarcane crop in Lower Punjab 6 CCA ordination showing the distribution of arthropod 106 species on different weeds of fodder crop in Lower Punjab 7 CCA ordination showing the distribution of arthropod 107 species on different weeds of wheat crop in Lower Punjab 8 CCA ordination showing the distribution of arthropod 108 species on different weeds of mustard crop in Lower Punjab Chapter 5 1 Predator-prey ratio of Coccinella septumpunctata with 120 different available preys 2 Predator-prey ratio of Cheilomenes sexmaculata with 121 different available preys 3 Predator-prey ratio of Hippodemia convergens with 121 different available preys

4 Predator-prey ratio of Hippodemia variegata with different 122 available preys 5 Predator-prey ratio of Chrysoperla carnea with different 122 available preys 6 Predator-prey ratio of Oxyopes javanus with different 123 available preys 7 Predator-prey ratio of Neoscona theisi with different 123 available preys 8 Predator-prey ratio of Araneidae nymph with different 124 available preys Chapter 6 1 Polymorphic RAPD banding pattern of twelve arthropod species 140 2 Polymorphic RAPD banding pattern of twelve arthropod species 140 showing unique bands of M. miscanthi 3 UPGMA dendrogram illustrating the genetic relationship among 141 seven arthropod predator species based on their similarities 4 UPGMA dendrogram illustrating the genetic relationship among 141 five arthropod prey species based on their similarities 5 UPGMA dendrogram illustrating the genetic relationship of 142 twelve arthropod predator and prey species based on their similarities 6 PCR amplification of C. septumpunctata and O. javanus 142 fed A. maidis 7 PCR amplification of H. convergens fed S.graminum 143 8 PCR amplification of N. theisi fed D. noxia 143

LIST OF APPENDICES

Appendix # Title Page # I Map of Province Punjab, Pakistan showing two zones viz. 74 Faisalabad representing (Central Punjab) and Multan representing (Lower Punjab) II Map of Faisalabad (Mixed crop zone): Area marked with 75 dots showing the sampling sites of this zone III Map of Multan (Cotton-Wheat zone): Area marked with 76 dots showing the sampling sites of this zone IV Monthwise meterological data for the year 2007 of two 77 crops (Sugarcane, Fodder) in Faisalabad V Monthwise meterological data for the year 2007-08 of two 77 crops (Wheat, Mustard) in Faisalabad VI Monthwise meterological data for the year 2007 of two 78 crops (Sugarcane, Fodder) in Multan VII Monthwise meterological data for the year 2007-08 of two 78 crops (Wheat, Mustard) in Multan VIII Multiple Linear Regression of PCA components for SUs 79 derived from an eigenanalysis of species data (Order Odonata) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity)taken in these units. IX Multiple Linear Regression of PCA components for SUs 80 derived from an eigenanalysis of species data (Order Orthoptera) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity)taken in these units. X Multiple Linear Regression of PCA components for SUs 81 derived from an eigenanalysis of species data (Order Hemiptera) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity)taken in these units. XI Multiple Linear Regression of PCA components for SUs 82 derived from an eigenanalysis of species data (Order Coleoptera) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity)taken in these units. XII Multiple Linear Regression of PCA components for SUs 83 derived from an eigenanalysis of species data (Order Lepidoptera) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity)taken in these units. XIII Multiple Linear Regression of PCA components for SUs 84 derived from an eigenanalysis of species data (Order Diptera) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity)taken in these units. XIV Multiple Linear Regression of PCA components for SUs 85 derived from an eigenanalysis of species data (Order Hymenoptera) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity)taken in these units. XV Multiple Linear Regression of PCA components for SUs 86 derived from an eigenanalysis of species data (Order Araneae) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity)taken in these units. XVI Multiple Linear Regression of PCA components for SUs 87 derived from an eigenanalysis of species data (Others) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity)taken in these units. XVII Monthly abundance data and Chi-square test showing the 125 association among selected predator and prey species in the cropland of Punjab

Chapter 1

INTRODUCTION Loss of biodiversity is one of the major causes leading to environmental degradation. Among many other factors the agricultural intensification in particular is responsible for this threat. Threats to agricultural biodiversity are actually an indirect alarm for sustainable agriculture and are very real (Mulvany, 2002). With the increase in population, there is more demand for food and it shows the importance of agricultural intensification. To improve the crop yield by using fertilizers and pesticides resulted in contamination and disturbance in natural ecosystems, ultimately harming biodiversity and community health (Hughes et al. 2002). One of the key features of agricultural intensification is the crop specialization in the production process, resulting in reduction in number of species, and it often leads to monoculture. Monoculture makes a system unstable and increases the chances of pest attack on crop thus leads to collapse of the crop (Olfert et al. 1999a). Problems to economic viability, soil fertility and arthropod diversity are the major issues facing in agriculture almost all over the world. Management strategies like crop diversification and reduced inputs are being promoted as solution for the stability and the sustainability of the agro ecosystems (Olfert et al. 1999b). Stability refers to constancy of a system in which pest populations are kept at the levels below those causing economic crop losses (Way and Heong, 1994) whereas, sustainability is the consistent viability of the agro ecosystem mostly dependent on in-farm resources rather than those of off-farm. The in-farm resources include crop residues, plant diversity, balanced prey/pest-predator population interactions and viable food web. Lots of research has been conducted on agricultural chemicals in past few decades, resulting into modern conventional farming systems. This is a type of production system that implies pre- and post- tillage practices, synthetic fertilizers and pesticides, characterized with high degree of crop specialization. In developing countries, this farming system comes under “Green Revolution” which began in 1960s (Naylor, 1996). Various negative environmental impacts were utilized in this remarkable success. Soil erosion and degradation, chemical pollution, pollution of surface and underground water, deforestation

1 and loss of many plant and animal taxa are the tarnished impacts of this conventional faming system. The highest yield obtained by larger inputs of fertilizers, high priced pesticides, herbicides and other chemicals in most places of the world have crossed the point of diminishing return. Therefore, use of these larger inputs is less productive. Moreover, at this stage environmental perturbation is of serious concern (Altieri, 1999). During the past two decades, interests in organic farming have attained popularity because of high costs of chemicals and commodity prices have stagnated therefore, farmers are in search of ways to reduce input costs. This farming system increases biodiversity and benefit microorganisms and soil arthropods that prey upon many insect pests. There is a significant interaction between biodiversity and farming system related to well adapted traditional and organic farming systems (Pfiffner, 2000). Organic farming system depends upon crop rotation, crop residues, animal manures, green manures, off-farm organic wastes, mechanical cultivations, aspects of biological control and ecological pest management, to maintain soil health, to supply plant nutrients, to control insects, manage weeds and other pests. Organic farming or eco-friendly farming is actually a self regulating, more diversified and sustainable chemical free crop production system. Organically farmed soils absorb rain water providing protection against drought (Cacek, 1984). In agro-ecosystems, biodiversity performs a variety of ecological services in addition to production of food, recycling of nutrients, regulation of microclimate and local hydrological processes, suppression of undesirable organisms and detoxification of noxious chemicals. Biodiversity mediated renewal processes are largely biological and depends upon the maintenance of diversity in agro-ecosystem (Alteiri, 1999). Although the agro- ecosystems are typically not managed in isolation from other natural ecosystems within a region, the physical, ecological and biochemical changes taking place within them have numerous influences on adjacent ecosystems. Similarly the neighboring ecosystems can influence agro-ecosystems. (Olfert et al. 2002) highlight the importance of arthropods. According to them, arthropod fauna is integral during evaluation of ongoing cropping practice and helps in redesigning of farming systems in order to make it economically viable and environment sustainable. It is now an established fact that arthropod predators suppress pest populations (Chang and Kareiva, 1999; Gurr and Wratten, 2000; Symondson et al. 2002). There are

2 evidences that species-rich ecosystems are more stable than species-poor ecosystems. If the relationship between biodiversity and stability holds, then it is in the interest of the long- term viability of a region to encourage diverse human and natural ecosystems (Minor, 2005). To diversify an agro-ecosystem by traditional means weeds help in increasing the diversity by lowering the damage level of phytophagous species by exploiting the inter specific competition among pest and non-pest species and thus improving natural prey- predator balance. Careful observations regarding organisms associated with these floral species provide information about the sustainability of the cropping system (Hyvonen and Huusela-Veistola, 2008). Weeds can also be used as an indicator group of an agro- ecosystem. They have a positive impact on the below-ground microbial biomass and especially on Associated Mycorrhizal Fungi (AMF) thus increasing the crop’s nutrient uptake efficiency (Douds and Millner 1999). Furthermore, few weed species that were not found important for beneficial animal groups were found to be important for phytophagous insects thus lessening the chances of attack on crop plants. Traditionally maintained vegetation patches supported higher weed populations where such patches are present they also colonized by many arthropods. The response of arthropod groups to vegetation cover (bare ground, litter, crop cover, broadleaf weed cover and grass cover) is very important in studying a sustainable crop system, its faunal community composition and components of the vegetation. Even where weed cover was relatively low, some relationships between arthropods and vegetation were seen (Jonsen and Fahrig, 1997). Generally weeds are considered as undesired plant species competing with the crop plants for their nutrients and thus reducing crop production. Contrary to this the ecological concern is that they have a functional role within the agro-ecosystem. Weeds providing diversity may play an important role being companion of primary producers, sharing the foundation trophic level of energy pyramid of crop systems. Arable weed species support a high diversity of insect species. Reduction or extinction of such associated insects or other taxa may help perturbation resulting in collapse of crop ecosystem by pest outbreak in the absence of natural and potential predator taxa (Marshall, 2001). Weeds also provide other ecosystem resources for phytophagous insects and indirectly serve zoophagous beneficial arthropod species when their preferred crop host is absent (Norris and Kogan, 2005).

3 Phytomorphic heterogeneity provides greater diversity of potential niches for organisms in the cropland. They indirectly effect crop via, their influence on beneficial insects. Use of plants by insects is a dynamic interaction, with characteristics of the insects (e.g. mandible structure) and plant (e.g. allelochemicals) affecting feeding behaviour. Thus weeds are closely related to crop and are particularly important in harbouring insects that attack crops (Capinera, 2005). Insect predator-prey relationship is playing a key role in the stability of an ecosystem by maintaining many natural processes. Beneficial arthropods, including predators and parasitoids provide valuable ecosystem services to agriculture. These arthropod-mediated ecosystem services (AMES) include crop pollination and pest control, which help maintain agricultural productivity and reduce the need of pesticides. Maximizing survival and reproduction of beneficial arthropods requires provision of pollen and nectar resources that are often scarce in modern agricultural landscapes (Isaacs et al. 2009). Trophic relations play a major role in structuring the faunal assemblages and probably largely determine local species abundance (Tscharntke and Hawkins, 2004). The seasonal distribution of a species may be linked to its status in the food web and also on traits like body size, resource specialization, and population size variability. Effects of habitat loss and habitat fragmentation on plant-herbivore, herbivore-predator, as well as plant-pollinator interactions are dependent on species and landscape (Tscharntke and Brandl, 2003). No insect population exists as an isolated entity. Rather, in any location there are many populations of organisms that interact in many different ways. Interactions are usually described according to their beneficial, harmful or neutral effects. In agro-ecosystem the interactions in which ecologists are interested involve feeding interactions among individuals in a population, and in turn fed upon by individuals of other populations (Tscharntke and Hawkins, 2004). Co-existence of species in the same environment is a result of a key component of diet and feeding behaviour among other life resources. How a predator use all available dietary resources is important for predator-prey dynamics (Bonsall and Hassel, 1997). Prey defense can also be a stabilizing factor in predator-prey interactions. Predation can be a strong agent of natural selection. Predation, while not the only cause of complex community interactions, has often been shown to have strong indirect and

4 cascading effects as the availability of an alternate prey can be stabilizing or destabilizing (Purves et al. 2000). Predation may increase the biodiversity of communities by preventing a single species from becoming dominant. Such predators are known as keystone species and may have a profound influence on the balance of organisms in a particular ecosystem. On a higher level of organization, populations of predator and prey species also interact. It is obvious that predators depend on prey for survival, and this is reflected in predator populations being affected by changes in prey populations. The population dynamics of predator-prey interactions moves in cyclic form. Predators may be put to use in conservation efforts to control introduced species. Besides their use in conservation biology, predators are also important for controlling pests in agriculture. Natural predators are an environmentally friendly and sustainable way of reducing damage to crops, and are one alternative to the use of chemical agents such as pesticides (Stanley, 2008). Predation data may be obtained by direct field observation, by mass rearing studies in laboratory, by experimental field manipulations or by gut content analysis (Sunderland, 1998; Greenstone, 1999). Analysis of insect predator gut contents is very useful in providing information on trophic interactions and predator-prey dynamics. Direct field observations are not very helpful in this regard whereas molecular experiments tracking trophic interactions in food-webs through polymerase chain reaction (PCR) provides the mean for amplification and thus visualizing the DNA (Sheppard and Harwood 2005). Gut analysis requires only short, sporadic presence in the field after which the gut analysis proceeds in the laboratory. One major advantage of this technique is that it allows rapid and more or less accurate assessment with minimal disturbance to study site, thus enlightening predator’s prey choice with little uncertainty. For example, Harwood et al. (2004, 2005a, 2005b) combined predator’s gut analysis with population monitoring to highlight different patterns of prey selection in different communities of generalist predators. Winder et al. (2005) used predator gut analysis to investigate population level and spatial associations between carabid beetle predators and their prey. According to recent literature, gut content analysis in unique in its importance either DNA or protein of the prey are in use (Fournier et. al., 2006). It is the time during which prey remains in the predator

5 gut and is of considerable importance during interpretation of molecular gut content data (Greenstone et. al. 2005). From past few years there has been stress on the interaction of agronomic practices with ecological processes within their ecosystem. To meet these needs, the complexity and dynamic nature of ecological processes within agro-ecosystem requires a systematic approach to research (Olfert et al. 2002). Genetic diversity assessment of different arthropod species can facilitate an understanding of their biology and ecology but little effort has been made to access their genetic characterization. RAPD (Random Amplified Polymorphic DNA) has its importance specifically with reference to wide availability of commercial primers and lack of DNA sequencing information prior to analysis (Williams et al. 1990). RAPD-PCR has the advantage of being quick and easy, requires minute material, cheap and detects genetic polymorphism among organisms (Fritsch and Rieseberg 1996). Recent reports revealed that the RAPD markers have been extensively used in applied and basic research program in many different fields (Li and Jin, 2006; Sreekumar and Renuka, 2006) and have proven to be an influential tool for assessing population structure of a species and are not exaggerated by variation in ploidy levels (Zhao et al. 2008). The polymerase chain reaction (PCR) has revolutionized the field of diagnostics and today it has various applications in different fields of biological sciences also. In applied entomology, for identification of insects and systematics, the use of recent molecular approaches is a common practice. PCR based methodologies are also widely used for parasitoids and predator-prey identification and detection, and such kind of studies have their contribution in developing different biological control strategies against arthropod pests (Gariepy et al. 2005). Islamic Republic of Pakistan is situated on the northwestern side of the Indian sub- continent. It is situated between the latitudes of 240 to 370 north and longitudes of 610 to 750 east, stretching over 1600 kilometers from north to south and 885 kilometers from east to west, with a total geographical area of 796,095 thousand square kilometers. The country has a subtropical and semi arid climate. The annual rainfall ranges from 125 mm in extreme southern plains to 500 to 900 mm in the sub- mountainous and northern plains. About 70 percent of the total rainfall occurs during summer and 30 percent in winter. The seasonal distribution of rainfall is strongly influenced by monsoon, which starts in July and ends in

6 September. Winter rains occur during December to March (FAO, 2004). Pakistan has a total land area of 88 M ha. Of this, 22 M ha are used for crop production. About 18 M ha of the cultivated land is irrigated while the remainder is dry farming. There are two main crop seasons in Pakistan, “Kharif” with sowing in Apr-May and harvest in Oct-Nov and “Rabi” starting in Nov-Dec and ending in Apr-May. Sugarcane, Cotton, Rice, Maize and Millet are Kharif crops while Wheat, Mustard, Gram, Tobacco, Rapeseed and Barley are Rabi crops. Agriculture is the backbone of Pakistan’s economy and Punjab province has a great share in this regard. Total geographic area of the province is 20.63 M ha, out of which 16.96 M ha is the cropped area, which contributes a major share in country’s economy by providing 63% of sugarcane, 51% of maize and fodders, 80% of wheat and 34% mustard to the national food production. About an area of 1 million hectare is occupied by sugarcane, 1998.2 thousand hectares by different types of fodder, 8 million hectares by wheat, and 0.748 million hectares by mustard crop in Punjab. Based on different cropping patterns and agro climatic conditions, cultivations in Punjab are classified into different zones. Two of them are 1) mixed-crop zone and 2) cotton-wheat zone. Mixed crop zone (2.6 million hectares) constitutes vast area of central Punjab. The Rabi crops like sugarcane and fodder and kharif crops like wheat and mustard are sown in the fields and fellow lands. Due to small ownership, heterogeneity of crops and their importance as food crops, the use of pesticides and synthetic fertilizers are relatively less intensive in this zone. Cotton-wheat zone (1.36 million hectares) constitutes vast area of southern Punjab. Cash crops of the zone are wheat, cotton and mustard and are sown extensively while sugarcane and fodder cultivations are sparse. There is a trend of mono- cropping in this zone and use of pesticides, herbicides and synthetic fertilizers is extensive. The arthropod fauna of the two different cropping systems is suspected to vary due to the differential chemical off-farm inputs. The organisms occupy all trophic levels as parasites, parasitoids, preys, pests and predators. At each trophic level the organisms are responsible for the dissipation of energy which they get from crop plants and reduce the net production. Focusing towards the ultimate adoption of natural agro-ecosystems, the study was aimed at the following objectives: 1) Identification of the major arthropod fauna found in the fields of two cropping systems.

7 2) Effect of plant biodiversity on faunal populations of two systems. 3) To study the probable interactions among faunal populations (Predator-prey relationship). 4) An estimation about the efficient use of predators against different preys based on gut content analysis.

The above mentioned objectives have been discussed under four headings in the form of four research papers: Biodiversity of foliage arthropods in the mixed crop zone and cotton-wheat zone in Punjab province, Pakistan (Objective 1) Weeds as life source for different arthropod species in the croplands of Punjab (Objective 2) Predator-prey association among selected arthropod species in the cropland of Punjab, Pakistan (Objective 3) Determination of genetic diversity in some selected arthropod species using RAPD (Random Amplified Polymorphic DNA) and predator-prey relationship by PCR (Polymerase Chain Reaction) (Objective 4)

8 Chapter 2

REVIEW OF LITERATURE

Biodiversity and Agro-ecosystems

Biodiversity is the variety of life forms within a given ecosystem, biome, or on the entire earth. It is an important parameter used for determining the health of a biological system (Wilson et al. 2008). About ten years ago, the field of biodiversity was not so important for the ecologists but now it has a great impact on functioning of ecosystem. The diversity must be included in the list of different factors that has a definite impact on ecosystem functioning in addition to species composition, disturbance regime and environmnetal factors. Importance of biodidversity can be highlighted as our society depends on natural and managed ecosystems for needs and services that are essential for survival of mankind but we know a little about the working of an ecosystem (Tillman, 2000). In 1992, in the convention on biological diversity all contries of the world had the opinion that biodiversity is of great importance in maintaining the planet’s life sustaining system. If we have to face the challenges of coming millennium, the traditinal methods are insufficient (Howlett and Dhand, 2000).

A great deal has been written about the value of biodiversity to agriculture (Ehrlich and Ehrlich, 1981) and as extinctions continue at an alarming rate (Wilson, 1996) it is clear that our future options in terms of feeding ourselves are concomitantly diminishing (Miller, 1990). There is no doubt that genetic diversity, species diversity and ecological diversity have been, are, and will continue to be absolutely essential to sustaining the world’s agriculture. In agriculture systems, biodiversity performs a variety of ecosystem services in addition to food production, fiber, fuel and income. This includes the nutrient recycling, management of local micro climate and detoxification of harmful chemicals. The persistence of these biological processes depends upon stability and maintenance of biological diversity within the ecosystem (Altieri, 1994). About 20% of the world’s life have been discovered and described according to a recent estimate. Invertebrates constitute a major part of it but comparatively less popular

9 than other groups. Arthropods dominate the diversity of plants and animals representing approximately 90% of all species (Pimental et al. 1992) and play an important role in maintaining an ecologically balanced agro-ecosystem. They are also useful bioindicators of agro-ecology and environment quality there. One of the requirements of modern agriculture is that these organisms should be protected in order to ensure the stability and sustainability after environmental changes and agricultural disturbances overtime (Paoletti et al. 1999a). They are also important as predators, parasitoids, preys and pests. In recent decades agricultural practices towards expansion and intensification includes treatment with large quantities of chemicals (fertilizers, pesticides, herbicides, fungicides etc). All these are the major threats to biodiversity (Benton et al. 2003). A critical issue is that biodiversity loss may lead to changes in ecosystem functioning, with concordant threats to stability and resilience of agricultural systems (Mozumder and Robert, 2006). Agriculture landscape often support complex and dynamic biological communities within a diverse array of land uses (Benton et al. 2003; Bengtsson et al. 2005). Attwood et al. (2008) compared the relative biodiversity value of land use ranging from low to high intensification. Arthropod abundance, richness and community composition among sites in cropland, pasture and woodland on nine southern Queensland properties was analyzed. Overall abundance of arthropods was significantly greater at cropping/pasture interfaces than in woodland or cropping sites, but order level richness did not differ between land uses. It appeared that even small and degraded woodland play an important role in maintaining arthropod diversity in agriculture landscape. Pesticides can damage soil, destroy the organisms living there, reduce food availability by destroying habitats of arthropods that are source of food for others (Pretty, 1998). Specific doses of pesticides have been a source of organ deformities in vertebrates in different parts of Florida (Bourne, 1999). It is an accepted fact that the agro-chemicals are the causative agents of many neurological disorders and few types of cancers (Morgan, 1992). Studies regarding damage caused by agro-chemicals suggest that annually about 10,000 farmers are poisoned by pesticides. It is suspected that they disrupt the endocrine system and also have potential effect on developing foetus (Khan, 2004). Organic agriculture practices increases the abundance of many species and organisms as compared to conventional farming. By using herbicides in conventional system

10 flora other than crop plants decreases which resulted in lethal effects of animal life depending on these plant species (Chiverton and Sotherton, 1991). Organic farming is believed to be more environment friendly results in little leaching of nutrients, less erosion and lower level of pesticides in water systems (Kreuger et al. 1999; Mader et al. 2002). Organic management provide a definite advantage over conventional agriculture in a sense that if a farm as a whole is dependent on organic standards, rather than conventional practices, the chances of obtaining an environmental friendly product are more in organic farming as compared to conventional one. However, gaps in the prerequisite of habitat quality and quantity of a system still exist (Vickery, 2002). Now a day’s Monoculture is attaining popularity in order to fulfill the demands of large growing population. Sufficient food should be available to meet the daily needs for survival of life on earth. Few major security risks linked with monoculture are: i) there is no clear picture showing relationship between crop products, soil, crop and animals there. ii) though sufficient amount of crop residue in the form of green manure is available but recycling of nutrients within agricultural system is difficult to predict iii) availability of continuous resources increases the chance of specialist crop herbivore (i.e. pest outbreak), also increases the chance of immigrant pest to settle in a consistent environment iv) to control the pest outbreak intensive chemical control is required v) productivity is at threat because after 5-10 years a new cultivar become available vi) in most of the crop systems yield is going to decline (Conway and Pretty, 1991). The concept of sustainable agriculture developed in response to decline in quality and quantity of natural resources as a result of advanced agricultural practices (Isaacs and Edwards, 1994). The interaction of plants and animals communities with their physical and chemical environments that have been modified by people for food, fibre, fuel and other products for human consumption form the basis of an agro-ecosystem. An understanding of these ecological relationships helps in doing manipulations for improved production, stability and sustainability with little side effects (Altieri, 1995). In the formulation of such a stable system following points are important: i) protect the soils in order to conserve the soil food web ii) proper recycling of biomass within the system iii) diversification of agro- ecosystem in terms of time and space iv) different biological interactions and synergies in the agro-ecosystem should be identified and promoted to make the system more stable.

11 Various strategies to conserve agricultural diversity includes i) crop rotation ii) polyculture iii) introduction of agro-forestry iv) using different cover crops v) coordination among different trophic levels vi) introduction of organic farming (Vandermeer, 1989). The reasons which make the diverse agro-ecosystem more valuable include opportunities for coexistence and beneficial interactions among species; resource-use efficiency; create a diversity of microhabitats within the cropping system for many organisms that are important to entire system; conservation of different life forms; and reduces the risks of loss (Altieri, 1994; Gliessman, 1998). By optimizing the use of locally available resources, reducing the use of off-farm inputs, relying on resources within the agro-ecosystem, conserve biological diversity, improving the match between cropping patterns and the productive potential by using local knowledge and practices farmers can enhance sustainable agriculture and maintain long term productivity (Pretty, 1995; van der putten et al. 2000). The agro-ecosystems which have differences in age, diversity, structure and management also have differences in type and abundance of biodiversity. Actually the basic ecological and agronomic principles vary among the dominant agro-ecosystems of the world. Such principles are dependent on four major components like i) diverse vegetation within and around agro-ecosystems ii) stability of various crops within the agro ecosystem iii) different management strategies and how isolated is the agro-ecosystem from the natural vegetation (Southwood and Way, 1970). Important thing is to identify the type of biodiversity that is favourable to maintain or enhance in order to perform various ecological services. It is direly needed that an efficient management strategy should be implemented to enhance or regenerate the biodiversity that not only support financially but also maintain the sustainability of agro ecosystems by providing different ecological services such as biological pest control, nutrient cycling, water and soil conservation (Altieri, 1999). Weeds in the cropland Generally it is said that weed are crop damaging plants and should be controlled immediately but it is a modern concept that at low density, they do not usually affect the crop yield, and even certain weeds can stimulate crop growth. In rain fed areas of Kenyan arid zone, some broad leaved and leguminous weeds increased the growth and yield of sorghum and millets. While in control of insects and nematodes pests, especially on

12 vegetables, some species of wild plants have an important role (Thijssen, 1991a). Therefore, certain weeds are managed and encouraged if they have some useful purpose in the crop. They are the source of food for animals, few are easy to digest have high crude protein content and easily available to the organisms (Nuwanyakpa and Bolsen, 1983). Weeds also have a potential to improve the soil health by providing green manure, in addition to inhibitory effect on other parasitic weeds. Although the non-crop plants compete with the crop plants for water, nutrients, space, light, also harbour many viral and fungal disease causative agents (Wang et al. 2007). In addition to this they also serve as an alternate host for many faunal species, provide food to birds, rodents and their predators. Heavy use of fertilizers not only responsible for changing the soil chemistry but also negative effect on weed flora associated with the crop. Farmers often control the weed at a stage when it is not competing with crop plants. Therefore the knowledge about status and information about management of a pest species either plant or animal is necessary (Labrada, 1996). Vegetation in and around the cropland has many useful purposes as few weeds are of medicinal value to both human and animals. Due to this importance such non-crop plants should be conserved, propagated and domesticated to some extent (Bhattacharjya and Borah, 2008). Weeds are an important component of agro-ecosystem being a part of primary producers. It is an admitted fact that crop yield is affected by weed species diversity and not by their densities. New management strategy for weeds thus has emphasis on both the diversity and density (Rasmussen et al. 1997). In a study conducted at United Kingdom there is evidence that weed flora has changed with the passage of time with few species diminishing and others have increased. The recent agricultural practices seemed to be responsible for such changes. According to a database of phytophagous insects many have strong association with arable weed species. The insects served as food for many birds thus, reduction in number of host plants may have a significant affect on associated populations. In short we can say that weeds have a positive role within the crop system in supporting biological diversity. This may be a first step in designing of a proper weed control and management strategy programme for sustainable agriculture according to recent conservation biodiversity plan (Marshall et al. 2003).

13 When we say that weed plants in the cropland are important for fauna present there, it is necessary that we have proper information on host plant relationship that is directly relevant in this regard. Only targeted work on single weed species could provide valuable results. Unfortunately such kinds of studies are lacking, although work on arable weed species has revolutionized the potential of this approach (Brown and Hymen, 1995). A number of insects are dependent on weeds for completion of their life cycle, some species support few specific type of insects whereas, others support a diversity of invertebrates (Marshall et al. 2003). A more recent concept that Ecological Pest Management (EPM) could replace Integrated Pest Management (IPM) has given by Tshernyshew (1995) due to more stable agro-ecosystem and high biological diversity. The diverse flora and fauna playing a pivotal role in maintaining an ecosystem balance. According to FAO estimates the existing genetic diversity within the cropland is at stake and in serious risk of demolishing in coming decade (Prabhakar, 1999). From the last 50 years the continuous decline in farmland biodiversity is of great concern. Though more emphasis has been placed on farmland birds (Newton, 2004) but invertebrates have also their part in this diversity (Biesmeijer et al. 2006). Less intensively managed farms play a key role in enhancing biodiversity (Lutman et al. 2009). Broad- spectrum herbicides used in combination with genetically modified herbicide-tolerant (GMHT) crops might further worsen the long-term declines in the weed flora, and those species dependent upon them (Hails 2003; Watkinson et al. 2000). That’s why a question arises that should spring and winter rape crops in the UK be largely replaced by GMHT varieties and managed as in the Farm scale evaluations FSEs, that is a remarkable risk in decline of farmland life during last quarter of 20th century (Gibbon et al. 2006). The plants and field margins both affect the animal distribution in an area and play an important role in predator-prey interactions (Ferguson, 2000). Also the movement pattern and feeding behaviour of predator is affected by the distribution of prey in the field (Kotliar and Wiens, 1990). Marginal plantation also has an effect on predators feeding activity as few preys available on the edges and are suspected to severe attack by the predators (Lima and Zollner, 1996). With increase in space marginal plantation pattern became more complex therefore, more chances of influences on predator-prey populations (Ritchie and

14 Olff, 1999). Comparatively more abundance of predator and less abundance of prey was observed on field margins thus theses margins favour predation (Murica, 1995).

15 Predator-prey relationship and pest control To control the insect pest is a complex issue over the last few decades. Generally the use of a single tactic for control is not recommended in order to avoid from resistance development or minimal damage to non-targeted species and less destruction to the environment (Song and Xiang, 2006). Traditional control means alterations in the cropping system make it less favourable for establishment and proliferation of insect pests. Although they are planned in such a way that there are very few side effects on the ambient environment but negative impacts may also results due to changes in climatic conditions and crop management strategies (Zhang and Chen, 2005). Predator-prey relationship is of great importance in complex food web of agricultural crops. To use a single predator species for control of several different pests result in a simple food web than that in which different predators are introduced to control several pests. When a predator share two preys, density of one prey decreases with increasing density of other prey species (Morris et al. 2004), this type of interaction result in omission of one of the two prey species (Bonsall and Hassell, 1997). Two species that share a common predator also affect each others density. If one prey species is in abundance and available to predator, there is less predation on other species. Such indirect effects are known as apparent mutualism. The use of one predator species for control of two pest may result in reduced control in short term, but increased control in long term (van Rijn et al. 2002). It is not clear that greater predator diversity is always preferred. In the presence of a diversity of predators when a single prey is available and its preferred predator also there in the field but that prey species is also shared by the most strongest predator in the community. In contrast where more preys are available top down suppression of prey species strengthens with increase in predator diversity (Ives et al. 2005). Thus greater prey diversity is responsible for appearance of its complementary predator and also weaken the intraguild predation if any (Ives et al. 2005; Briggs and Borer, 2005). Condition in the field is very much dependent on specific ecological conditions and not on landscape and farming system. If a landscape structure is kept constant but the quality of one compartment has a strong affect on pest. Because if the preferred prey is unavailable, alternate are there. Thus, the dynamics of arthropods particularly the pest differs even if the cropping system is not subjected to any change (Way and Heong, 1994). Predatory role of spiders to contain the

16 insect pest has been considered significant thus playing an important role in reducing crop damage. Spiders are not only a single natural enemy but are a component of large complex of natural enemies (Carter and Rypstra, 1995). They control Colorado beetle (Cappaert et al. 1991) mosquitoes (Service, 1973) caterpillar in cotton (Clark et al. 1994) hoppers and other pest on rice (Settle et al. 1996). The effect of spider species on pest populations may be enhanced by providing a rich supply of nutritive food in the form of an alternate prey. In early season high spider pest ratio facilitates in the later specialist enemies in suppressing pest population below economic threshold level (Axelsen et al. 1997). Spiders of the family Lycosidae are worldwide in distribution, mostly generalized in their feeding habits and have a positive role in the agro-ecosystem (Platnick, 2002). Guoyue and Hongbin (1998) described an abundance of beneficial insects like beetles (Coccinellids) in the presence of Diuraphis noxia in wheat. Evan and England (1996) concluded that Coccinella septumpunctata indirectly influence the ongoing biological control of weevils by reducing pea aphids and thus making honeydew available for weevil parasitoids. Diversity and gut content analysis by molecular techniques The study of predator-prey relationship or the trophic link between different faunal species is difficult to study directly in field conditions. But the recent molecular advancements has made this work possible and now it is an achievable task. To use beneficial insects (predators and parasitoids) for the control of targeted pest species, it is essential to have information on their population dynamics, breeding biology and host switching. In present scenario the molecular DNA markers have proved to be the best tool for this (Macdonald et al. 2004). Molecular markers explain the ecological interactions in a better way, because they have taxonomic information of recently developed taxa, e.g. biotypes, races, subspecies, cryptic species, sibling species, and immature life stages which present nonflexible morphological differences (Claridge et al. 1997). Gut contents is important in determining predator-prey relationships among different species. The study of predation is a difficult task. A number of techniques have been used by ecologists to study arthropod predation especially for those having sucking type of mouth parts. Traditionally methods for detecting gut contents were the use of isozymes or serological techniques with antibodies developed against prey. Immunological assays were specifies specific and highly sensitive, expensive and complex (Greenstone, 1996).

17 Alternative methods for (MoAbs) are the DNA based studies and have been used successfully in studying insect vector diseases (Hill and Crampton 1994). The selection of a suitable oligonucloetide of DNA is comparatively easier than developing a primer for a specific species from available literature. For the ecologist a major hindrance is the deficiency in sequence information of majority of species which are required for PCR (Hadrys et al. 1992). Recently, PCR based methods for detection of prey in predator’s diet is an efficient mean for study of predator-prey relationship (Harper et al. 2005). In order to confirm the hypothesis that Dicyphus tamaninii feed on Trialeurodes vaporariorum, Agusti et al. (2000) developed certain DNA markers of prey to check their presence in the predator’s gut. Specific DNA fragment or (a unique fragment) of Trialeurodes vaporariorum absent in other closely related prey species and predator was identified by random amplified polymorphic DNA (RAPD) analysis. After cloning and sequencing of this fragment sequence characterized amplified regions or (SCAR) marker were developed. In gut assay the COI and COII markers showed significantly better results as compared to SCAR. COI has higher detection efficiency than COII markers (Lange et al.

2004). It is a complex method to study the relationship among different communities by using molecular techniques because they address the flaws created during visual identification. Presently, monoclonal antibody or DNA based methodologies provide a fast and precise method of detecting a prey remnant in the predator’s gut or fecal matter (Sheppard and Harwood, 2005). Different types of trophic interactions are based on the ability to differentiate unique pieces of DNA in invertebrate predator and prey. The amplification of DNA by PCR is the key step in this mechanism (Ehrlich, 1989). In entomology DNA based techniques have proven to be successful in different areas like taxonomy, phylogeny, population genetics and so on (Behura, 2006). Reviews in molecular diagnostics in predators and parasitoids are of great importance. Different food items consumed by a natural predator are the basic task of studying a predator-prey relationship. According to Bigler et al. (2005) taxonomy and molecular characterization including various types of molecular analysis required before release of a natural predator in the field for control of a specific pest species. Study of population dynamics and behaviuoral study of a predator facilitate the minimum non-targeted effects on the ambient environment

18 and also on other populations residing there. Additionally micro satellite DNA analysis could also be used to study different strains and in evaluating genetic diversity of different populations especially the arthropods being small in size and highly sensitive group of organism subjected to mutations due to different type of environmental changes (Gariepy et al. 2007). RAPD-PCR has limitations due to its non-reproducibility of results and therefore has a limited use in detecting successful predator-prey relationship (Greenstone, 2006). The most common method used in identification and study of trophic interactions among different group of organisms involves species specific primers, although this strategy has few limitations too (Erlandson and Gariepy, 2005). There are advantages of using PCR that we can incorporate primers of a number of different species at a time in one reaction (multiplex PCR). Thus it can provide a rapid and cost-effective method of detecting a single DNA sample for many targets. Indeed our ecosystems especially the agro-ecosystem would not function in the absence of arthropods (Siddiqui, 2005). Way and Heong (1994) found that insect biodiversity is an important factor in pest management strategies rather than applying agrochemicals. The casual relationship to insect biodiversity through habitat loss, habitat fragmentation and ecological changes were best explained by (Cornell and Karlson, 1996). The diverse fauna from smallest soil microorganism to domestic live stock play a pivotal role in maintaining a balanced ecosystem. According to FAO estimates in coming decade more than 90% of the present genetic diversity within the cropland is at risk (Prabhakar, 1999). Like other countries in the world, Pakistan is also having problems of biodiversity loss. Pakistan has a remarkable number of world’s ecological regions with its geological history, broad latitudinal spread and immense altitudinal range. Its biodiversity is a blend of elements from Palaeartic, Oriental and Ethopion origins resulting into eighteen more or less distinct type of natural habitats. A major threat to biodiversity due to loss of its natural habiatat can be accessed with the oversimplification of natural system due to modern agriculture. Decline in number of plant and animal species in the ongoing scenario is of great concern. Agro-ecosystems are also facing a detrimental loss, by facing a decline in soil micro and macro invertebrates, vertebrates at all trophic level of food web thus indirectly

19 posing a stress on human life. In world ranking Pakistan is at second number with highest rate of depleting the biodiversity ‘deforestation’ (Govt. of Pakistan, 2000). Although the Government is making efforts to conserve biodiversity by constituting a working group by Ministry of Environment, Local Govt. and Rural Development (MELGRD) and in collaboration with the World wide fund for conservation of nature, Pakistan (Govt. of Pakistan, 2000). Biodiversity Conservation centre also came into existence in University of Karachi in this regard. For implementing sustainable agriculture strategies, no proper database of faunal species is available. The literature on taxonomy and role of non-crop plants in the cropland sustainability is deficient. Also the arthropod predator prey relationship studies are lacking in making the system more stable. With this background information the present study was planned with respect to cropland biodiversity with special emphasis on arthropods, their role in stability and sustainability of agro- ecosystem in major crops of two important zones or agro-ecosystems in Punjab, Pakistan.

20 Chapter 3 BIODIVERSITY OF FOLIAGE ARTHROPODS IN THE MIXED CROP ZONE AND COTTON-WHEAT ZONE IN PUNJAB PROVINCE, PAKISTAN ABSTRACT Arthropods are the most integral part of an agro-ecosystem, but the crop intensification practices are badly affecting these key components. Studies pertaining to biodiversity of arthropods in the cropland of two zones i.e. Mixed crop zone (Faisalabad) and Cotton-Wheat zone (Multan) Punjab, Pakistan were conducted for a period of one year. The main focus was to collect, identify and compare the species richness and evenness. Sugarcane, Fodder, Wheat and Mustard were sampled round the year showed variations in species composition of their fauna in the two districts representing the two zones. Mixed-crop zone was highly diversified with respect to species and abundance of individuals per species. On the whole order Orthoptera was dominant followed by Araneae, Hemiptera, Coleoptera, Lepidoptera, Hymenoptera, Odonata, Diptera and Thysanoptera, Neuroptera, Prostigmata each represented by single species except Mantodea with two species. This data base will be helpful in future ecological pest management strategies. The mixed-crop zone was found better than cotton-wheat zone with respect to faunal diversity that may be functional in keeping the sustainability of agro-ecosystem intact.

Key words: Foliage fauna; Arthropods; Croplands; Sustainable agro-ecosystem

INTRODUCTION

The increasing world population and changes in consumption patterns increased significance of agricultural intensification during the last few decades. Unless crop yield is improved and release of fertilizers and pesticides in the croplands is reduced, such intensification would augment contamination and perturbation of managed and natural ecosystems, ultimately damaging biodiversity and public health (Hughes, 2002).

In more intensive agriculture, arthropod populations are lowest. This intensification highlights many contributory factors, which can be addressed individually. These include cropping pattern, frequency of tillage, amount and nature of fertilizers used, amount and nature of pesticides used etc. However, it should be noted that all these factors which are interrelated to a greater or lesser degree, often cause negative synergies to the agriculture

30 (Cherry, 2003). The cropsystems, biodiversity performs a variety of ecological functions beyond the production of food, including recycling of nutrients, help regulation of microclimate and local hydrological cycles, suppression of undesirable organisms and detoxification of chemicals especially the agro-chemicals. Biodiversity mediated renewal processes and ecological functions are largely biological, and their persistence depends upon the maintenance of species integrity and diversity in agro-ecosystem (Alteiri, 1999).

Diversification of cropping system often leads to reduce herbivore populations. Studies suggest that more diverse the agro-ecosystem and the longer this diversity remains undisturbed, the more internal links develop to promote greater insect stability. It is clear, however, that the stability of insect community depends not only its trophic diversity, but also on the actual density dependence nature of the trophic levels (Southwood & Way, 1997).

(Olfert et al. 2002) highlighted the importance of arthropods. According to them, arthropods fauna is integral during evaluation of ongoing cropping practice and helps in redesigning of farming systems in order to make it economically viable and environment sustainable. It is now an established fact that arthropod predators suppress pest populations (Chang & Kareiva, 1999; Gurr & Wratten, 2000; Symondson et al. 2002). There are evidences that species-rich ecosystems are more stable than species-poor ecosystems. If the relationship between biodiversity and stability holds, then it is in the interest of the long- term viability of a region to encourage diverse human and natural ecosystems (Minor, 2005). Based on different cropping patterns and agro climatic conditions, cultivations in Punjab are classified into different zones. Two of them are 1) mixed-crop zone and 2) cotton-wheat zone. Mixed crop zone (2.6 million hectares) constitutes vast area of central Punjab. The Rabi crops like sugarcane and fodder and Kharif crops e.g. wheat and brassica are sown in the fields and fellow lands. Due to small land ownership, heterogeneity of crops and their importance as food crops the use of pesticides and synthetic fertilizers are relatively less intensive in this zone. Cotton-wheat zone (1.36 million hectares) constitutes vast area of southern Punjab. Cash crops of the zone are wheat, cotton and brassica and are sown extensively while sugarcane and fodder cultivations are sparse. There is a trend of mono-cropping in this zone and use of pesticides, herbicides and synthetic fertilizers is extensive. The arthropod fauna of the two different cropping systems is suspected to vary

31 due to the differential chemical off-farm inputs. The objectives of the present study aim at i) identification of the major arthropods in the crop fields of two zones ii) effect of important environmental factors on faunal populations iii) crop preference of different faunal species in the two cropping systems. MATERIAL AND METHODS A preliminary survey was made to select the crop fields of sugarcane, fodder, wheat and brassica in two zones i.e. mixed crop zone and cotton-wheat zone. For extensive information on current and past management practices in these habitats a questionnaire was made for interviewing the land owners/farmers, with specific reference to the use of chemicals and mechanical operations at farms. At each locality two blocks, each of more than five acres of different cropland were taken. Then at each block, two acres were selected randomly for collection of fauna. Sampling was initiated as per schedule from June 2007 to May 2008 (two days in a month in each zone). Sweep net was used to sweep all types of adult and large arthropod present above the canopy of the crop. Heavy duty muslin nets (38_cm dimension) were used to sweep through vegetation forming a figure of eight. Direct hand picking and automated sifters were also employed to collect the foliage fauna. All the arthropod specimens were preserved in laboratory grade Alcohol with few drops of Glycerine. The identification up to species level was done with the help of available, related taxonomic information in the “Fauna of British India” and online electronic keys available on different websites. Museum of the Department of Agri. Entomology, University of Agriculture, Faisalabad and Entomological Research Institute Jhang road Faisalabad was also consulted for this purpose. The trophic level of each species (phytophagous, zoophagous & saprophagous) was confirmed from recent available literature. Shannon’s diversity index and Multiple Linear Regression (Magurran, 1988; Ludwig & James, 1989) using GW Basic vesion 6 while Cluster analysis using Statistica version 9 were used for exploratory and confirmatory analyses.

32 RESULTS AND DISCUSSION Four major crops viz. sugarcane, fodder, wheat and mustard were sampled in two zones for the collection of arthropods present on them. 1. MCZ Agro-ecosystem District Faisalabad covers an area of 5,856 sq km of Central Punjab. The Rabi crops like sugarcane and fodder and Kharif crops e.g. wheat and mustard are sown in the fields and fellow lands. Due to heterogeneity of crops and their importance as food, use of pesticides and synthetic fertilizers are less intensive in this zone. District Faisalabad, being in the center was selected to represent MCZ agro-ecosystem. Fields of sugarcane, fodder, wheat and mustard were selected for comparison of faunal (different arthropod predator and preys/pests) and floral diversity there in.

2. CWZ Agro-ecosystem District Multan was selected to represent CWZ agro-ecosystem due to its central position in the zone. It covers an area of 3,721 square Kilometers of Southern Punjab. Cash crops of the zone are wheat and mustard, these are sown extensively while sugarcane and fodder cultivations are sparse. Thus there is a trend of mono cropping and use of pesticides, herbicides and synthetic fertilizers is very extensive. Fields of sugarcane, fodder, wheat and mustard were selected for comparison of faunal (different arthropod predator and preys/pests) and floral diversity there in.

Distribution of arthropod in the cropland of two zones (Order wise)

Out of 218 species of arthropods reported from two zones, 212 were captured from Faisalabad representing mixed crop zone hereafter called MCZ fields whereas 182 from Multan representing cotton-wheat zone hereafter CWZ. Twelve orders were identified and grouped as more abundant (Orthoptera, Hemiptera, Coleoptera, Lepidoptera & Araneae) less abundant (Hymenoptera, Odonata & Diptera) and rare (Thysanoptera, Mantodea, Neuroptera & Prostigmata). Order Orthoptera had highest diversity followed by Hemiptera, Coleoptera, Araneae, Lepidoptera, Hymenoptera, Odonata, and Diptera in MCZ whereas in CWZ Orthoptera was followed by Araneae, Hemiptera, Coleoptera, Lepidoptera, Diptera, Hymenoptera and Odonata. Thysanoptera, Mantodea, Neuroptera and Prostigmata showed no difference in species diversity in two zones studied. The reduction in number of species

33 was consistent for all the orders in CWZ fields except Araneae. A detailed description of each order in both the zones is as follows:

Odonata

A total of 11 species were reported from two zones, all were present in MCZ while only 6 species were observed in CWZ (Table 1). Only a single species Pseudagrion spp. (51) was abundant in the cropland of MCZ. Among different crops sampled, sugarcane was the preferred one in MCZ with respect to faunal diversity and abundance having 9 species while fodder was preferred in CWZ with 6 species.

Orthoptera

Among different species reported, 61 were present in MCZ while 52 were present in CWZ (Table 2). Species namely, Phlaeoba antennata (57), Chrotogonus robertsi (62), Acrida turrita (81), Acrida nymph (165), Phlaeoba spp (175), Cyrtacanthacris ranacea (65), Chorintippus dorsatus (82), Leptysma marginicolis (107), Metaleptea brevicornis (84), Aleurolobus barodensis (52), Belocephalus sabalis (62) and Oedipoda germanica (61) were abundant in MCZ while only two species were also abundant in CWZ namely, Acrida nymph (135) and Phlaeoba spp (86). Among different crops, sugarcane was the preferred one with respect to faunal abundance and diversity in both the zones.

Hemiptera

Among different species, 32 were present in MCZ while 26 were present in CWZ (Table 3). Species namely Pyrilla perpusilla (73), Aspongopus janus (69), Amrasca devastens (84), Diuraphis noxia (125), Rhopalosiphum padi (134), Rhopalosiphum maidis (257), Aphis fabae (85), Macrosiphum miscanthi (698), Schizaphis graminum (301) and Aphis gossypii (112) were abundant in MCZ while D. noxia (91), M. miscanthi (282), S. graminum (92) and A. gossypii (77) were also abundant in CWZ. Among the crops, sugarcane was the preferred one with respect to diversity of fauna in MCZ while fodder was preferred in CWZ.

34 Coleoptera

Among different species, 32 were present in MCZ while 26 were present in CWZ (Table 4). Species namely, Coccinella undecimpunctata (98), Hippodemia variegata (110), Cheilomenes larvae (326), Coccinella septumpunctata (699), Hippodemia convergens (171), Tanymecus sciurus (55), Coccinella larvae (318), Brumodes suturalis (60), Cheilomenes sexmaculata (229) and Adalia hexaspiolta (119) were abundant in MCZ while H. variegata (72), Cheilomenes larvae (93), C. septumpunctata (255), H. convergens (74), Coccinella larvae (180), C. sexmaculata (175) and A. hexaspiolta (64) were also abundant in CWZ except Micraspis allardi (58) restricted to this zone only. Again the sugarcane crop was preferred one in MCZ with respect to faunal diversity while wheat seemed to support maximum abundance of coleopteran species in both the zones.

Lepidoptera

Among different species, 20 were present in MCZ while 16 were present in CWZ (Table 5). Only a single species Spodoptera frugiperda (100) was abundant in the sample of MCZ. Sugarcane crop was the preferred one with respect to species diversity and abundance of fauna in both the zones.

Diptera

Among different species reported, 11 were present in MCZ while 9 were present in CWZ (Table 6). Species namely, Musca domestica (103), Anopheles quadrimaculatus (63) and Piophila casei (63) were abundant in the sample of MCZ while only a single species M. domestica (50) was abundant in CWZ. Among the crops sugarcane was preferred in MCZ with respect to faunal diversity while fodder was preferred in CWZ.

Hymenoptera

Among different species, 12 were present in MCZ while 8 were present in CWZ (Table 7). Three species Triepeolus alachuensis, Polistes olivaceous and Vespa orientalis were abundant in the sample of MCZ. Among the crops, fodder and wheat were preferred with respect to faunal diversity and abundance in MCZ while fodder and mustard were

35 supporting 6 faunal species and sugarcane and wheat supporting 5 faunal species each in CWZ.

Araneae

An interesting picture was observed in this case. 27 species were present in MCZ while 34 were present in CWZ (Table 8). Few species were abundant in the sample of MCZ namely, Pardosa oakleyi (114), Oxyopes javanus (143), Neoscona bengalensis (60), Clubiona filicata (63) and Pardosa sumatrana (63). Out of these O. javanus (84) was also abundant in CWZ whereas, Araneidae nymph (57), Neoscona pavida (57), Neoscona theisi (88), Cheiracanthium himalayensis (69), Thomisus cheraponjiesis (51), Neoscona spp. (53), Cheiracanthium denieli (55), Clubiona bengalensis (60), Oxyopes biharensis (63) and Neoscona mukerjei (74) were only abundant in CWZ. Sugarcane was the preferred crop with respect to faunal diversity and abundance in both the zones.

Others

This is a combined group of different orders (Thysanoptera, Mantodea, Neuroptera and Prostigmata) represented by single species each in the sample (Table 9). More faunal abundance was observed in MCZ as compared to CWZ sharing almost all the crops in two zones.

Statistical Analysis Table 10 showed highly significant differences between MCZ and CWZ fields. Changed farming practices of these two areas were probably the reason for this trend. Intensification of agriculture by use of high-yielding crop varieties, fertilization, irrigation, and pesticides has altered the biotic interactions and reduced the in-farm resources for sustainability of the system and have serious local, regional, and global environmental consequences (Matson et al., 2007). Similar findings for MCZ and CWZ were also given by Siddiqui (2005) in the wheat agro-ecosystem while comparing four major zones in Punjab. T-tests confirmed the difference in diversity of species in different orders (Table 10). Fig 1 shows the comparison of specie diversity of various orders in four crops of two zones. Accordingly, almost all orders were highly diversified with respect to species richness including pest and predator species except Araneae which had significantly high diversity in

36 all crops of CWZ. Similarly Coleoptera which included major part of coccinellid predators had higher species diversity in all four crops of MCZ. Furthermore, the faunal species from eight sampling units (four crops in each zone) were transformed into three principal components. Multiple linear regression was applied on these dependent variables to check the effect of four environmental factors (independent variables). Environmental factors were, Temperature, Relative humidity, Rainfall and Wind velocity. The MLR was statistically significant in Order Hemiptera, Coleoptera, Lepidoptera, Hymenoptera and Araneae at F-ratio 10.019, 9.725, 9.264, 10.091 and 9.364 respectively at df 3,4 for PCA component-I as shown in (Table 11). The correlation of rainfall with a value of 90.9 in Hemiptera, 90.2 for Coleoptera, 35.8 for Lepidoptera, 39 for Hymenoptera and relative humidity 55 in Araneae were more pronounced than those of other environmental factors. According to Trewavas, (2001) the rain water and humid environment is always suitable for beetle (larvae, pupae) and spiderlings to achieve growth and development. Moreover the prey populations they consume also flourish in such environmental conditions of much vegetational growth. In the present situation although the R-value was not statistically significant for remaining orders even though the relative contribution of rainfall with a value of 42 in Odonata, 78.3 for Diptera, relative humidity 53.4 for Orthoptera and wind velocity 65.4 for others were more important correlates. Cluster analysis was performed to evaluate the habitat preferences of different species in the cropland. It was suspected that similar crop would support same faunal diversity irrespective of the locality. Interestingly many different clustering patterns were observed (Fig 2). Among them the wheat and mustard crop in CWZ were preferred by many species of Odonata, Hemiptera, Coleoptera, Diptera and Araneae. Second combination was of fodder and wheat in MCZ supporting a number of Odonata, Coleoptera and Diptera. Although the wheat crop wherever it was present supported similar diversity of Orthopteran and Lepidopteran species. Landscape structure influences local diversity by different movement pattern between natural habitats and as well as crop and non-crop interfaces. In such conditions generalist predators prefer the habitats where more food is available, in case one prey is absent its alternate is available in plenty (Tscharntke et al. 2005). As majority of dragons, beetles and spiders were generalist predators therefore they were present in the crops where aphids, grubs and larvae were present.

37 Identified specimens were categorized on the basis of their feeding habits. Six major categories defined were Phytophagous (Plant eater), Zoophagous (capture preys), Phytozoophagous (feed on both plant and animals fluids), phytosaprophagous (feed on dead remains of plants), zoosaprophagous (feed on dead remain of animlas etc.), and omnivores (feed on whatever is available) organisms. Among the predators order Araneae was most dominant with respect to diversity and abundance. Next was the order Coleoptera though less diverse but highly abundant with respect to family Coccinellidae having a pioneering role in control of different insect pests. They play their effective role as biocontrol agents for those crops that are especially susceptible to aphid attack, namely maize, alfalfa, canola, wheat, flax, the forage crops canary seed (or canary grass), peas, apples and potatoes (Zahoor et al. 2003; Khan & Suhail, 2001). Odonates were represented by 11 species of dragon and damsel flies. Odonata naiads or nymphs are aquatic and powerful predators of protozoa, fry, small tadpols, oligochaetes, larvae of flies, chiromonids, mosquitoes and bugs (Hussain & Ahmad, 2003). Trophic structure in the cropland of MCZ Trophic structure refers to the interconnected food chains. Food chain is a diagrammatic representation of who eats whom in an ecosystem. In the present study, identified faunal species were given different ranks on the basis of their feeding habits available in the recent literature. Accordingly, the crop and weed plants were the autotrophs while the arthropod fauna belonged to heterotrophs. These heterotrophs were further divided into six categories viz. phytophagous, zoophagous, phyto-zoophagous, phyto-saprophagous, zoo-saprophagous and omnivores on the basis of their feeding records in the agro-ecosystem (Binks et al. 2005). Figure 3 (a-d) showed the suspected trophic sketches of arthropod species in the present data of MCZ: i) Phytophagous (primary consumers) ii) Zoophagous (secondary consumers) iii) Phyto-Zoophagous (secondary consumers) iv) Phyto-saprophagous (secondary consumers) v) Zoo-saprophagous (secondary consumers) vi) Omnivores (tertiary consumers)

38 Phytophagous This group contain the species mainly feed on plants either crop or weed. But these cropland weeds relieves to certain extent the primary consumption burden on crop plants by offering alternate producers to these phytophagous species. It could be observed from the Fig 3(a) that in sugarcane crop 53% fauna belonged to this category. Out of these 17 species were the major pests while 30 were minor pests of sugarcane. The other 53 species have not yet been reported as major or minor pests. It meant that at least 100 species out of 190 used to feed on plants (either crop or weed). Fig 3 (b) showed that in fodder crop again 53% faunal species were phytophagous. Out of these 20 species were major pests while 17 were minor pests of fodder. Rest 44 species have not yet been reported as major or minor pests. Thus 81 species out of 155 used to feed on plants (either crop or weed). Fig 3 (c) showed the situation in wheat crop. 52% faunal species were phytophagous. Out of which 9 species were major pests while 7 were minor pests of wheat. Remaining 51 species have not yet been reported as pest. It meant that 67 species out of 126 used to feed on plants either on crop or weeds. Fig 3 (d) showed the situation in mustard crop. 58% faunal species were phytophagous. Out of which 7 were major pest and 5 were the minor pests of mustard crop. Remaining 50 species have not yet been reported as pest. Thus 62 species out of 119 used to feed on plants either crop or weeds. Zoophagous This group contains the organisms mainly feed on phytophagous species hence, know as predators or the natural enemies of insect pests. Fig 3 (a) showed that in sugarcane crop 24% species were zoophagous followed by 25%, 28% and 26% zoophagous species in fodder, wheat and mustard crops as shown in Fig 3 (b, c & d) respectively. Majority of the predators belonged to order odonata, coleoptera, araneae, few members of diptera and hymenoptera. The arthropods belonging to these orders could be used as efficient biological controlling agents against different crop pests. Phyto-Zoophagous Another interesting category which included the species feed on both plants and animals is termed as phyto-zoophagous. Main reason involved in this dual nature of feeding seemed to be the difference in larval and adult feeding habits. Majority of the larval stages of insects are zoophagous while in adult stage they are nectar feeders. In sugarcane crop

39 8.4% species were phyto-zoophagous followed by 5%, 4% and 5% species in fodder, wheat and mustard crop respectively as shown in Fig 3 (a, b, c & d). Major Pests They are usually defined as the unwanted organisms such as insects, pathogens and small animals that compete with human for food, shelter, threaten their health, comfort and welfare, and cause serious problems. In the present study majority of the species which feed on crop plants and weeds found within the crop, considered as pests. All the hemipteran, lepidopteran, coleopteran and dipteran pests were common in the croplands of MCZ (Table 12). The major crop pests found only in the cropland of this zone included Scirpophaga novella, Chilo infuscatellus, Emmalocera depressella, Myllocerus spp., and Cephus cinctus. Minor Pests These are the organisms which does not seriously harm the crop plants even after reaching a certain threshold level. The orthopteran, hemipteran, coleopteran, lepidopteran and dipteran pests were common in the cropland of this zone (Table 13). Majority of the minor pests were abundant in the crops of this area. The minor crop pests found only in the cropland of this zone included Paraletrix semihirexitus, Locusta migratoria, Chrotogonus nymph, Tettigoniidae nymph, Acrida nymph, Phlaeoba spp and Coreidae nymph. Trophic structure in the cropland of CWZ Figure 4 (a-d) showed the suspected trophic sketches of arthropod species in the present data of CWZ: Phytophagous It could be observed from the Fig 4 (a) that in sugarcane crop 58% fauna belonged to this category. Out of these 13 species were the major pests while 27 were minor pests of sugarcane. Rest 50 species have not yet been reported as major or minor pests. It meant that at least 90 species out of 155 used to feed on plants (either crop or weed). Fig 4 (b) showed that in fodder crop 48% faunal species were phytophagous. Out of these 18 species were major pests while 17 were minor pests of fodder. Rest 39 species have not yet been reported as major or minor pests. Thus 74 species out of 148 used to feed on plants (either crop or weed). Fig 4 (c) showed the situation in wheat crop. 52% faunal species were phytophagous. Out of which 8 species were major pests while 7 were minor pests of wheat. Remaining 40 species have not yet been reported as pest. It meant that 55 species out of 116 used to feed

40 on plants either on crop or weeds. Fig 4 (d) showed the situation in mustard crop. 43% faunal species were phytophagous. Out of which 7 were major pest and 1 was the minor pests of mustard crop. Remaining 41 species have not yet been reported as pest. Thus 49 species out of 115 used to feed on plants either crop or weeds. Zoophagous Fig 4 (a) showed that in sugarcane crop 26% species were zoophagous followed by 32%, 28% and 37% zoophagous species in fodder, wheat and mustard crops as shown in Fig 4 (b, c & d) respectively. Majority of the predators belonged to order odonata, coleoptera and araneae. The arthropods belonging to these orders could be used as efficient biological controlling agents against different crop pests. Phyto-Zoophagous In sugarcane crop 5% species were phyto-zoophagous followed by 5%, 4% and 8% species in fodder, wheat and mustard crop respectively as shown in Fig 4 (a, b, c & d). Major Pests All the hemipteran, coleopteran, lepidopteran, and dipteran pests were common in the croplands of CWZ (Table 12). The major crop pests found only in the cropland of this zone included only a single species Bemisia tabaci recorded from fodder crop. Among different pests of the croplands most of the species of aphids and bugs were abundant in this area indicating the perturbation in the agro-ecosystems of this zone. Minor Pests The orthopteran, hemipteran, coleopteran, lepidopteran and dipteran pests were common in the cropland of this zone but no specific minor pest of this area was recorded (Table 13). Majority of Orthopterans and Lepidopterans are known crop pests whereas most of the Coleopteran, Hymenopteran and Araneae species are natural enemies of crop pests. In MCZ more habitats in the form of phytomorphic heterogeneity were available to faunal species as compared to CWZ, which agreed with that of Bos et al. (2007) who also found most of the pest and predator species residing in the agro forestry systems with a diversity of shade trees in tropical areas in addition to pristine forest reserves. Outstanding diversity and abundance of predators could be due to the lesser sensitivity to chemicals of Arachnid and Coccinellid predators which shared fairly in the samples. Their existence could be

41 interpreted in the light of findings of Feber et al. (1998) who concluded that the abundance and diversity of spiders was directly affected by the increased levels of understory vegetation in the organic fields. Wisniewska & Prokopy (1997) reported that if pesticides were only used early in the growing season spider populations increased. Spatial limitations of pesticides also resulted in higher spider numbers since they could move out of the treated area and returned when the chemical dissipated (Balanca & de Visscher, 1997). CONCLUSION From the above study it could be concluded that Orthoptera, Hemiptera, Coleoptera and Araneae were the dominating orders in both zones with significantly greater diversity of the former three orders in MCZ and the later one in CWZ. As discussed above the significantly greater diversity of Araneae could be related with the relative humidity which had been low in the relatively arid climate of CWZ. By and large, it could be stated that mixed cropping system with reduced chemical applications was relatively better with respect to the conservation of biodiversity of cropland.

42 REFERENCES Alteiri, M. A. 1999. The ecological role of biodiversity in agro-ecosystems. Agric. Ecosyst. Environ., 74: 19-31. Balanca, G and M. N. de Visscher. 1997. Impacts on non-target insects of a new insecticide compound used against the desert locust. Arachnol. Environ. Contam. Toxicol., 32: 58-62. Bos, M., P. Hohn, S. Saleh, B. Buche, D. Buche, I. Steffan-Dewenter and T. Tscharntke. 2007. Insect diversity responses to forest conservation and agro-forestry management. Environ. Sci., 978: 277-294. Chang, G. C. and P. Kareiva. 1999. The case for indigenous generalists in biological control. Pp. 103-105 in Hawkins, B. A and H. V. Cornell (Eds) Theoretical approaches to biological control. Cambridge University Press, Cambridge. Cherry, R. 2003. The effect of harvesting and replanting on arthropod ground predators in florida sugarcane. Florida Entomol., 86: 49-52. Feber, R. E., J. Bell, P. J. Johnson, L. G. Firbank and D. W. Macdonald. 1998. The effect of organic farming on surface active spider assemblages in wheat in southern England. J. Arachnol., 26: 190-202. Gurr, G. M. and S. D. Wratten. (Eds) 2000. Biological control: measures of success. Kluwer, Dordrecht. Hughes, J. B., A. R. Ives and J. Norberg. 2002. Do species interactions buffer environmental variation (in theory)? In Biodiversity and Ecosystem Functioning: synthesis and perspectives. M. Loreau, S. Naeem and P. Inchausti (eds.) Oxford University Press, New York, pp. 92-101. Hussain, R. and K. B. Ahmed. 2003. Damselfly Naiads (Odonata: Zygoptera) of Sindh- Pakistan. Int. J. Agric. Biol., 5: 53-56. Khan, H. A. and A. Suhail. 2001. Feeding Efficacy, Circadian Rhythms and Oviposition of the Lady Bird Beetle (Coccinellidae: Coleoptera) under Controlled Conditions. Int. J. Agric. Biol., 3: 384-386. Ludwig, L. A. and F. R. James. 1988. Statistical ecology. A primer on Methods and Computing. A wiley-International Publication, New York.

43 Magurran, A. E. 1988. Ecological diversity and its measurement, Princeton University Press, New Jersey. Matson, P. A., W. J. Parton, A. G. Power and M. J. Swift. 2007. Agricultural intensification and ecosystem properties. Science, 300: 504-509. Minor, M. 2005. Soil biodiversity under different land uses in New York State. The SUNY College of Environmental Science and Forestry in Syracuse, Moscow State University. Olfert, O., G. D. Johnson, S. Brandt and A. G. Thomas. 2002. Use of arthropod diversity and abundance to evaluate cropping systems. J. Agron., 94: 210-216. Siddiqui, M. J. I. 2005. Studies on the biodiversity of invertebrates in the wheat Triticum aestivum farm agro-ecosystems of Punjab, Pakistan. Ph.D. Thesis. Department of Zoology & Fisheries, University of Agriculture, Faisalabad, Pakistan Southwood, T. R. E. and M. J. Way. 1997. Ecological background to pest management. pp. 6-28. In: Rabb, R.L. and F.E. Guthrie (Eds.). Concepts of pest management. Raleigh, North Carolina State University. Symondson, W. O. C., K. D. Sunderland and M. H. Greenstone. 2002. Can generalist predators be effective biocontrol agents? Annu. Rev. Entomol., 47: 561-594. Trewavas, A. J. 2001. The Population/ Biodiversity Paradox. Agricultural Efficiency to Save Wilderness. Plant Physiol., 125: 174-179. Tscharntke, T., T. A. Rand and J. J. Felix. 2005. The landscape context of trophic interactions: insect spillover across the crop non-crop interface. Ann. Zool. Fennici, 42: 421-434. Wisniewska, J and R. J. Prokopy. 1997. Pesticide effect on faunal composition, abundances and body length of spiders in apple orchards. Environ. Entomol., 26: 763-776. Zahoor, M. K., A. Sohail, Z. Zulfiqar, J. Iqbal and M. Anwar. 2003. Biodiversity of scarb beetles (Scarabaeidae: Coleoptera) in agro-forest area of Faisalabad. Pakistan J. Entomol., 25: 119-130.

44 Chapter 4 WEEDS AS LIFE SOURCE FOR DIFFERENT ARTHROPOD SPECIES IN THE CROPLANDS OF PUNJAB

ABSTRACT

Weeds are considered as a constraint on crop production. Simultaneously these non-crop plants are part of primary production within the crop system and play essential role as life sources of many organisms including bio-control agents which help restricting pest populations well below the economic threshold. Crops like sugarcane, fodder, wheat and mustard were sampled for one year for the collection of weed flora and arthropod fauna staying on them. Quadrate method was used for sampling. Twenty eight weed species and eight major arthropod orders present on these weeds were identified. Majority of the weed plants were broad leaved while some were grassy. Review of weed data depicted considerable change in the weed flora of Punjab. This could be related to the intensive and extensive farming along with irrigation canal system brought drastic changes in the soil structure and climatic of the region. Most of the phytophagous species used weed plants as food and were fed upon by few zoophagous species who visited the weeds for their prey, shelter and egg laying for completion of their life cycle. Thus, weeds have a specific role within the agro-ecosystem and supporting biodiversity, an assurance of sustainability of the crop system. Key words: arthropods, weeds, crops, sustainable agriculture

88 INTRODUCTION Weeds are considered as undesired plant species interfering with the crop plants but actually they have functional importance within agro-ecosystems. They provide diversity to flora of an area, may play a pivotal role in ecosystem functioning by being companion of crop plants, the primary producers and thus help in trapping Sun’s energy at the first trophic level of energy pyramid of crop system. Arable weed species support a high diversity of insect species. Reduction or extinction of such associated insects or other taxa may cause perturbation resulting in collapse of crop system by pest outbreak in the absence of natural and potential predator taxa (Marshall et al., 2001). Weeds also provide alternate resources for phytophagous insects and indirectly serve zoophagous beneficial arthropod species when their preferred crop plants are absent (Norris and Kogan, 2005). Phytomorphic heterogeneity provides greater diversity of potential niches for organisms in the cropland. They indirectly effect crop via, their influence on beneficial insects. Use of plants by insects is a dynamic interaction, with characteristics of the insects (e.g. mandible structure) and plant (e.g. allelochemicals) affecting feeding behaviour. Thus weeds are closely related to crop and are also important in harbouring insects that attack crops (Capinera, 2005). Diversification of an agro-ecosystem by traditional means helps increase the diversity by lowering the damage level of phytophagous species because of inter specific competition among pest and non-pest species and improved natural prey-predator balance (Norris and Kogan, 2005). Careful observations regarding organisms associated with these floral species provide information about the sustainability of the cropping system (Hyvonen and Huusela-Veistola, 2008). Weeds can also be used as indicators of an agro-ecosystem (Siddiqui, 2005). Weeds also have a positive impact on the below-ground microbial biomass and especially on Mycorrhizal Fungi thus increasing the crop’s nutrient uptake efficiency (Douds and Millner, 1999). Furthermore, few weed species that were not found important for other animal groups were found to be important for phytophagous insects thus neutralizing phytophagy on crop plants. Traditionally maintained vegetation patches support higher weed populations where such patches are present, they are colonised by many arthropods. The response of arthropod groups to vegetation cover (bare ground, litter, crop cover, broadleaf weed cover and grass cover) is very important in studying a sustainable crop system, its faunal community

89 composition and components of the vegetation. Even where weed cover was relatively low, some relationships between arthropods and vegetation were seen (Johnson et al., 1996). Addressing few of the above roles of weeds in different crops following objectives were in view. i) Identification of major weed species associated with major crops of the area ii) Identification of faunal species associated with these weed plants iii) Role of these faunal species in the crop MATERIALS AND METHODS Based on different cropping patterns and agro climatic conditions, cultivations in Punjab are classified into different zones. Two of them are Mixed-crop zone (MCZ) and Cotton-Wheat zone (CWZ). The flora and fauna of these zones are suspected to be affected little due to different cropping systems there. One year study was conducted in sugarcane, fodder, wheat and mustard crops from June 2008 to May 2009 (twice a month at each locality). Various cropland localities around the peripheral area of Central Punjab representing MCZ and around Multan representing (CWZ) were selected randomly. At each locality two acres each of the available crop of sugarcane, fodder, wheat and mustard were randomly selected. Fauna associated with the weed plants was collected by quadrate method. Three 1 x 1 m plots 10 m apart were sampled in each acre. All the arthropods visible to naked eyes were collected from the weeds included immature and adults whether sitting, moving or residing (sticking on the foliage or stem) on weeds. Sampled specimens were kept in properly labeled vials containing laboratory grade alcohol with few drops of glycerine. Sampling was made by hand picking, hand net and automated sifters (60 sec) per quadrate. The respective weed plants were also preserved for later identification. For identification of weed species “Flora of Pakistan” by Cope et al., (1982) was consulted. Faunal identification was done with the help of available, related taxonomic information in “Fauna of British India”and online electronic keys available on different websites. Museum of the Department of Agri. Entomology, University of Agriculture, Faisalabad and Entomological Research Institute Jhang road Faisalabad was also consulted for this purpose. The trophic levels of each species (phytophagous, zoophagous and saprophagous) were confirmed with the help of recent available literature on internet.

90 Canonical correspondence analysis was used for exploratory and confirmatory analyses about preferences of various weed species by arthropods. The software was applied using Canoco Computer Package for Windows (version 4.5). RESULTS i) Central Punjab (MCZ Agro-ecosystems) A total of twenty weed species were reported, twelve were broad leaved while eight were grassy weeds. The species recorded were Anagalis arvensis, Anethum graveolens, Chenopodium album, Cenchrus setigerus, Cichorium intybus, Cnicus arvensis, Convolvulus arvensis, Conyza boneriensis, Coronopus didymus, Cynodon dactylon, Dichanthium annulatum, Fumaria indica, Melilotus indica, Malvastrum coromandelianum, Phalaris minor, Parthenium hysterophorus, Rumex dentatus, Saccharum bengalense, Sonchus oleraceous and Vaccaria hispanica. Of these, fifteen species were observed in sugarcane, eight in fodder, six in wheat and four in mustard crop (Table 1). Different faunal species (arthropods) were collected from these weeds. Canonical Correspondence Analysis (CCA) In ecology, habitat plays a major role in designing the structure of a community. CCA analysis was used to determine the degree of association between faunal and floral species. The stability and sustainability of an agro-ecosystem seems to be dependant on such associations. Sugarcane weeds Figure 1 shows CCA for the arthropods associated with fifteen weeds of sugarcane field. The length of an arrow showed the strength of association. A strong association of some arthropods with the weeds namely, S. bengalense, D. annulatum, M. indica and A. graveolens was observed. The species associated with S. bengalense were Cheriacanthium vire, Oxyopes sertatus, Oxyopes spp., Clubiona phragmitus, Solenopsis invicta, S. xyloni, and Camponotus pennsylvanicus among the predators while Helicoverpa zea, Xysticus atrimaculatus, Dysdercus mimulus, Anopheles spp., A. stephensii, and Culex pipiens among the preys/pests. Only a single species C. carnea was associated with D. annulatum. Similarly the species associated with M. indica were Adalia punctata the only single predator while Coreidae nymph, Lygaeus lineolaris, L. triticus, Lygaeus spp., Bagrada hilaris and Aphis gossypii among preys/pests. The species associated with A. graveolens

91 were Attrecus affinus, Calliphora vicina, Apis dorsata, Oonops spp., and Misummena menoka among predators while Tanymecus palliates, Rhopalosiphum padi and Orbellia orbellia among preys/pests. Fodder weeds Figure 2 shows CCA for the arthropods associated with eight weeds of fodder field. But a strong association of some arthropods with the weeds namely, C. album, C. arvensis, C. didymus, A. arvensis and C. setigerus was observed. The species associated with C. album were Acheta domesticus, Tetrix subulata, and Schistocerca rubiginosa. Similarly the species associated with C. arvensis and C. didymus were Coccinella larvae, Camponotus sayi, C. pennsylvanicus, Formica spp. among predators while D. singulatus, D. mimulus, D. calmii, Schizaphis graminum, and A. stephensii among preys/pests. Interestingly only two predator species Chrysoperla carnea and C. viridiana were associated with A. arvensis and C. setigerus. Wheat weeds Figure 3 shows CCA for the arthropods associated with six weeds of wheat field. A strong association of some arthropods with the weeds namely, P. minor, A. arvensis, and R. dentatus was observed. Only a single species C. pennsylvanicus was associated with P. minor. While the species associated with A. arvensis were Episyrphus baltaetus, Califora vicina, Syrphus ribessi, Anopheles spp., A. stephensii, Dipterous larvae, Musca domestica, O. orbellia and C. pipiens. Similarly the species associated with R. dentatus were Camponotus spp., C. sayi, Solenopsis invicta, S. xyloni, Formica spp., F. rufa, Apis dorsata and C. carnea among predators while L. brassicae and X. atrimaculatus among the preys/pests. Mustard weeds Figure 4 shows CCA for the arthropods associated with four weeds of mustard field. But a strong association of some arthropods with the weeds namely, C. intybus, S. oleraceous, and P. hysterophorus was observed. The species associated with C. intybus were Paederus littoralis, A. puctata, Brumoides suturalis, Cheilomenes sexmaculata, Micraspis allardi, F. rufa, C. sayi, and S. xyloni. Similarly the species associated with S. oleraceous were E. baltaetus, Dipterous larvae, A. maculates among preys/pests while Formica spp., S. invicta, Camponotus spp. and C. pennsylvanicus among predators. The species showed

92 association with P. hysterophorus were Acrididae nymph, Acrida ungarica, Tetrix subulata, T. brunneri, A. gossypii, Pentatomidae nymph, S. graminum, Mayetiola destructor, D. singulatus, D. calmii, D. mimulus and E. servus. All are the known pest of different crops. ii) Multan or Lower Punjab (CWZ Agro-ecosystems) A total of fifteen weed species were reported, eleven were broad leaved while four were grassy weeds. The species recorded were Aphylla mediflora, Chenopodium album, Cnicus arvensis, Convulvulus arvensis, Cynodon dactylon, Chenopodium murale, Cyperus rotundus, Euphorbia hirta, Eclipta alba, Malvastrum coromandelianum, Oxalis corniculata, Phalaris minor, Rumex dentatus, Solanum nigrum and Trianthema partulacastrum. Of these ten species were observed in sugarcane, seven in fodder, four in wheat and three in mustard crop (Table 1). Different faunal species (arthropods) were collected from these weeds. Sugarcane weeds Figure 5 shows CCA for the arthropods associated with ten weeds of sugarcane field. A strong association of some arthropods with the weeds namely, C. murale, E. hirta, C. arvensis and O. corniculatus was observed. The species associated with C. murale were Coccinella larvae, M. allardi, F. rufa, Formica spp., and C. pipiens among the predators while Limepithema humile, Lucilia sericata and Discus rotundus among the preys/pests. Similarly the species associated with E. hirta were Lestes spp., Coenagrion spp., Phyllodermia spp., C. undecumpuctata, A. affinus, and Hippodemia convergens among the predators while Pyrilla perpusilla, Nezara viridula, R. padi, and E. pustulatus among the preys/pests. The species associated with C. arvensis were C. septumpunctata, A. punctata, B. suturalis, P. littoralis Oxyopes spp., C. lutescens and A. mellifera among the predators while Acrididae nymph, Schistocerca nitens, Chorthipus brunni, L. lineolaris, Galleria ganus were among the preys/pests. Similarly the species associated with O. corniculatus were Neoconocephalus ensiger, Cotesia flavipes, Apis dorsata, and Oxyopes sertatus among predators while Chloealtis conspersa, Pyrallid larvae and Melliscava auricallis among the preys/pests.

93 Fodder weeds Figure 6 shows CCA for the arthropods associated with seven weeds of fodder field. A strong association of some arthropods with the weeds namely, R. dentatus, C. dactylon and S. nigrum was observed. The species associated with R. dentatus were Lestes spp., N. ensiger, B. suturalis, C. flavipes, A. dorsata, Oxyopes spp., and O. sertatus among predators while S. nitens, Arphia conspersa, C. brumcus, Pyrallid larvae, and M. domestica among preys/pests. Similarly the species associated with C. dactylon were C. septumpunctata, A. punctata and P. littoralis among predators while Acrididae nymph, Phyllodemia spp., D. mimulus, M. autumnalis, P. brassicae and Porcellionides pruinosus among preys/pests. The species associated with S. nigrum were C. undecumpuctata, E. baltaetus, F. rufa, and C. pennsylvanicus among predators while Taylorilygus apicalis, P. perpusilla, Dysdercus voelkeri and Miridae nymph were among the preys/pests. Wheat weeds Figure 7 shows CCA for the arthropods associated with four weeds of wheat fields. A strong association of some arthropods with the weeds namely, C. murale, C. dactylon and P. minor was observed. The species associated with C. murale were M. allardi, B. suturalis, C. septumpunctata, H. convergens, O. javanus, O. sertatus, C. inclusum, C. lutescens, and Oxyopes spp. among predators while Monomorium minimum, F. fusca, D. singulatus, Geocoridae nymph, Chlorops spp. and M. domestica were among the preys/pests. Similarly the species associated with C. rotundus were E. baltaetus, Hispa atra, C. sexmaculata, C. similare, C. sayi, and C. rostrata among predators while X. atrimaculatus, Blattela asahinai, Miridae nymph, E. servus, M. millenium and M. domestica among preys/pests. Majority of the phytophagous species namely X. californicus, H. armigera, O. olens, A. flava and a single predator species C. novemnotata were associated with P. minor. Mustard weeds Figure 8 shows CCA for the arthropods associated with three weeds of mustard crop. A strong association of fauna was observed with all these weeds. The species associated with C. album were A. turita and B. asahinai among preys/pests while H. convergens, C. rostrata, Clubiona spp., O. javanus and F. fusca were among the predators. Similarly the species associated with C. rotundus were Acrididae nymph, P. perpusilla, X. californicus, H. armigera, A. flava, S. spp. among preys/pests while P. littoralis, M. allardi, C.

94 septumpunctata, C. lutescens and Araneae nymph were among the predators. The species associated with C. arvensis were A. conica, D. cingulatus, L. kalmii, M. mellinium, M. domestica among preys/pests while C. septumpuctata, B. suturalis, E. baltaetus, C. sayi, S. invicta and O. saradae were among the predators. DISCUSSION Weeds are generally considered as competitors of crop plants but there is another view point that they add phytomorphic heterogeneity which sustains many arthropod species including beneficial bio-control agents. They also provide food to phytophagous insects and help neutralizing the potential pest attack. Moreover, weed seeds are food of many granivorous birds. In this way weeds play an important role in structure of a crop system (Newton, 2004). Present study is an attempt to access the positive role of various weeds occurring along with four major crop plantations. Many agricultural studies have shown significant yield increases in diverse cropping systems. Ecological studies suggested that more diverse plant communities are more resistant to disturbance and more resilient in the face of environmental perturbation (Alteiri and Nicholls, 1999). In the present study twenty eight weed species were identified from selected crops of Central Punjab and Multan, of which ten were grassy. Whereas, Ashiq et al., (2003) has reported nearly 50 weed species in the cropland of Punjab of which fifteen were grassy. The presence of more grassy weed species in our study area is an indication of changed condition of soil from light sandy to loamy. The application of fertilizers in the fields is one of the major factor in changing soil conditions. In addition the farming practices and tillage also has a great impact on weed flora (Siddiqui, 2005). Four weeds viz. S. bengalense, D. annulatum, M. indica and A. graveolens were significantly preferred by many important pests as well as predator species. Generalist predators such as spiders and hymenopterans and some preys/pests showed their affinities with these weeds in sugarcane. Similarly five weeds viz. C. album, C. arvensis, C. didymus, A. arvensis and C. setigerus were preferred by important pest and predators in fodder. Beetles and green lace wing showed their affinities with these weeds in fodder. Three weeds viz. C.intybus, A. arvensis, and R. dentatus were significantly preferred by many important pests of the cropland in wheat. Similarly three weeds C. intybus, S. oleraceous, and P. hysterophorus were preferred by coccinellid predators and important crop pests in mustard.

95 Majority of the phytophagous species were found to be suspected weed feeder thus releasing the burden on crop. Rest of the species belonged to higher trophic guild also share weeds as provision of cover, reproduction sites and structure within the crop system as indicated by (Brown and Hyman, 1995). The predator species are of particular importance in maintaining natural predator-prey balance in the cropland. Outstanding diversity and abundance of different predator groups could be interpreted in term of their high resistant power against a specific type of stress. Araneae, Coleoptera and some Hymenoptera predators were the best example of this trend which shared fairly in the sample. Their existence could be interpreted in the light of findings of Feber et al. (1998) who concluded that the abundance and diversity of these taxa was directly affected by the increased levels of under story vegetation in the crop fields. C. album, A. arvensis, R. dentatus and F. indica had a record of supporting 31, 50, 8 and 3 species of insects respectively (Marshall et al., 2003). Similarly, in the present study the first three weed species were found to support different arthropod species in the croplands. The weed species of family Polygonaceae and Chenopodiacea are part of food items for birds (Buxton et al., 1998). Whereas, insects constituted 42% of the food items taken by little spotted owl (Athene bramalso) and 33% of small indian mongoose (Herpestes auropunctatus) in the cropland of district Sheikhupura and Faisalabad (Mushtaq-ul-Hassan et al., 2003; Rana et al., 2005). Thus weeds and their fauna are playing a key role in stability of an agro-ecosystem.

CONCLUSION Weeds are important component of crop system as they enhance the floral diversity. They are used by many pest and predator species as alternate food source, breeding site and shelter. Certainly they play a very positive role as life source for many phytophagous and zoophagous taxa.

96 REFERENCES Altieri, M. A. and C. I. Nicholls. l999 Biodiversity, ecosystem function and insect pest management in agricultural systems. In: Biodiversity in Agroecosystems. W.W. Collins and C.O. Qualset (eds) pp. 69-84. CRC Press, Boca Raton. Ashiq, M., M. M. Nayyar. and J. Ahmed. 2003. Weed Control Handbook for Pakistan. Directorate of Agronomy. Ayub Agriculture Research Institute, Faisalabad. pp. 11- 184. Brown, V. K. and P. S. Hyman. 1995. Weevils and plants: characteristics of successional communities. Entomological Society Washington, 14: 137-144. Capinera, J. L. 2005. Relationship between insect pests and weeds: an evolutionary perspective. J. Weed Sci., 53: 892-901. Cope, T. A., Nasir, Y. J. and S. I. Ali. 1982. Flora of Pakistan, 143:205- 207.

Douds, D. D. and P. Millner. 1999. Biodiversity of arbuscular mycorrhizal fungi in agroecosystems. Agriculture Ecosystem and Environment, 74: 77-93. Feber, R. E., J. Bell, P. J. Johnson, L. G. Firbank and D. W. Macdonald. 1998. The effect of organic farming on surface active spider assemblages in wheat in southern England. Journal of Arachnology, 26: 190-202. Hyvonen, T. and E. Huusela-Veistola. 2008. Arable weeds as indicator of agricultural intensity (A case study from Finland). Biological Conservation, 141: 2857-2864. Johnson, W. C., J. W. Todd, A. K. Culbreath, and B. G. Mullinix. 1996. Role of warm- season weeds in spotted wilt epidemiology in the southeastern coastal plain. J. of Agronomy, 88:928-933. Marshall, E. J. P., V. Brown, N. Boatman, P. Lutman and G. Squire. 2001. The impact of herbicides on weed abundance and biodiversity. p 940. A report for the UK pesticides safety directorate. IACR-Long Ashton Research Station, UK. Marshall, E. J. P., V. Brown, N. Boatman, P. Lutman, G. Squire and L. Ward. 2003. The role of weeds in supporting biological diversity within crop fields. Weed Research, 43: 77-89.

97 Mushtaq-ul-Hassan, M., A. Gill, B. Dar and M. I. Khan. 2003. Diet of little spotted owl (Athene brama) from Faisalabad and Sheikupura, Pakistan. Acta Zool. Bulg., 55: 53- 58. Newton, I. 2004. The recent decline of farmland bird populations in Britian: an appraisal of causal factors and conservation actions. Ibis, 146: 579-600. Norris, R. F and M. Kogan. 2005. Ecology interactions between weeds and arthropods. Annual Review of Entomology, 50: 479–503. Rana, S. A., S. M. Smith and M. J. I. Siddiqui. 2005. Scat analysis of small Indian mongoose (Herpestes auropunctatus) feeding on fauna of some high and relatively low input crop fields of Faisalabad, Pakistan. Int. J. Agriculture and Biology, 7: 777- 780. Siddiqui, M. J. I. 2005. Studies on the biodiversity of invertebrates in the wheat Triticum aestivum farm agro-ecosystems of Punjab, Pakistan. Ph.D. Thesis, Department of Zoology and Fisheries, University of Agriculture, Faisalabad.

98 Chapter 5 PREDATOR-PREY ASSOCIATION AMONG SELECTED ARTHROPOD SPECIES IN THE CROPLAND OF PUNJAB, PAKISTAN ABSTRACT In this paper, a simple mathematical formula was used to analyze the dynamics of predator- prey relationship. Arthropods are the major group of invertebrates found in the cropland, including many beneficial predators, parasitoids, pollinators and prey/pests. Fodder (Maize, Sorghum, Alfalfa), Wheat and Brassica crops were sampled round the season. The most abundant predator species Coccinella septumpunctata, Cheilomenes sexmaculata, Hippodemia convergens, H. variegata, Chrysoperla carnea, Oxyopes javanus, Neoscona theisi and Araneidae nymph while abundant preys/pests Aphis maidis, Schizaphis graminum, Macrosiphum miscanthi, Empoasca kerri, Lepidopterous larvae and Musca domestica were selected. Predator-prey ratios (p/p) were calculated from monthly abundance data and horizontal linear graphical pattern showed the probable association of a predator with selected preys. A. maidis showed the best probable association with all the beetles and O. javanus (spider), S. graminum with green lacewing while M. domestica seemed to be associated with all the spiders. Chi-square test was applied on the relative abundance of a predator and associated prey to check the significance of association. Such findings seemed to be helpful in ascertaining the species-specific biological control in the field. Key words: Croplands, Arthropods, Predator-prey relationship

109 INTRODUCTION Arthropod predator-prey relationship is playing a key role in the stability of an ecosystem by maintaining many natural processes. Beneficial arthropods including predators and parasitoids provide valuable services by maintaining agricultural productivity and reduce the need for pesticide inputs to agriculture each year. These arthropod-mediated ecosystem services (AMES) include crop pollination and pest control (Isaacs, et al. 2009). Local patterns (biodiversity) and processes (trophic interactions) are influenced by the regional setting. The ratio of the foraging range and/or dispersal ability influences local population dynamics. The seasonal distribution of a species may be linked to its trophic status (Tscharntke and Brandl, 2003). Predator-prey interaction is an important factor in the ecology of populations, determining mortality of prey and birth of new predators. Prey defense can be a stabilizing factor in predator-prey interactions while predation can be a strong agent of natural selection. Predation alone is not the only cause of complex community interaction but it has strong indirect and cascading effects on an alternate prey (Purves, 2000). The population dynamics of predator-prey interactions moves in cyclic form. Predators may be put to use in conservation efforts to control introduced species. Besides their use in conservation biology, they are also important for controlling pests in agriculture. Natural predators are among the sustainable means of reducing damage to crops and are an alternate to use of chemical agents such as pesticides (Stanley, 2008). Dietary breadth represents a key component in the ecology, behavior, and evolutionary diversification of predators. A predator's use of these dietary resources has important implications for the outcome and stability of predator-prey dynamics (Begon et al. 1996). Coccinellids are the predators of aphids and coccids. C. septempunctata, C. sexmaculata, H. convergens and H. variegata are the predominant predators of agro- ecosystems and of great economic importance (Omkar and Pervez, 2000; Omkar and Srivastava, 2001). Previously the presence of a ladybird at a prey-site was considered as a proof of its food choice. But this inference was criticized due to inadequate information on their life history studies (Hodek and Honek, 1996). All aphid species are not equally suitable for these generalist predators and they exhibit a choice for certain aphid species (Omkar et al. 1997).

110 The green lacewing C. carnea is an effective predator of aphids in various agro- ecosystems of Indo-Pak region. Because of its abundance and broad habitat range it is extensively studied as an effective bio-control agent for most of the crop pests (Tauber et al. 2000). Although it is a generalist predator, the preference for different aphid species varies from crop to crop. There are no records of lacewing feeding on Lipaphis erysimi (a vegetable aphid). A review of manipulative field studies showed that in approximately 75% of cases, generalist predators, whether single species or species assemblages, reduced pest numbers significantly (Symondson, 2002). Spiders rapidly colonize the new habitat as they are euryphagous in feeding therefore considered as natural enemies of different crop pests. Although semi-desert conditions are a potential source for spiders but they migrate into arable land during winter. The high productive wheat fields might attract phytophagous insects that in turn attract their predators including spiders. However, the population growth of spiders is greatly effected by disturbance in arable land (Pluess et al. 2008). Keeping all in view the aim of the present study was to determine the predator-prey relationship among dominant arthropod species and their use in species specific biological control programme.

111 MATERIALS AND METHODS

The study was conducted in two zones viz. Faisalabad and Multan. Faisalabad stands in the rolling flat plains of northeast Punjab, between longitude 73°74 East, latitude 30°31.5 North, with an elevation of 184 metres above sea level with a summer maximum temperature 50 °C (122 °F) and a winter temperature of −1 °C (30.2 °F). The average yearly rainfall lies only at about 400 mm and is highly seasonal with approximately half of the yearly rainfall in the two months July and August while Multan is located in southeran part of Punjab, between longitude 72°4 East, latitude 30°45 South, with an elevation of 215 metres above sea level with a summer maximum temperature 54 °C (129 °F), and in the winter −1 °C (30.2 °F). The average yearly rainfall is roughly 127 mm and dust storms are the common features of local climate.

Fields of fodder, wheat and brassica in each zone were sampled for collection of arthroopod fauna (twice a month at each locality). At each locality two blocks each of more than five acres of different crop were taken. Then two acres of each of the available crop were randomly selected for faunal collection. Different methodologies like hand picking, sweep nets and automated sifters were used for foliage collection. The collected specimens were preserved in glass vials containing laboratory grade alcohol with few drops of glycerine. The identification up to species level was done with the help of available, related taxonomic information in the “Fauna of British India” and online electronic keys available on different websites. The trophic guild of identified species was confirmed from recent available literature.

The common and most abundant species of predator and prey/pest in two zones were selected to analyze their association. The p/p ratio (of a predator with different available preys) was estimated by simply dividing the density of preys with the density of a predator in each set of monthly sample. The ratio was plotted against the time scale of monthly sample on graph. A horizontal straight line was considered as constant and best indicator of association between a predator and prey species in question. Chi-square test was applied on the relative abundance of selected predator and prey species to check the significance of association.

112 Assumption of p/p association

The predation efficiency depends on the cost-effective availability of its prey species. It also depends on the adaptive efficiency of predator in approaching, manipulating and utilizing the prey species. Quality and quantity of the prey species is also an important factor. Optimality model favors specialist/stenophagous predators and assures the availability of almost fixed number of preys in the area. Even for generalists there is some hierarchy of preferred food items and so is the adaptability of the individuals within the population of a generalist predator. Microsoft Excel 2003 and R language statistical computing were used for calculation of p/p ratio and Chi-value. RESULTS According to Cohen (1977) a predator is an organism that consumes any kind of food item available in the food web and a prey is an organism that is consumed by any predator in the food web. Predator-prey interactions may play an important role in explaining the population structure of a species in an area. To check the relationship between these two populations, dominant species of two groups were selected from field data of different cropland. Among predators C. septumpuctata, C. sexmaculata, H. convergens, H. variegata, C. carnea, Araneidae nymph, N. theisi and O. javanus were dominant while A. maidis, S. graminum, M. miscanthi, E. kerri, Lepidopteran larvae and M. domestica were dominant preys/pests of the croplands. Predator-prey associations In order Coleoptera, all the preys except E. kerri and Lepidopterous larvae showed horizontal linear significant association with C. septumpunctata (Fig 1). Although the maximum x2 (chi-value) was observed with A. maidis 38.28 followed by S. graminum (32.34), M. miscanthi (30.79) and M. domestica (29.68) respectively. In case of C. sexmaculata only a single prey species A. maidis showed horizontal linear association with x2 value (49.04) while rest of the preys were showing fluctuations in ratio (Fig 2). In case of H. variegata and H. convergens again the A. maidis showed horizontal linear association (Fig 3, 4) with an x2 value (21.29) and (25.89) respectively. C. carnea is mostly seen on fodder, wheat and mustard crops (a major neuropteran predator). Here in the case it was observed that a known pest of wheat aphid S. graminum was showing horizontal linear

113 association with green lace wing (Fig 5) with an x2 value (28.09). Interestingly high predator-prey ratios were observed in order Araneae. Such results were an indication that more food was available to these predators in all the crops. M. domestica was showing horizontal linear association with O. javanus, N. theisi and Araneidae nymph (Fig 6, 7, 8) with an x2 value of (39.95, 36.64 and 31.74) respectively. Although A. maidis was observed in linear relation with O. javanus only (Fig 6) with an x2 value (40.25). DISCUSSION Predators are said to play an important role in environmental sanitations. But their number should not exceed the optimum range otherwise they have the potential to become the pest and damage the natural balance of organisms. There is a large degree of specialization among the predators. The specialists are usually particularly well suited to capturing their preferred prey while generalist captures each and every available prey item. An alternative view about predation is that it is a form of competition: the genes of both the predator and prey are competing for their survival (Doghairi, 2004).

Predator-prey models based on temporal basis interpret the consumption rate as a part of behavioral phenomenon. One of the classical assumption is that a predator encounter different preys at random and the trophic function depends on the abundance of prey only (Omkar, 1997). It is reasonable to say that trophic function depends on abundance ratio of predator and prey. Several field and laboratory studies support this hypothesis (Arditi and Ginzburg, 1989). The predator prey ratio appeared to be constant in food webs of different habitats of agro-ecosystems (Gaston, 1996). Similar trend was observed in the present study. After three year study on predator prey ratio in arthropods based on species richness and diversity Lockwood et al. (2005) found that a constant p/p ratio had lower values in more sensitive density ratios, which showed significant variation in time and space. Similar situation was observed in case of C. septumpuncata with four prey species as depicted in the form of horizontal straight line and significant chi-values but with lower p/p ratio. When the taxa within the food web are aggregated to larger trophic groups, little changes were observed in predator-prey density ratio (Closs et al. 1999). There are examples that most of the insect predators share same prey but some species are preferred over the other (Omkar, 1997). Presently it was observed that coccinellids showed preference for A. maidis, green lacewing for S. graminum while flies were the favourite food of spiders.

114 It was concluded from the data that Coccinellid fed mainly on aphids. According to a report by CNF Ladybug Survey (2000) lady bugs, eat aphids and other harmful pests such as scale insects, take care of the pest problems in gardens, orchards and farms. Their pioneering role in the development of biological pest control has rendered the Coccinellidae of great practical and scientific interest. About 90% of the coccinellid species are considered beneficial because of their predatory activity mainly against homopterous insects and mites. One reason for their success is their broad feeing niche and second is the wide distribution in all the crop fields. According to Iperti (1999) Coccinellids live in all terrestrial ecosystems: tundra, forest, grassland, agro-ecosystems, and from plains to mountains, euryphagous in feeding and euryhaline in nature. Such species could be used as bioindicator insects owing to their climatic and trophic characteristics. In the context of biological control, the coccinellids represent an important cause of mortality of coccids, aphids and mites.

Presently C. carnea was observed in direct association especially with the wheat aphids. It is useful against aphids specific to wheat crop. There are the predation record of C. carnea on the aphids, whitefly, thrips, bollworms, pear psyllids, mites, yellow stripped army worms and mealy bugs (Syed et al. 2005). Presently, the close association between C. carnea and S. graminum showed that the species could be used specifically against this pest. Similarly all the spiders showed close association with house flies. Spiders have a wide range of variation and lifestyle, foraging primarily on different kind of insects. Because of their high abundance and insectivorous foraging, they are suspected to play an important predatory role in agro-ecosystems, woodlands and other terrestrial ecosystems. Non- arthropod preys (earthworms, gastropods and small vertebrates) are rarely captured by spiders to supplement their arthropod diet (Nyffeler and Symondson, 2001). Prominent fluctuations were observed in majority of p/p ratios. The non-significant chi-values were further confirming the assumption. One of the probable cause for this imbalance is the crop intensification methodologies. Use of chemicals has increased to a greater extent in past few decades as observed in wheat farm agro-ecosystem of Punjab. The application of chemicals in these crop fields alters the predator, pest or parasitoid ratios thus causing more loss than benefit (Siddiqui, 2005). The affinities of coccinellids for single aphid species and spiders for house flies seemed to confirm the reduction of other prey species probably due to continuous use of

115 insecticides for controlling aphid attack especially on wheat and brassica. Seemingly, abundant A. maidis might also be the result of occupation of niches vacated by other aphid species present in cropland understory. Outburst of pest resistance has also been observed by Brown (1992). Further the species competing for same food source were also expected to face stress due to scarcity of food. Thus, the present study will be helpful to agronomists in providing the basic information about the arthropod fauna of these two areas. On this basis species specific control programmes could be designed to control different pest species, which would be useful in sustaining the crop system. This will not only lead to increase in crop yield but also stabilize the food web of agro-ecosystem. CONCLUSION Summarizing the present study, A. maidis was preferred by major coccinellid predators and a single spider species most probably due to its availability in all the crops. S. graminum was preferred by lacewing due to its abundance during wheat-brassica crop season while house flies were the best natural food for spiders being present round the year in almost every habitat.

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117 Nyffeler, M. and W. O. Symondson. 2001. Composition, abundance and pest control potential of spider communities in agroecosystem: a comparison of European and US studies. Agric. Eco. Environ. 95: 579-612.

Omkar, A. and A. Pervez. 2000. Biodiversity of predaceous coccinellids (Coleoptera: Coccinellidae) in India. Journal of Aphidology, 14: 41-67. Omkar, A. and S. Srivastava. 2003. Influence of six aphid prey species on development a reproduction of a ladybird beetle, Coccinella septempunctata. BioControl, 48: 379– 393. Omkar, A., S. Srivastava and B.E. James. 1997. Prey preference of a ladybeetle, Coccinella septempunctata Linnaeus (Coleoptera: Coccinellidae). J. Adv. Zool., 18: 96–97. Pluess, T., I. Opatovsky, E. G. Regev, Y. Lubin and M. H. Schmidt. 2008. Spiders in wheat fields and semi-desert in the Negev (Israel). Journal of Arachnology, 36:368–373. Purves, W.K., G.H. Orians and H.C. Heller. Life: The Science of Biology. Trophic Links: Predation and Parasitism, Oxford University Press, UK. Stanley (2008). "Predation defeats competition on the seafloor" (extract). Paleobiology 34:525-529. Syed, A. N., M. Ashfaq, and S. Khan. 2005. Comparison of development and predation of Chrysoperla carnea (Neuroptera: Chrysopidae) on different densities of two hosts (Bemisia tabaci, and Amrasca devastans). Pak. Entomol., 27: 41-44. Symondson, W. O., K. D. Sunderland and M. H. Greenstone. 2002. Can generalist predators be effective biological controlling agent? Annual Review Entomology, 47:561-94. Tauber, M. J., C. A. Tauber, K. M. Daane and K. S. Hagen. 2000. Commercialization of predators: recent lessons from green lacewings (Neuroptera: Chrysopidae: Chrysoperla). Am. Entomol. 46: 26–38. Tscharntke, T. and R. Brandl. 2004. Plant Insect interactions in fragmented landscapes. Annual Review of Entomology, 49: 405-430.

118 Table 1. Chi-square test showing the significance of horizontal linear association of selected predator and prey species in the cropland of Punjab

Order Predator Prey Chi-square value Remarks (x2)

Coleoptera Coccinella Aphis maidis 38.28 *** septumpunctata Schizaphis 32.34 " graminum Macrosiphum 30.79 " miscanthi Musca domestica 29.68 "

Cheilomenes A. maidis 49.04 " sexmaculata Hippodemia A. maidis 21.29 " variegate Hippodemia A. maidis 25.89 " convergens Neuroptera Chrysoperla carnea S. graminum 28.09 "

Araneae Oxyopes javanus M. domestica 39.95 "

A. maidis 40.25 "

Neoscona theisi M. domestica 36.64 "

Araneidae nymph M. domestica 31.74 "

***= significant value

119

6

5

cs vs ll 4 cs vs sg cs vs mm 3 cs vs ek Ratios cs vs am 2 cs vs md

1

0 Oct Nov Dec Jan Feb Mar Apr May Months

Fig 1: Predator-prey ratio of Coccinella septumpunctata with different available preys CS= Coccinella septumpunctata, ll= Lepidopterous larvae, sg= Schizaphis graminum, mm= Macrosiphum miscanthi, ek= Empoasca kerri, am= Aphis maidis, md= Musca domestica

120 6 5 csx vs ll 4 csx vs sg csx vs mm 3 csx vs ek Ratios 2 csx vs am 1 csx vs md 0 Oct Nov Dec Jan Feb Mar Apr May Months

Fig 2: Predator-prey ratio of Cheilomenes sexmaculata with different available preys csx= Cheilomenes sexmaculata

6

5 hc vs ll 4 hc vs sg hc vs mm 3 hc vs ek Ratios 2 hc vs am hc vs md 1

0 Oct Nov Dec Jan Feb Mar Apr May Months

Fig 3: Predator-prey ratio of Hippodemia convergens with different available preys hc= Hippodemia convergens

121 6

5 hv vs am 4 hv vs sg hv vs mm 3 hv vs ek Ratios 2 hc vs ll hc vs md 1

0 Oct Nov Dec Jan Feb Mar Apr May Months

Fig 4: Predator-prey ratio of Hippodemia variegata with different available preys hv= Hippodemia variegata

6

5 cc vs am 4 cc vs ll 3 cc vs mm

Ratios cc vs ek 2 cc vs sg 1

0 Oct Nov Dec Jan Feb Mar Apr May Months

Fig 5: Predator-prey ratio of Chrysoperla carnea with different available preys cc= Chrysoperla carnea

122 10

9

8

7 oj vs am 6 oj vs sg oj vs mm 5 oj vs ek Ratios 4 oj vs ll oj vs md 3

2

1

0 Oct Nov Dec Jan Feb Mar Apr May Months

Fig 6: Predator-prey ratio of Oxyopes javanus with different available preys oj= Oxyopes javanus

10

9

8

7 nt vs am 6 nt vs sg nt vs mm 5 nt vs ek Ratios 4 nt vs ll

3 nt vs md

2

1

0 Oct Nov Dec Jan Feb Mar Apr May Months

Fig 7: Predator-prey ratio of Neoscona theisi with different available preys nt= Neoscona theisi

123 10

9

8

7 an vs am 6 an vs sg an vs mm 5 an vs ek Ratios 4 an vs ll an vs md 3

2

1

0 Oct Nov Dec Jan Feb Mar Apr May Months

Fig 8: Predator-prey ratio of Araneidae nymph with different available preys an= Araneidae nymph

124 Chapter 6 DETERMINATION OF GENETIC DIVERSITY IN SOME SELECTED ARTHROPOD SPECIES USING RAPD (Random Amplified Polymorphic DNA) AND PREDATOR-PREY RELATIONSHIP BY PCR (Polymerase Chain Reaction) ABSTRACT Genetic diversity of twelve arthropod species viz. Coccinella septumpunctata, Cheilomenes sexmaculata, Hippodemia convergens, Hippodemia variegata, Oxyopes javanus, Neoscona theisi, Chrysoperla carnea, Macrosiphum miscanthi, Schizaphis graminum, Aphis maidis, Diuraphis noxia and Musca domestica was accessed by RAPD markers. A total of 195 fragmnets were amplified by using 25 RAPD primers. Out of which 182 fragments were polymorphic showing 93% polymorphism. The number of amplification products varied between 6 to 10 with an average of 7.08 per primer. Genetic characterization was done with the help of cluster analysis constructed on the basis of similarity matrix. In interspecific comparisons three main cluster groups were depicted. In one group only (phytophagous) prey species were present. Second group contained all (zoophagous) predator species. Among them arachnids were in a separate cluster from the rest of predator species. A single band of approximately 1100bp was identified in DNA sample of Macrosiphum miscanthi. Such fragments could be used as fingerprints for the identification of species. Predator-prey relationship was observed on the basis of comparison among control and fed predators. Few species specific fragments of preys were identified in the fed predators, suggesting a trophic link between predator and prey species studied. Key Words: Arthropods, Diversity, RAPD, Predator-prey relationship, PCR

128 INTRODUCTION Arthropods are the most diverse group of organism in the agro-ecosystem. Ecosystem based arthropod fauna are integral to evaluate existing cropping practice and aid in redesigning of farming systems to make them economically viable and environment sustainable. The potential of arthropod predators to suppress pest populations has been thoroughly established by recent literature reviews (Chang and Kareiva 1999; Symondson et al. 2002). Arthropods, including insects, spiders and mites are integral to crop loss and soil health because they include both beneficial and pest species. Cropping systems must incorporate the relationship between farm practices and the ecosystem to create an equilibrium where farm inputs enhance rather than replace natural processes. From past few years there has been stress on the interaction of agronomic practices with ecological processes within their ecosystem. To meet these needs, the complexity and dynamic nature of ecological processes within agro-ecosystem requires a systematic approach to research (Olfert et al. 2002). Genetic diversity assessment of different arthropod species can facilitate an understanding of their biology and ecology but little effort has been made to access their genetic characterization. RAPD (Random Amplified Polymorphic DNA) has proved to be particularly valuable, owing to the wide availability of commercial primers and the lack of any need for DNA sequencing information prior to analysis (Williams et al. 1990). RAPD- PCR has the advantage of being quick and easy, requiring little material, and can be used reliably and inexpensively to detect genetic polymorphism among organisms (Fritsch and Rieseberg 1996). Recent reports revealed that the RAPD markers have been widely used in applied and basic research program in many different fields (Li and Jin, 2006; Sreekumar and Renuka, 2006) and have proven to be a powerful tool for assessing population structure and are not affected by variation in ploidy levels (Zhao et al. 2008). Analysis of insect predator gut contents is very useful in providing information on trophic interactions and predator-prey dynamics. Direct field observations are not very helpful in this regard whereas molecular experiments tracking trophic interactions in food-webs through polymerase chain reaction (PCR) provides the mean for amplification and thus visualizing the DNA (Sheppard and Harwood 2005).

129 MATERIALS AND METHODS Seven generalist predators and five prey species abundant in the field sample were selected for molecular analysis. Zoophgaous species comprised of Coccinella septumpunctata, Cheilomenes sexmaculata, Hippodemia variegate, Hippodemia convergens, Chrysoperla carnea, Neoscona theisi and Oxyopes javanus. While phytophagous species comprised of Macrosiphum miscanthi, Schizaphis graminum, Aphis maidis, Diuraphis noxia and Musca domestica. Former three species were the major pest in wheat crop and later two infest fodder crops. DNA extraction The collected specimens were immediately stored in 100% ethanol separately in 5ml glass vials and genomic DNA was extracted using method with few modifications as devised by (Zhu and Greenstone, 1999). For each predator species DNA extraction was made in three groups. Adult body of the individual (After feeding), whole larvae if present (in case of Coccinellids) and only head of the adult individual in order to avoid contamination of DNA from other body parts (used as control). The whole body of each prey species was taken separately for DNA extraction. Total genomic DNA concentration was measured by spectrophotometer (AARI, USA) at 260nm wavelength. Quality of DNA was checked by running 5µl of extracted DNA on 0.8% agarose gel prepared in 0.5X TBE buffer. The DNA samples giving smear in the gel were rejected. RAPD-PCR Analysis Random Amplified Polymorphic DNA analysis were done using random decamer primers synthesized by Genelink Company, USA. Total of 43 RAPD primers (Table 1) of seven different series (A, B, C, D, I, J and K) were used to amplify the genomic DNA of twelve arthropod species. The RAPD-PCR reaction was performed by using 10X PCR buffer with

(NH4)2SO4, MgCl2, dNTPs (dATP, dCTP, dGTP, dTTp), decamer oligonucleotide primer and Taq polymerase. RAPD-PCR was optimized containing 2.5µl of 10X buffer, 3µl of

3mM MgCl2, 5µl dNTPs (25mM for each), 2.5µl of 0.01 mM Gelatin, 2µl RAPD-primer (15 ng), 0.2µl Taq (1 unit) and 2.5µl of 10ng genomic DNA. The Taq polymerase along with

10X buffer, MgCl2, dNTPs and gelatin were purchased from Fermentas, USA. The genomic DNA amplification was done by using thermal cylcer (Biorad) using the following

130 programme: 5 minutes initial denaturation at 95oC followed by 40 cycles comprising 1 minute denaturation at 95oC, 1 minute primer annealing at 37oC and extension at 72oC for 2 minutes and then final extension at 72oC for 10 minutes. Scoring and Data Analysis Amplified fragments were scored from top to bottom of the lane as presence (1) or absence (0) and bivariate 1-0 data was used for genetic analysis for each species. Only visible and unambiguous amplified fragments were scored. Amplification profiles of all species were compared with each other and to 1kb ladder. Polymorphic bands and their occurrence frequencies along with mean band frequency of each primer were calculated (Table 1). Genetic similarity among twelve sampled species was calculated using Nei’s similarity indices by analysing the data in Popgen software (ver 1.44). Based on these similarities a dendrogram was constructed amongst predators, preys and total number of species. Gut Content Analysis For gut analysis, the predator species C. septumpunctata, O. javanus, H. convergens and N. theisi were collected from sugarcane, fodder, wheat and mustard crops. It was suspected from the field observations that these generalist predators feed on different available aphid species. Also the field data showed continuous incoming pattern of few predator and prey species in all the crops. Early morning and late evening is suspected to be the peak time period of feeding for these predators. At that time they were captured, brought alive to the laboratory and killed by freezing. Protocols for species specific mitochondrial cytochrome oxidase II primers for major aphid species were used as given in (Chen et al. 2000) with few modifications. PCR reactions, using aphid primers (Table 2) were performed as denaturation of DNA at 94oC for 3 minutes followed by 35 amplification cycles with 30s denaturation at 94oC, 30s annealing at 57 oC, and 1 minute extension at 72 oC. DNA was finally extended for 2 minutes at 72 oC after amplification. PCR products were separated on 1.5% agarose gel, stained with ethidium bromide and photographed under UV light.

131 RESULTS Optimization and reproducibility of RAPD For optimization and reproducibility of RAPD reagent concentration was kept constant throughout the experiment and each of the PCR reaction was repeated 3 times for confirmation of its reproducibility. Three different concentrations of DNA (10 ng/µl, 15 ng/µl and 20 ng/µl) were tested and 10ng/µl DNA was found to be optimum. In the same manner 3mM concentrations of MgCl2, and 1 unit Taq was found to be optimum for best amplification of RAPD fragments. Genetic characterization based on RAPD analysis Total 65 random primers were used for characterization of seven predator species Coccinella septumpunctata, Cheilomenes sexmaculata, Hippodemia variegate, Hippodemia convergens, Chrysoperla carnea, Neoscona theisi,Oxyopes javanus and five prey species Macrosiphum miscanthi, Schizaphis graminum, Aphis maidis, Diuraphis noxia and Musca domestica. Out of 65, 45 primers (Table 1) produced polymorphic amplification (Fig 1), the remaining 18 produced monomorphic banding pattern and thus excluded from the study. A total of 295 fragments were amplified, out of which 282 fragments were polymorphic, showing 93% polymorphism. The number of amplification products produced varied between 6 to 10 with an average of 7.08 per primer. The primer GLB-10 produced maximum number of polymorphic bands i.e. 10 whereas primer GLD-11 amplified 9 bands and six primers produced 8 bands. The minimum number of 5 bands were produced by primer GLC-19. The overall mean band frequency ranged from 0.383 to 0.899 with an average value of 0.673 (Table 1). A unique fragment of approximately 1100bp was identified with primer GLB-10 in the DNA sample of Macrosiphum miscanthi (Fig 2). These fragments can be used in DNA finger printing. Genetic similarity and relationship among individuals Genetic similarity was estimated with Pop Gene software by using Neis similarity matrix. Among arthropod predator species highest similarity of 91% was observed between C. septumpuctata and C. sexmaculata. Rest of the similarity lies between 45-65%. Genetic relationship among predator species was estimated by cluster analysis and it was observed that C. septumpnctata and C. sexmaculata were closely grouped followed by C. carnea, H. variegata and H. convergens. While closely clusterd spiders viz. N. theisi and O. javanus

132 were found to be distinct from the rest of the predator species (Fig 3). Among prey species highest similarity of 64% was observed between M. miscanthi and S. graminum. Remaining similarity lies between 48-63%. Genetic relationship among prey species showed some narrow genetic base as M miscanthi and S. graminum were most closely grouped with each other followed by A. maidis and D. noxia while M. domestica was found to be distinct from rest of the preys (Fig 4). Interspecific comparisons were also studied. These results revealed that all the predator species are present in one group while the prey species are in a separate group. C. carnea (predator) was found exceptionally clustered with two prey species D. noxia and M. domestica (Fig 5). Predator-prey relationship based on gut content analysis Protocols for the design of species-specific mitochondrial COII primers for three aphid species were used as given by Chen et al. (2000). PCR reactions were performed as described by Chen et al. (2000). PCR products were separated on a 1.5% agarose gel, stained with ethidium bromide, and photographed under UV light. All the seven predators were checked for the consumption of three aphid species viz. Aphis maidis, Schizaphis graminum and Diuraphis noxia. The fed predators of C. septumpuctata and O. javanus captured from the field early in the morning expected to feed at that time were positive for the consumption of A. maidis while the unfed (control) were negative. A fragment of approximately 200bp was present in the predator’s DNA sample of C. septumpunctata and O. javanus with primer ClaCOIIF and ClaCOIIR1 (Figure 6). The negative results by control specimens demonstrated that the animals also consumed alternate available preys in the crop field and no such specific fragment existed in predator’s gut. Similarly the fed H. convergens was positive for the consumption of S. graminum with primer GbCOIIF2 and GbCOIIR1. A fragment of approximately 111bp was present in the DNA samples selected predator (Figure 7). Following this the fed N. theisi was positive for the consumption of D. noxia with primer RwaCOIIF3 and RwaCOIIR1. A fragment of 100bp was present in DNA sample of predator i.e. N. theisi (Figure 8). PCR based gut content analysis is an established strategy and there are few records of its local use in arthropod predator gut content analysis. Moreover, this technology is superior to monoclonal antibodies being less time consuming, little expensive and with more defined results.

133 DISCUSSION The reproducibility of the RAPD techniques can be influenced by variable factors, such as sequence of a primer, template quality and quantity, the type of thermal cycler and polymerase employed. The use of a standardized RAPD protocol can ensure the reproducible RAPD pattern. More concentration of Taq and MgCl2, give smear and low concentrations produced light bands (Khan et al. 2005). The high degree of polymorphism in the study compared to other reports appears to be due to more diverse material which belonged to different species germplasm. Here the polymorphism seemed to be slightly higher than that obtained by (Gadelhak and Enan, 2005) studying order Coleoptera. They found 64% polymorphism by using 20 random primers. DNA markers has been developed to identify different species specifically. RAPD has a very positive role in this regard by providing few unique fragments. Similarly in the present study a unique fragment was identified in M. miscanthi absent in other closely related species, after cloning and sequencing of these fragments SCAR (sequence characterized amplified region) primers could be developed. These species specific primer could be helpful as compared to monoclonal antibodies techniques i.e. more expensive and involved processes comprising of many steps in analysis of many predator or prey species (Greenstone and Shufran, 2003; Zhang et al. 2007). Genetic similarity and relationship among individuals was very clear if compared with the available taxonomical literature. All the arthropod species have distinct phenotypic morphological similarities as well as variations. Morphological similarities between two groups have a genetic basis and is a result of common evolutionary history. The relatedness of animals is reflected in the genotype and phenotype of related animals. The genes and proteins of related animals therefore are more similar than from distantly related animals (Miller and Harley, 2007). The grouping of predator C. carnea with available preys was a unique entity. But there are some reasons that C. carnea is fluid feeder with sucking and piercing type of mouth parts like family aphididae. One reason is the rapid mutation rate in insects because of heavy use of pesticides/ insecticide in the croplands inducing genetic diversity among invertebrates as revealed by (Rana et al. 2008). Four coccinellid (Insect) predators clustered in one major group have body divided into head, thorax, abdomen with three pair of legs, one pair of antennae, two pair of eyes and chewing-biting type of mouthparts also used for seizing the

134 prey. N. theisi and O. javanus (Arachanid) predators were clustered separately. They have a cephalo-thorax with four pair of legs, two pair of antennae, four pair of eyes and a pair of chelicerae used to sieze and kill the prey and have sucking type of mouth parts (Miller and Harley, 2007). Predator’s gut content analysis to study predation is one of the best feasible approach and the sequence of primer, optimal annealing temperature and fragment size are the best for the prey species studied. The prey DNA remains could be detected for several hours after feeding in the predator’s gut because most of them consumed the given prey immediately after feeding so the PCR assay can specifically detect target DNA in the presence of competing congeneric DNA (Chen et al. 2000). By using species specific primer of A. maidis (known pest of the cropland) its consumption was confirmed by gut analysis of C. septumpunctata and O. javanus. Few other pest species like S. graminum and D. noxia were confirmed in the gut of H. convergens and N. theisi. Similar kind of approach was used for the detection of different aphid species in the gut of spiderlings of O. salticus (Greenstone and Shufran, 2003). PCR based gut content analysis is an established strategy and there are few records of its local use in arthropod predator gut content analysis. Moreover, this technology is superior than monoclonal antibodies being less time consuming, little expensive and with more defined results. With many advantages there is a disadvantage that we are unable to get larval or instar stage specificity as described by (Greenstone, 1995) because of the presence of DNA in all tissues of all life stages. Such specificity might be achieved by reverse transcriptase PCR. CONCLUSION Using RAPD a band of approximately 1100bp was identified in DNA sample of Macrosiphum miscanthi. Such unique fragments could also be used for development of SCAR marker. For the study of predator-prey relationship, few species specific primers of aphids were used and the prey fragments were identified in the gut of fed predators. Such results might also be helpful for the development of an efficient biological control strategy against major crop pests.

135 REFERENCES

Chang, G. C., and Kareiva, P. 1999. The case for indigenous generalists in biological control. In Hawkins, B. A., and Cornell, H. V. (Eds) Theoretical approaches to biological control. Cambridge University Press, Cambridge. pp. 103-105. Chen, Y., Giles, K. L., Payton, M. E., and Greenstone, M. H. 2000. Identifying key cereals aphid predators by molecular gut analysis. Mol. Ecol. 9: 1887-1898. Fritsch, P., and Riesebarg, L. H. 1996. The use of RAPD in conservation of genetics: In Smith, T. B., and Wayne, R. K. (Eds.) Molecular Genetics Approaches in Conservation Genetics, Oxford University Press, New York, USA. Pp. 53-73. Gadelhak, G. G., and Enan, M. R. 2005. Genetic diversity among populations of red palm weevil Rhynchophorus ferruginus Olivier (Coleoptera, Curculinoidae) determined by RAPD-PCR. Int. J. Agric. Biol. 3: 395-399. Greenstone, M. H. 1995. Bollworm or Budworm? Squashblot immunoassay distinguishes eggs of Helicoverpa zea and Heliothis virescens. J. Econ. Entomol. 88: 213-218. Greenstone, M. H., and Shufran, K. A. 2003. Spider predation: Species specific identification of gut contents by polymerase chain reaction. J. Arachnol. 31: 131- 134. Khan, I. A., F. S. Awan., A. Ahmad., Y. B. Fu., and A. Iqbal. 2005. Genetic diversity of Pakistani Wheat germplasm as revealed by RAPD markers. Gen. Res. Crop. Prot. (Short Communications) 52: 239-244. Li, J. M., and Jin, Z. X. 2006. High genetic differentiation revealed by RAPD analysis of narrowly endemic Sinocalycanthus chinensis Chenget S. Y. Chang, an endangered species of China. Biochem. Syst. Ecol. 34: 725-735. Miller, A. S., and Harley, J. B. 2007. ZOOLOGY (8th eds.). McGraw Hill Publications, Singapore. Olfert, O., Johnson, G. D., Brandt, S., and Thomas, A. G. 2002. Use of arthropod diversity and abundance to evaluate cropping systems. J. of Agron. 94: 210-216. Rana, S. A., Rana, N., Ruby, T., Mustafa, Z., Saeed, F. S., and Sabahat, S. 2008. Effect of agrochemicals inducing genetic diversity among earthworm species, as revealed by RAPD, in sugarcane fields. Biologia Pak. 54: 33-42.

136 Sheppard, S. K., and Harwood, J. D. 2005. Advances in molecular ecology: tracing trophic links through predator prey food-webs. Func. Ecol. 19: 751-762. Sreekumar, V. B., and Renuka, C. 2006. Assessment of genetic diversity in Calanus thwaitessii BECC (Arecaceae) using RAPD markers. Biochem. Syst. Ecol. 34: 397- 405. Symondson, W. O. 2002. Molecular identification of prey in predators diets. Mol. Ecol., 11: 627-641. William, J. G., Kubelik, A. R., Livak, K. J., Raflask, J. A., and Tingey, S. V. 1990. DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucl. Acids. Res. 18: 6531-6535. Zaidi, R. H., Jaal, Z., Hawkes, N. J., Hemingway, J., and Symondson, J. 1999. Can multi- prey copy sequences of prey DNA be detected amongst the gut contents of invertebrate predators? Mol. Ecol. 8: 2081-2088. Zhang, G. F., Chaung, Z. C., and Wan, F. H. 2007. Detection of Diuraphis noxia remains in predators guts using a sequence characterized amplified region marker. Entomol. Exp. et. Appl. 121: 81-90. Zhao, N. X., Gao, Y. B., Wang, J. L., and Ren, A. Z. 2008. Population structure and genetic diversity of Stipa grandis a dominant species in the typical steppe north China. Biochem. Syst. Ecol. 36: 1-10. Zhu, Y. C. and Greenstone, M. H. 1999. Polymerase chain reaction techniques for distinguishing three species and two strains of Aphelinus from Schizaphis graminum. Annl. Entomol. Soc. America, 92: 71-79.

137 Chapter 7 SUMMARY Studies pertaining to biodiversity of arthropods in the crop fields of Punjab were conducted from June 2007 to May 2009. Four major crops viz. sugarcane, fodder, wheat and mustard were sampled from Faisalabad representing Central Punjab (Mixed Crop Zone) and Multan representing Lower Punjab (Cotton-Wheat Zone) for the collection of fauna. The fauna was identified and compared for species richness and evenness in four major crop fields. Trophic status of all the identified species was determined from online available literature. Organisms were categorized into predator, preys and pests on the basis of their feeding habits. The weed flora within four crop fields was also identified and arthropods present on these floral species were identified and analyzed on the basis of their associations with different weed species. Moreover, predominant species of predators and prey/pests were selected to study the predator-prey relationship in the crop fields. Lastly, the genetic diversity of dominant arthropod species was determined by using RAPD technique. The gut content analysis of generalist predators was performed by PCR and it was observed that few pest species could be controlled by certain predator species. Biodiversity of arthropods in the cropland of Punjab The Faisalabad was found better than Multan with respect to biodiversity that their agro-ecosystems hold. Among different identified faunal orders Orthoptera, Hemiptera, Coleoptera and Araneae predominated in both zones having significantly greater diversity of former three orders in the cultivations of Faisalabad and the later one in those of Multan. Significantly greater diversity of Araneae in the cropland of Multan could be related to the low relative humidity. Relatively arid climatic conditions generally persist in this zone. By and large, mixed cropping system of Faisalabad with reduced chemical applications was found relatively better with respect to the unintentional conservation of cropland biodiversity. Effect of plant diversity on faunal populations Twenty eight weed species were observed from the four major crops in two zones. Out of which eighteen were broad leaved while ten were grassy weeds. The presence of more grassy weed species especially wide spread of Phalaris minor instead of Vicia sativa in Central Punjab was probably an indication of changed soil conditions from light sandy to

144 more moist loamy soil. The application of fertilizers and canal irrigation in the fields were suspected as among the major factors changing soil conditions. In addition the farming practices and tillage also has a great impact on weed flora. Majority of the phytophagous species used these plants (weeds) as a food and in turn fed upon by some zoophagous species. These species visited the weeds for their prey, shelter and egg laying for completion of life cycle. Thus, weeds have a definite role within the agro-ecosystems in supporting biodiversity. Summarizing all this it could be suggested that weeds acts as life source for many phytophagous and zoophagous taxa. Probable interactions among these faunal populations (Predator-prey relationship) Dominant predator and prey/pest species were selected from the field data to ascertain the probable interactions among these populations. The dynamics of predator-prey relationship was studied by using a simple mathematical formula. Predator-prey ratios (p/p) were calculated from monthly abundance data and smooth horizontal graphical line showed best association of a predator with selected preys. Accordingly, the Aphis maidis showed the best association with all the beetle species namely, Coccinella septumpunctata, Cheilomenes sexmaculata, Hippodemia convergens and Hippodemia variegata. Schizaphis graminum showed good association with green lacewing, Chrysoperla carnea while Musca domestica was found to be preferred by all spider species. These findings were assumed to be helpful in ascertaining the species-specific biological control in the field. Molecular analysis of Predator-prey relationship by PCR Seven generalist predators and five prey species abundant in the field samples were selected for molecular analysis. Predator species comprised of Coccinella septumpunctata, Cheilomenes sexmaculata, Hippodemia variegata, Hippodemia convergens, Chrysoperla carnea, Neoscona theisi and Oxyopes javanus. While prey/pest species comprised of Macrosiphum miscanthi, Schizaphis graminum, Aphis maidis, Diuraphis noxia and Musca domestica. Former three species were the major pest in wheat crop and later two infest fodder crops. Genetic diversity among the selected species was analyzed by RAPD techniques. Total 65 random primers were used for genetic characterization. Out of 65, 45 primers produced polymorphic amplification while the remaining 18 produced monomorphic banding pattern and thus excluded from the study. Predators were in one cluster while preys were in other, although few exceptions were also observed. Interestingly,

145 a unique fragment of approximately 1100bp was identified with primer GLB-10 in the DNA sample of Macrosiphum miscanthi. Such fragments could be used for the development of SCAR marker or as fingerprints for species identification. As discussed earlier in predator- prey associations based on horizontal linear graphical line, that the Aphis maidis was consumed by four members of family coccinellidae and one spider species. Similar findings were observed in gut analysis of Coccinella septumpunctata and Oxyopes javanus by using species specific primer of Aphis maidis. All the fed predators were positive for consumption of A. maidis while the unfed were negative. A fragment of approximately 200bp was present in the predators C. septumpunctata and O. javanus with primer ClaCOIIF and ClaCOIIR1. In addition to this, fed H. convergens was positive for the consumption of S. graminum with primer GbCOIIF2 and GbCOIIR1. A fragment of approximately 111bp was present in the gut sample of selected predator. Similarly a common spider species N. theisi was positive for the consumption of D. noxia with primer RwaCOIIF3 and RwaCOIIR1. A fragment of 100bp was present in DNA sample of predator’s gut. Such results are of indication that above discussed four predator species could be effectively used for control of these major crop pests.

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29

Map of Punjab Province, Pakistan showing two zones viz. Faisalabad representing (Central Punjab) and Multan representing (Lower Punjab).

74

Map of Faisalabad (Mixed crop zone): Area marked with dots showing the sampling sites of this zone.

75

Map of Multan (Cotton-Wheat zone): Area marked with dots showing the sampling sites of this zone.

76

Appendix IV: Monthwise meterological data for the year 2007 of two crops (Sugarcane, Fodder) in Faisalabad

Months Temp. (oC) Rel. Humidity (%) Rainfall Wind Velocity (km./h) Max Min Mean 8 A.M. 5 P.M. Mean (mm) 8 A.M. 5 P.M. Mean Jun-07 36.6 25.4 31 72.2 60 66.1 159.4 2.4 2.6 2.5 Jul-07 37.6 25.5 31.5 72.9 55.1 64 27 2.6 2.9 2.75 Aug-07 35.3 23.2 29.25 71.1 54.7 62.9 38.9 2.4 2.6 2.5 Sep-07 33.7 14.8 24.25 66.6 36 51.3 Traces 1.2 1.6 1.4 Oct-07 28 11.1 19.55 83 55 68.5 Traces 0.6 0.9 0.75 Nov-07 21.4 4.4 12.9 86.1 52 69.05 6 0.5 0.8 0.65 Average 32.1 17.4 24.74 75.32 52.13 63.64 57.83 1.67 1.9 1.76

Appendix V: Monthwise meterological data for the year 2007-08 of two crops (Wheat, Mustard) in Faisalabad

Months Temp. (oC) Rel. Humidity (%) Rainfall Wind Velocity (km./h) Max. Min Mean 8:00 5:00 Mean (mm) 8:00 5:00 Mean AM PM AM PM Dec.07 21.4 4.4 12.9 86.1 52 69.1 6 0.5 0.5 0.5 Jan-08 17.5 2.2 9.85 83.4 50.7 67 58.7 0.8 2.2 1.5 Feb.08 21.4 5.8 13.6 77.1 45.9 61.5 10.3 1.5 3.4 2.45 Mar.08 31.5 15.4 23.45 68.5 40.9 54.7 Traces 1.7 2.2 1.95 Apr.08 34 19.2 26.6 56.6 36.4 41.5 19.3 2.3 3.2 2.75 May-08 38.4 23.4 30.9 50.6 34.2 42.4 53.9 3.1 3.4 3.2 Average 27.3666667 11.7333 19.55 70.3833 43.35 56.0333 29.64 1.65 2.48333 2.05833

77 Appendix VI: Monthwise meterological data for the year 2007 of two crops (Sugarcane, Fodder) in Multan

Months Temp. (oC) Rel. Humidity (%) Rainfall Wind Velocity (km./h) Max. Min Mean 8:00 5:00 Mean (mm) 8:00 5:00 Mean AM PM AM PM

Jun-07 45.5 35.2 40.5 54.3 19.3 36.8 0 0.5 0.5 0.5

Jul-07 47.3 39.1 43.2 72.9 43.3 58.1 3.6 0.8 2.2 1.5 Aug-07 49.2 35.4 42.3 73.7 39 56.3 5.1 1.5 3.4 2.45 Sep-07 36.6 21.1 28.8 78.5 51.4 64.9 0 1.7 2.2 1.95

Oct-07 28.7 21.5 25.1 81.7 55 68.3 0 2.3 3.2 2.75 Nov-07 22.3 15.6 18.9 77.2 48.5 62.8 Traces 3.1 3.4 3.2 Average 38.26 27.98 33.13 73.1 42.8 57.9 1.4 1.65 2.48 2.05

Appendix VII: Monthwise meterological data for the year 2007-08 of two crops (Wheat, Mustard) in Multan

Months Temp. (oC) Rel. Humidity (%) Rainfall Wind Velocity (km./h) Max. Min Mean 8:00 5:00 Mean (mm) 8:00 5:00 Mean AM PM AM PM Dec-07 22.5 9.1 15.8 81.7 55 68.3 Traces 0.7 0.8 0.75 Jan-08 19.7 5.9 12.8 78.5 51.4 64.9 3.6 0.8 2.2 1.5 Feb-08 22.3 8.6 15.4 73.7 39 56.3 Traces 1.9 3.4 2.7 Mar-08 28.7 15.1 21.9 72.9 43.3 58.1 5.1 2.2 3.1 2.7 Apr-08 36.6 21.1 28.8 54.3 19.3 36.8 Traces 2.1 3.6 2.9 May-08 40 22 31 56 80 68 4 3.9 5.1 4.5 Average 28.3 13.63 20.95 69.51 48 58.73 4.23 1.93 3.03 2.51

78 Appendix VIII: Multiple Linear Regression of PCA components for SUs derived from an eigenanalysis of species data (Order Odonata) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity) taken in these units. Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 1

Y( 1 ) = 3.21 + -0.039 * X( 1) + -0.034 * X( 2) + -0.031 * X( 3) + 0.249 * X( 4) Coefficient of Multiple Determination: R2 = 0.663; F Ratio = 9.661 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 26.9 2 29.9 3 42 4 1.2

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 2

Y( 2 ) = -1.78 + -0.003 * X( 1) + 0.032 * X( 2) + 0.009 * X( 3) + -0.105 * X( 4) Coefficient of Multiple Determination: R2 = 0.332; F Ratio = 3.421 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 25.6 2 35.9 3 36.7 4 1.8

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 3

Y( 3 ) = -2.38 + -0.041 * X( 1) + 0.062 * X( 2) + -0.002 * X( 3) +-0.156 * X( 4) Coefficient of Multiple Determination: R2 = 0.466; F Ratio = 0.655 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 8.5 2 40.4 3 46.2 4 5.0

79 Appendix IX: Multiple Linear Regression of PCA components for SUs derived from an eigenanalysis of species data (Order Orthoptera) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity) taken in these units.

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 1

Y( 1 ) = -8.89 + 0.043 * X( 1) + 0.116 * X( 2) + 0.086 * X( 3) +-0.526 * X( 4) Coefficient of Multiple Determination: R2 = 0.502; F Ratio = 11.789 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 5.9 2 53.4 3 36.1 4 4.6

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 2

Y( 2 ) = 17.26 + 0.019 * X( 1) + -0.338 * X( 2) + 0.025 * X( 3) + 0.991 * X( 4) Coefficient of Multiple Determination: R2 = 0.309; F Ratio = 7.355 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 8.9 2 74.2 3 11.8 4 5.2

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 3

Y( 3 ) = 7.84 + 0.147 * X( 1) + -0.216 * X( 2) + 0.023 * X( 3) + 0.422 * X( 4) Coefficient of Multiple Determination: R2 = 0.455; F Ratio = 2.314 (df = 4 , 3 ) 2 Percent Contribution of each Environmental Factor to R Environmental Percentage Factor Contribution ------1 17.4 2 55.1 3 26.9 4 0.6

80 Appendix X: Multiple Linear Regression of PCA components for SUs derived from an eigenanalysis of species data (Order Hemiptera) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity) taken in these units.

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 1

Y( 1 ) = -2.03 + -0.023 * X( 1) + 0.029 * X( 2) + 0.045 * X( 3) +-0.095 * X( 4) Coefficient of Multiple Determination: R2 = 0.968; F Ratio = 10.019 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 3.9 2 5.0 3 90.9 4 0.2

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 2

Y( 2 ) = 8.64 + 0.031 * X( 1) + -0.176 * X( 2) + 0.005 * X( 3) + 0.480 * X( 4) Coefficient of Multiple Determination: R2 = 0.243; F Ratio = 3.919 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 0.1 2 83.0 3 11.9 4 4.9

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 3

Y( 3 ) = 10.61 + -0.057 * X( 1) + -0.184 * X( 2) + 0.021 * X( 3) + 0.631 * X( 4)

Coefficient of Multiple Determination: R2 = 0.729; F Ratio = 0.903 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 35.8 2 29.3 3 21.9 4 13.1

81 Appendix XI: Multiple Linear Regression of PCA components for SUs derived from an eigenanalysis of species data (Order Coleoptera) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity) taken in these units.

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 1 Y( 1 ) = -4.75 + 0.028 * X( 1) + 0.101 * X( 2) + -0.004 * X( 3) -0.850 * X( 4) Coefficient of Multiple Determination: R2 = 0.901; F Ratio = 9.752 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 6.9 2 2.5 3 90.2 4 0.4 Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 2 Y( 2 ) = 2.64 + -0.002 * X( 1) + -0.060 * X( 2) + 0.019 * X( 0.251 * X( 4) Coefficient of Multiple Determination: R2 = 0.740; F Ratio = 2.383 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 53.5 2 10.6 3 35.5 4 0.5

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 3 Y( 3 ) = -0.17 + 0.009 * X( 1) + 0.004 * X( 2) + -0.009 * X( 3 -0.038 * X( 4) Coefficient of Multiple Determination: R = 0.859; F Ratio = 4.571 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R Environmental Percentage Factor Contribution ------1 64.7 2 2.2 3 29 4 4.1

82 Appendix XII: Multiple Linear Regression of PCA components for SUs derived from an eigenanalysis of species data (Order Lepidoptera) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity) taken in these units.

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 1 Y( 1 ) = 20.08 + -1.533 * X( 1) + 1.950 * X( 2) + -0.587 * X( 3)40.118 * X( 4) Coefficient of Multiple Determination: R2 = 0.988; F Ratio = 9.264 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 23.3 2 26.9 3 35.8 4 14

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 2 Y( 2 ) = -50.51 + 3.684 * X( 1) + -4.716 * X( 2) + 1.533 * X( 3)96.941 * X( 4) Coefficient of Multiple Determination: R2 = 0.881; F Ratio = 5.528 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 4.3 2 10.9 3 83.8 4 1.0

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 3 Y( 3 ) = -3.17 + 0.313 * X( 1) + -0.350 * X( 2) + 0.103 * X( 3) 6.647 * X( 4) Coefficient of Multiple Determination: R = 0.859; F Ratio = 4.571 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R Environmental Percentage Factor Contribution ------1 5.2 2 32.6 3 62.1 4 0.1

83 Appendix XIII: Multiple Linear Regression of PCA components for SUs derived from an eigenanalysis of species data (Order Diptera) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity) taken in these units.

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 1

Y( 1 ) = 33.78 + -0.199 * X( 1) + -0.223 * X( 2) + 0.021 * X( 3) + -8.718 * X( 4) Coefficient of Multiple Determination: R2 = 0.435; F Ratio = 0.577 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 8.1 2 11.4 3 78.3 4 2.2

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 2 Y( 2 ) = 21.84 + -0.126 * X( 1) + -0.179 * X( 2) + 0.035 * X( 3) +-4.872 * X( 4) Coefficient of Multiple Determination: R2 = 0.314; F Ratio = 0.343 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 21.5 2 17.7 3 48.8 4 12.0

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 3 Y( 3 ) = 4.67 + 0.034 * X( 1) + -0.042 * X( 2) + 0.007 * X( 3) +-1.716 * X( 4) Coefficient of Multiple Determination: R = 0.648; F Ratio = 1.383 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R Environmental Percentage Factor Contribution ------1 6.8 2 39 3 49.1 4 5.2

84 Appendix XIV: Multiple Linear Regression of PCA components for SUs derived from an eigenanalysis of species data (Order Hymenoptera) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity) taken in these units.

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 1 Y( 1 ) = 33.78 + -0.199 * X( 1) + -0.223 * X( 2) + 0.021 * X( 3) +-8.718 * X( 4) Coefficient of Multiple Determination: R2 = 0.976; F Ratio = 10.091 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 17.8 2 7.8 3 39 4 35.4

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 2 Y( 2 ) = 21.84 + -0.126 * X( 1) + -0.179 * X( 2) + 0.035 * X( 3) +-4.872 * X( 4) Coefficient of Multiple Determination: R2 = 0.551; F Ratio = 0.919 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 21.5 2 17.7 3 48.8 4 12.0

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 3 Y( 3 ) = 4.67 + 0.034 * X( 1) + -0.042 * X( 2) + 0.007 * X( 3) +-1.716 * X( 4) Coefficient of Multiple Determination: R2 = 0.546; F Ratio = 0.903 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 3.7 2 30 3 38 4 8.3

85 Appendix XIV: Multiple Linear Regression of PCA components for SUs derived from an eigenanalysis of species data (Order Araneae) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity) taken in these units.

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 1 Y( 1 ) = 1.38 + 0.013 * X( 1) + -0.053 * X( 2) + 0.055 * X( 3) + 0.086 * X( 4) Coefficient of Multiple Determination: R2 = 0.897; F Ratio = 9.364 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 16.1 2 55 3 28.5 4 0.4

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 2

Y( 2 ) = -2.56 + 0.001 * X( 1) + 0.043 * X( 2) + 0.015 * X( 3) +-0.196 * X( 4) Coefficient of Multiple Determination: R2 = 0.202; F Ratio = 0.190 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 16.1 2 55 3 28.5 4 0.4

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 3 Y( 3 ) = 2.06 + 0.132 * X( 1) + -0.098 * X( 2) + 0.017 * X( 3) + 0.062 * X( 4) Coefficient of Multiple Determination: R2 = 0.692; F Ratio = 1.682 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 16.3 2 36.7 3 33.3 4 13.7

86 Appendix XIV: Multiple Linear Regression of PCA components for SUs derived from an eigenanalysis of species data (Others) onto the environmental factors (Temperature, Relative Humidity, Rainfall, Wind Velocity) taken in these units.

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 1 Y( 1 ) = 6.67 + 0.080 * X( 1) + -0.170 * X( 2) + 0.028 * X( 3) + 0.390 * X( 4) Coefficient of Multiple Determination: R2 = 0.537; F Ratio = 0.522 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 1.7 2 25.5 3 7.2 4 65.6

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 2 Y( 2 ) = 4.60 + 0.011 * X( 1) + -0.096 * X( 2) + 0.012 * X( 3) + 0.283 * X( 4) Coefficient of Multiple Determination: R2 = 0.355; F Ratio = 0.358 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 9.9 2 9.4 3 41.2 4 39.6

Regression Equation: Intercept and Partial Regression Coefficients for PRINCIPAL COMPONENT # 3

Y( 3 ) = 2.50 + 0.023 * X( 1) + -0.055 * X( 2) + -0.006 * X( 3) + 0.170 * X( 4) Coefficient of Multiple Determination: R2 = 0.835; F Ratio = 3.784 (df = 4 , 3 ) Percent Contribution of each Environmental Factor to R2 Environmental Percentage Factor Contribution ------1 0.7 2 15.9 3 39.5 4 43.5

87 Appendix XVII: Monthly abundance data and Chi-square value showing association of selected predator and preys.

Lepidoptera M. domestica C.septumpuctata A. maidis S. graminum M. miscanthi larvae larvae O E O E O E O E O E O E 72 81 82 90 56 69 72 80 38 42 52 61 87 92 92 101 62 56 80 89 42 56 58 68 64 87 88 96 59 52 68 73 46 65 54 75 91 92 96 112 52 53 78 82 40 43 60 77

124 95 103 113 68 55 92 98 52 48 58 59

146 118 98 102 72 61 104 101 49 55 66 73 113 102 93 100 64 60 84 88 52 68 62 65 52 68 83 77 50 56 64 77 36 47 59 56 x2=∑(O-E)2/E Chi/v=38.28 Chi/v=32.34 Chi/v=30.79 Chi/v=29.68 df = 7 for each

C. sexmaculata A. maidis H. variegata A. maidis O E O E O E O E 18 24 38 42 11 17 82 90 24 31 42 56 12 23 92 101 34 37 46 65 15 13 88 96 25 28 40 43 12 23 96 112 33 29 52 48 14 21 103 113 37 36 49 55 18 26 98 102 33 41 52 68 16 23 93 100 25 34 36 47 12 23 83 77

x2=∑(O-E)2/E Chi/v=49.04 df = 7 x2=∑(O-E)2/E chi/v=21.29 df = 7

125 C. carnea S. graminum H. convergens M. miscanthi O E O E O E O E 24 32 38 42 15 18 72 80 26 35 42 56 22 31 80 89 30 24 46 65 18 20 68 73 22 25 40 43 22 31 78 82 28 25 52 48 26 32 92 98 30 24 49 55 30 37 104 101 24 32 52 68 22 33 84 88 20 21 36 47 16 18 64 77

x2=∑(O-E)2/E Chi/v=28.09 x2=∑(O-E)2/E Chi/v=25.89 df = 7 df = 7

O. javanus A. maidis M. domestica O E O E O E 9 9 38 42 52 61 10 11 42 56 58 68 8 10 40 65 54 75 6 9 28 43 36 77 12 10 52 48 58 59 14 13 49 55 67 73 16 12 52 68 70 65 10 9 36 47 59 56

x2=∑(O-E)2/E Chi/v=40.25 Chi/v=39.95 df = 7 for each

126

N. Araneidae nymph M. domestica theisi M. domestica O E O E O E O E 9 15 52 61 9 17 52 61 11 18 58 68 11 21 58 68 9 15 54 75 10 23 54 75 12 19 60 77 11 21 60 77 11 18 58 59 13 23 64 59 11 18 67 73 12 22 67 73 11 18 52 65 10 19 52 65 9 15 49 56 10 19 59 56

x2=∑(O-E)2/E Chi/v=31.74 x2=∑(O-E)2/E Chi/v=36.64 df = 7 df = 7

127