Wild pollinator communities of native woodlands and commercial almond plantations in a semi-arid Australian landscape: Implications for conservation of insects and ecosystem services

Manu E Saunders B Arts; B Env Sc (Ecology) Hons, University of Queensland

July 2014

This thesis is submitted in fulfilment of the degree of Doctor of Philosophy Charles Sturt University Faculty of Science School of Environmental Sciences

Supervisors: Professor Gary W Luck Professor Geoff M Gurr Dr Margaret M Mayfield (UQ) ii iii iv Table of Contents Page

Preface viii Acknowledgements ix Abstract xi List of Figures xiii List of Tables xiv PART I: GENERAL BACKGROUND 1 Chapter 1: Introduction and Scope 3 1.1 Agroecosystem function 3 1.2 Insects as purveyors of ecosystem functions 4 1.3 Aims and Scope 6 1.4 Justification of terms used in this work 8 Chapter 2: Study system and sampling protocol 11 2.1 Study area 11 2.2 Mallee woodlands and shrublands 12 2.3 Almond cultivation in the Mallee 15 2.4 Sampling protocol 16 PART II: ANALYSIS 19 Chapter 3: Almond orchards with living ground cover host more wild 23 pollinators 3.1 Introduction 24 3.2 Methods 27 3.3 Results 37 3.4 Discussion 44 Chapter 4: Wild pollinators in flowering almond orchards and 49 proximity to unmanaged vegetation 4.1 Introduction 50 4.2 Methods 52 4.3 Results 59 4.4 Discussion 65 Chapter 5: The influence of a semi-arid woodland —almond 71 plantation edge on wild pollinator communities 5.1 Introduction 72 v 5.2 Methods 75 5.3 Results 81 5.4 Discussion 88 Chapter 6: Food and habitat resources available to wild pollinators in 95 a late winter flowering crop 6.1 Introduction 96 6.2 Methods 99 6.3 Results 104 6.4 Discussion 114 PART III: SYNTHESIS 121 Chapter 7: Synthesis and Conclusions 123 7.1 Synthesis and general discussion 123 7.2 Conclusions 126 PART IV: REFERENCE MATERIAL 129 Literature Cited 130 Appendix 1: Pan trap catches of pollinator insects vary with habitat 163 (published manuscript) Appendix 2: Taxonomic lists of flower visitor observations and trap 183 samples Appendix 3: Full set of models for analyses of ground cover vegetation — 197 pollinator relationships (Chapter 3) Appendix 4: Location attributes and pollinator occurrence along ecotone 202 gradients (Chapter 5) Appendix 5: Physical attributes and full candidate sets of models for 211 analysis of habitat structure relationships (Chapter 6)

vi Certificate of Authorship

I hereby declare that this submission is my own work and to the best of my knowledge and belief, understand that it contains no material previously published or written by another person, nor material which to a substantial extent has been accepted for the award of any other degree or diploma at Charles Sturt University or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by colleagues with whom I have worked at Charles Sturt University or elsewhere during my candidature is fully acknowledged. I agree that this thesis be accessible for the purpose of study and research in accordance with normal conditions established by the Executive Director, Library Services, Charles Sturt University or nominee, for the care, loan and reproduction of thesis, subject to confidentiality provisions as approved by the University.

Name: Signature: Date:

vii Preface

This thesis includes five individual manuscripts that have been published in academic journals. Consequently, there will be some repetition across chapters, especially within discussions of background material and methodological details. Chapter 2 provides a brief summary of the study area and an overview of the general sampling approach but specific methodological and analytical procedures for each individual study are detailed in each chapter. A single reference list is provided at the end of the thesis to avoid unnecessary duplication. I am personally responsible for conceptualising the project, all data collection and analysis, and the majority of written text. Gary Luck, Margie Mayfield and Geoff Gurr contributed to the planning and sampling design stages and to the papers they have co-authored with me.

Peer-reviewed publications from this work:

 Chapter 3: Almond orchards with living ground cover host more wild insect pollinators. (Saunders ME, Luck GW, Mayfield MM ( 2013 ) Journal of Insect Conservation 17:1011-1025).  Chapter 5: Spatial and temporal variation in pollinator community structure relative to a woodland —almond plantation boundary. (Saunders ME, Luck GW ( 2014 ) Agricultural and Forest Entomology , doi:10.1111/afe.12067).  Chapter 6: Keystone resources available to wild pollinators in a winter- flowering tree crop plantation. (Saunders ME, Luck GW, Gurr GG ( in press ) Agricultural and Forest Entomology ).  Appendix 1: Pan trap catches of pollinator insects vary with habitat. (Saunders ME, Luck GW ( 2013 ) Australian Journal of Entomology 52:106-113). Conference presentations from this work:

 Saunders ME, Luck GW, Mayfield MM (2012) Living ground cover influences native pollinator abundance in commercial almond orchards. 1st Apimondia ApiEcoFlora Symposium , San Marino, Republic of San Marino, 4-6 October 2012, poster presentation. (Chapter 3)

 Saunders ME, Luck GW (2013) Pan trap catches of pollinator insects vary with habitat context. VII Southern Connection Congress , Dunedin, New Zealand, 21-25 January 2013, poster presentation. (Appendix 1)

viii Acknowledgements

Submission of this thesis is a beginning for me, not an end. I am especially grateful to my principal supervisor, Gary Luck, who provided the precise relative proportions of humour, guidance, encouragement and patience that I needed. He gave me freedom to learn to be the ecologist I am, and direction when tangents knocked at my door. I could not have asked for a better supervisor. My co- supervisor, Margie Mayfield, has been an equally inspirational force, before I even knew I was going to undertake a PhD. I cannot thank her enough for giving me the opportunity as an undergraduate student to develop my interest in pollination ecology and for being part of this project. I also sincerely thank Geoff Gurr, for project advice and intellectual contributions; the Institute for Land Water and Society and the School of Environmental Sciences, for financial, administrative and logistic support; Simon McDonald, Deanna Duffy and Gail Fuller, for technical support, patience with all my ignorant questions about GIS and GPS and swiftness in delivering top-quality conference posters; the indispensable lab staff, particularly Lauren Sheather, who helped with equipment, field work preparation, and assistance during the long days I spent staring down a microscope; Ken Walker for voluntary identification of my (very poorly- mounted) native bee specimens; Select Harvests and Manna Farms, for kindly providing access to their almond trees; and the Department of Sustainability and Environment, Victoria, for research access to mallee national parks. Many other people have contributed, either directly or indirectly, to me reaching this point (in no particular order): Alison Matthews, Lisa Smallbone, Paul Humphries, Nicole McCasker, Ian Lunt, Gill Earl, Janet Cohn, Katrina Lumb, Tobias Smith, Peter Spooner, Wayne Robinson, Antonio Castro, Simon Watson, Saul Cunningham, Alan Yen, Michael Batley, Peter Lillywhite, Gimme Walter and Barbara Gemmill-Herren. James, you and Sarge have kept me afloat over the last 3.5 years, and there would have been more tears than smiles without you and the music. Most importantly, I thank my mother for teaching me to believe in what truly matters.

ix x Abstract

Understanding how monocultures of introduced crop species interact with native ecosystems and wildlife in the surrounding landscape is vital for the ecological management of agroecosystems, and can have far-reaching benefits for biodiversity conservation and crop yields. Insects make essential contributions to ecosystem functions in all types of ecosystems and many functions are synergistically linked, like pollination and biological pest control. Thus, the conservation of invertebrate communities in agricultural landscapes is paramount. Although many studies have investigated pollinator communities using field crops (e.g. canola), more information is needed on how plantations of deciduous tree crops influence local insect communities, as these agroecosystems can create permanent spatial and temporal homogeneity across agricultural landscapes. I sampled potential wild pollinators in commercial almond plantations and native mallee vegetation in northwest Victoria, Australia, over two almond flowering seasons in late winter of 2010 and 2011. The aim of the study was to provide information on the abundance, richness and composition of insect pollinator communities at this time, which is the critical life stage for both almond trees (reproduction) and insects (emergence and reproduction). In particular, I focused on comparing patterns of insect community distribution relative to environmental attributes of mallee vegetation and almond plantations, with the goal of identifying how plantations might influence the conservation of wild insect communities, and thus enhance ecosystem service provision in commercial plantations. Overall, I found that potential wild pollinators were using almond plantations in the Victorian mallee during the almond flowering season. However, their movement into plantation interiors was limited by homogeneous vegetation structure and a lack of essential resources, such as temporal continuity of food resources and the availability of nesting sites. Wild pollinators, particularly native bees, were more abundant in small almond orchards with weedy ground cover across orchard floors, compared to both monoculture plantations and native mallee vegetation. In small orchards surrounded by a heterogeneous agricultural mosaic landscape, abundance and richness of pollinator groups were negatively associated with the proximity of native vegetation. However, in broadacre

xi monoculture plantations, most pollinator taxa were limited to edges near native mallee vegetation, or within remnant patches of mallee in the plantation interior. In particular, native bees were only collected in remnant mallee patches inside the plantations, rather than in blocks of almond trees. Before and after almond flowering, when resources inside plantations were limited, pollinator groups were mostly associated with sites that provided alternative foraging and nesting resources, such as leaf litter, ground cover vegetation or the presence of herbivorous insect prey. The work presented in this thesis has two-fold significance. My results provide valuable information on potential wild pollinators using Australian almond plantations, which has the capacity to motivate the adoption of ecological management goals among growers and enhance awareness of the essential contribution of wild insects to ecosystem functions. I also highlight important knowledge gaps in our understanding of the diverse invertebrate fauna of the Victorian mallee regions, an area of high conservation value, which can inform future research and conservation goals.

xii List of Figures

Figure Caption Page 2.1 Map of the study region and sampling localities 11 2.2 Wild insect pollinators of the Victorian mallee 13 2.3 Typical mallee plant communities of the study region 14 3.1 Representative photographs of the three sampled vegetation 30 types: plant-diverse orchards, monoculture orchards, native mallee vegetation 3.2 Species accumulation curves for insect pollinator groups in each 39 sampled vegetation type 3.3 Diversity profiles for insect pollinator groups 41 3.4 Dendrograms showing pollinator community similarity between 43 each site group 4.1 Representative images of landscape composition in Complex and 53 Simple landscapes 4.2 Ordination plot showing overall pollinator community 58 composition between landscape types 4.3 Ordination plots showing pollinator community similarity 60 between sites of difference resource profiles in each landscape type 4.4 Proportional abundance and richness of each pollinator group in 61 each landscape type 4.5 Proportional abundance of pollinator groups collected in almond 64 blocks and interior mallee patches in Simple landscape 5.1 Map showing the almond/mallee edges sampled for ecotone 76 study 5.2 Representative photographs showing phenological changes 77 occurring across plantations during the flowering season 5.3 Distance-decay graph for decline in pollinator community 82 similarity with distance 5.4 Average pollinator group abundance and richness for each month 84 at mallee and almond sites 5.5 Monthly turnover of wasp and fly abundance and richness along 85 the ecotone gradients 6.1 Pollinator community similarity between mallee and almond sites 106 A1.1 Map of the study area 166 A1.2 Proportional abundance of total insects collected in each pan trap 172 colour A1.3 Proportional abundance of total native Hymenoptera collected in 172 each pan trap colour A2.1 Representative photographs of insect pollinators observed 184 visiting flowers in mallee vegetation and almond plantations

xiii List of Tables

Table Caption Page 3.1 Almond orchards and mallee sites used for sampling 28 3.2 Average abundance and richness of insect pollinators in each 38 vegetation type 3.3 Parameter estimates for relationships between pollinators and 42 ground cover attributes 4.1 Abundance and richness of pollinator groups and proportion of 57 land use types around sites in each landscape type 4.2 Relationships between pollinator groups and proportion of 64 unmanaged vegetation types around sites 5.1 SHE analysis: comparison of physical and taxonomic boundaries 87 along ecotone gradient 6.1 Average pollinator abundance and richness and resource attributes 105 at mallee and almond sites 6.2 Relationships between pollinator groups and overall site 108 heterogeneity 6.3 Relationships between pollinator groups and resource attributes 109 6.4 Summed Akaike weights for each predictor variable (resource 113 attributes) A1.1 Total abundance of pollinator groups collected in each colour pan 170 trap A1.2 Results of Marascuilo procedure comparing proportion of 171 pollinators collected in each colour pan trap A2.1 Observations of flower visitors visiting almond and native flowers 183 A2.2 Taxonomic list of samples 2010 185 A2.3 Taxonomic list of samples 2011 192 A3.1 Full candidate model set for relationships between pollinator 197 insects and ground cover vegetation A4.1 Physical attributes of sampled patches in ecotone study 202 A4.2 Occurrence of Diptera and Hymenoptera family groups along 203 ecotone gradients A5.1 Physical attributes of almond plantations in resource study 211 A5.2 Full list of candidate models for pollinator relationships with 212 resource attributes

xiv PART I: GENERAL BACKGROUND

1 2 Chapter 1: Introduction and scope

1.1 Agroecosystem function

Agroecosystems provide a foundation for human civilisation and incorporating knowledge of ecosystem functions and services into their management is essential. Any system that is structured around living organisms will respond to natural cycles and processes in the absence of human inputs. Thus, ensuring the long-term productivity of agroecosystems depends on understanding how they function as a system and how they interact with the natural ecosystems and landscapes in which they are embedded (Pimentel et al. 1989). A useful way to investigate the ecology of agroecosystems is to examine communities of ecosystem service providers, such as pollinating insects. Land use change often impacts processes of community assembly more than individual species per se (Mayfield et al. 2010; Cadotte et al. 2011) and the delivery of most ecosystem services is synergistically linked across multiple species and interactions (Luck et al. 2009; Macfadyen et al. 2012; Lundin et al. 2013). Every process or exchange of energy that occurs between an ecosystem ’s components is a ‘function ’ of that ecosystem (Şekercioğ lu 2010) and the ecological outcomes of these functions generally provide beneficial ‘services’ to humans (Vogt 1948; Ehrlich & Mooney 1983; Daily 1997). A greater diversity of species, community patterns and natural processes allows for greater availability of ecosystem services (Altieri 1999; Loreau et al. 2001; Symstad et al. 2003; Mayfield et al. 2010; Maestre et al. 2012). However, biotic diversity is often lacking in agroecosystems. Most modern agroecosystems aim to ‘standardise’ production by disconnecting from ecological cycles through the segregation of animals and plants, isolation from local ecological knowledge, and cultivation of plant monocultures (Weis 2010; Woodhouse 2010). In some parts of the world, landscape-scale heterogeneity and ecological complexity are being rapidly replaced by extensive vegetation homogeneity in the form of broadacre crop monocultures (Hartemink 2005; Plourde et al. 2013). Yet, natural systems are never wholly autonomous or static, either in configuration or function (Odum 1969; Ulanowicz 1990; Bejan & Lorente 2010), so intensively-managed homogeneous agroecosystems contravene the principles of natural systems and 3 can disrupt local availability of ecosystem services. Thus, there is a need for more studies that identify ecological and structural differences between crop monocultures and local native vegetation, and relate these differences to the distribution of ecological communities (e.g. Ramirez & Simonetti 2011; Dąbrowska -Prot & Wasiłowska 2012; Meng et al. 2012; Pryke & Samways 2012). Results from such studies would provide valuable information for biodiversity conservation and the ecological management of agroecosystems, which can assist managers to balance long-term productivity with a reduction in input costs.

1.2 Insects as purveyors of ecosystem services

Many ecosystem services that are beneficial to humans are a result of processes or interactions to which invertebrates are key contributors. These include pollination, biological control, soil formation and nutrient cycling. Mutualisms, such as plant- pollinator interactions, are particularly critical to ecosystems and there are far- reaching consequences if these interactions are lost (Kiers et al. 2010). Pollinators indirectly affect many other parts of ecosystems and their loss could result in significant changes to both the physical appearance of the ecosystem and its biogeochemical structure through changes in plant community composition (Ashman et al. 2004; Knight et al. 2005; Biesmeijer et al. 2006; Pauw 2007; Pauw & Hawkins 2011) and, subsequently, soil structure and the hydrologic cycle (D’Odorico et al. 2010). All ecosystem services are interdependent (Daily 1997; Kremen et al. 2007; Bennett et al. 2009) and recent evidence has shown that other services important to agroecosystems, such as biological pest control, can be synergistically linked to the provision of pollination services (Bos et al. 2007; Lundin et al. 2013). Thus, pollinator communities are an ideal focal group to study how agroecosystems influence local ecological communities. Plantations of perennial tree crops, such as orchard fruits, may have particular influence over native pollinator communities in agricultural landscapes. Many deciduous fruit trees are pollinator-dependent (Kevan 1999), but if they are managed as intensive monoculture plantations they create large areas of spatial and temporal homogeneity (in vegetation structure and resource availability) that are inhospitable to pollinators before and after the brief flowering period 4 (Sheffield et al. 2008; Marini et al. 2012). Compared to field crops, which are most commonly grown in broadacre monocultures, tree crop plantations can have a very different ecological influence over the surrounding ecosystems. Field crops, such as wheat, canola or maize, are grown in short rotation cycles and alternated with other field crop varieties, which may contribute temporal heterogeneity to the landscape that could counteract the spatial homogeneity (Vasseur et al. 2013). In contrast, tree crop plantations are embedded in the landscape, often for decades, creating an ecologically stable, semi-permanent ecosystem (Tedders 1983; Altieri 1999). This could potentially influence the structure of local invertebrate communities in the long-term (Harding et al. 1998; Tscharntke et al. 2002). The conservation value of tree crop agriculture has long been recognised (Smith 1916; Altieri et al. 19 83; Ryszkowski & Kędziora 1987) but the environmental influence of tree plantations in agricultural landscapes has historically received less attention in the scientific literature than annual crops (Hartemink 2005). In particular, most of the published studies investigating the influence of crop monocultures on pollinator taxa or communities have focused on field crops, such as canola, legumes or vegetables (e.g. Westphal et al. 2003; Gemmill-Herren & Ochieng 2008; Arthur et al. 2010; Rader et al. 2012). These studies provide valuable insight into how agricultural land uses affect pollinators, but tree monocultures are very different in structure and phenology to monocultures of herbaceous or shrubby species and are likely to influence pollinator communities, and the surrounding environment, in different ways. Some important pollinator taxa, particularly bees and hoverflies, often prefer foraging in relatively open areas dominated by early successional vegetation (Winfree et al. 2011; Robinson & Lundholm 2012) compared to dense forest systems (Gittings et al. 2006; Hoehn et al. 2010; Proctor et al. 2012). Intensively-managed tree crop plantations can be structurally similar to dense forests, and this could affect the availability of pollinator communities for tree crop varieties that rely on pollinators to set fruit. Although some studies have provided valuable information on the effects of tree plantations on local insect communities, these have largely been relevant to plantation forests managed for timber production (e.g. Humphrey et al. 1999; Gittings et al. 2006; Campbell et al. 2011; Pryke & Samways 2012). Pollinator-dependent orchard fruit plantations (e.g. apple, almond, stone fruit) are managed very differently to timber forests, 5 particularly with regard to orchard floor vegetation and disturbance cycles. Given the recent increase in global demand for orchard fruit production (McKenna & Murray 2002; Jackson et al. 2011), understanding how these systems influence local flora and fauna is paramount, particularly in Australia where the majority of orchard crop varieties are exotic species (Cunningham et al. 2002). Previous work on pollinators in Australian tree plantations has been conducted in plantations of native macadamia or Eucalyptus species (e.g. Heard & Exley 1994; Barbour et al. 2008) and evergreen tropical tree crops like cashew or longan (e.g. Heard et al. 1990; Blanche et al. 2006). This thesis aims to address some important knowledge gaps by presenting the first study of wild pollinator insects using monoculture plantations of introduced almond trees, a pollinator-dependent deciduous tree crop, in a semi-arid region of southern Australia.

1.3 Aims and scope

The research presented here compares abundance, richness and distribution of potential wild pollinator insects in almond plantations of north-west Victoria with pollinator communities in native mallee vegetation. Furthermore, it intends to provide insight into how intensive monoculture plantations of orchard fruit trees interact with the landscape, ecosystems and biotic communities around them. To do this, I asked the following questions: 1. Are wild pollinator communities influenced by the presence of living ground cover under almond trees? (Chapter 3) 2. Is there a relationship between wild pollinator abundance and diversity in almond orchards and the proportion of remnant native vegetation in the surrounding matrix? (Chapter 4) 3. How does wild pollinator abundance and diversity change across habitat boundaries between almond monocultures and adjacent native mallee vegetation? (Chapter 5) 4. Do winter-flowering monoculture plantations provide food and nesting resources for overwintering and emerging wild pollinator taxa? (Chapter 6) My underlying hypothesis is that wild pollinator communities will be more species rich and abundant, and therefore more available to pollinate almond 6 flowers, at sites with a greater diversity of food and habitat resources. My sampling approach focused on the late winter flowering season, as this is a critical life stage for almond trees (flowering and nut formation) and pollinator insects (emergence and reproduction). Hence, supporting the conservation of wild pollinator communities in the long-term will depend on matching habitat and resource requirements of pollinators with the structural and phenological attributes of the plantation at this time. The structure of this thesis is as follows:  Chapter 2 presents a brief summary of the ecological significance of the study area and discusses the general sampling protocol;  Chapter 3 investigates relationships between pollinator groups and weedy ground cover vegetation on almond orchard floors. This provides important information for almond growers in Australia, many of whom actively remove vegetation on plantation floors to increase management efficiency, despite the important benefits ground cover provides, such as erosion control, improved soil structure and habitat for beneficial insects (Eilers & Klein 2009; Ramos et al. 2010);  Chapter 4 investigates whether the composition of pollinator communities in orchards during flowering is associated with the presence of natural or semi-natural vegetation near sites. This knowledge has implications for almond management and local biodiversity conservation, because a matrix composed of a diverse array of food and habitat resources is essential to support pollinators in agricultural landscapes dominated by perennial crops that flower for a brief period once a year (Williams & Kremen 2007; Sheffield et al. 2008);  Chapter 5 examines how the physical habitat boundary between mallee woodlands and almond plantations influences the distribution of pollinator communities before, during and after the brief almond flowering event. This contributes to ecological knowledge of how insects might respond to abrupt changes in spatial and temporal distribution of resources in agroecosystems;  Chapter 6 examines the potential for late-winter flowering monoculture plantations to support overwintering or emerging pollinator insects. This has rarely been considered in studies of agroecosystem impacts on pollinator species, and provides valuable insights into how pollinator 7 communities use plantations of almond trees during their emergence and reproductive stages, which coincides with the almond reproductive stage;  Chapter 7 provides a brief synthesis and discussion of the overall results. In particular, I link results from individual studies to highlight the overall significance and contribution of this thesis, and provide recommendations for future research and management goals.  Finally, the Appendices provide additional information relevant to the overall thesis. In particular, Appendix 1 presents the manuscript of an original publication based on data collected during my PhD research, which contributes to the methodological literature by discussing important considerations for designing ecologically meaningful pan trap studies; and Appendix 2 lists the total abundance of bee species and wasp and fly family groups collected across both sampling seasons.

1.4 Justification of terms used in this work

 ‘Pollinator’ refers to individuals in the taxonomic orders Diptera (flies) and Hymenoptera (bees, wasps). Other insect groups are also common pollinators, like Lepidoptera (moths and butterflies), Thysanoptera (thrips), and Coleoptera (beetles), however my sampling method (see Chapter 2) was designed to target Hymenoptera and Diptera individuals, as these are the most commonly-recorded visitors to open blossoms of almond trees and related species (Rosaceae: Prunus spp.) in other parts of the world (e.g. Abrol 1988; Jordano 1993; Bosch & Blas 1994). Although I did not quantify pollination efficiency in this study, I use the term pollinator to maintain consistency with the crop pollination literature because one of the main aims of my research was to document wild insect species using almond orchards that had the potential to provide pollination services to growers. Flies and wasps are important, but understudied, pollinators in a variety of natural and agricultural systems (Kevan & Baker 1983; Larson et al. 2001; Ssymank et al. 2008), particularly in native Australian ecosystems (Armstrong 1979; Pickering & Stock 2003). Many pollinating Diptera and non-bee Hymenoptera are also economically- important predators and parasitoids of herbivorous pests. Because

8 ecosystem service provision often involves multiple species and their interactions (Luck et al. 2009; Lundin et al. 2013), studies of ecosystem services benefit from concentrating on groups of taxa that provide the same services (Bennett et al. 2009; Macfadyen et al. 2012), rather than on a single, well-known taxon (e.g. bees).  ‘Broadacre monoculture’ refers to large -scale, intensive cultivations of a single crop species covering more than 100 hectares of land. This type of agroecosystem is more common in developed countries with substantial areas of rangeland, like Australia, Canada, and the United States of America and is mostly used to cultivate fast-growing, high-yielding crop varieties that require high chemical usage and seed purchase for each planting (Mason 2003; Plourde et al. 2013). More recently, this practice has become common in commercially-grown perennial tree crop or plantation crop systems, such as oil palm, orchard crops and agroforestry schemes (Hartemink 2005).  ‘Orchard’ and ‘plantation’ are used interchangeably in this work to refer to a commercial almond cultivation system. The term ‘orchard’ has mainly been used when comparing small plant-diverse orchard systems with broadacre monoculture plantations (Chapters 3-4), as the most descriptive term relevant to both types of cultivation system was chosen to maintain consistency throughout individual papers. However, the goal of this thesis was to investigate and discuss the ecological role of monoculture plantations, using intensive almond plantations as a study system; hence, the term ‘plantation’ is used elsewher e.

9 10 Chapter 2: Study system and sampling protocol

2.1 Study area

The study area is in north-west Victoria, Australia, within the Murray Mallee bioregion (Department of Sustainability and Environment 2013). Sampling sites for all studies were within native mallee vegetation and commercial almond plantations located in the districts of Wemen, Liparoo, Colignan and Nangiloc (Figure 2.1). The region is semi-arid with a Mediterranean climate. Annual rainfall varies widely with an average of around 300 mm per year. The region was wetter than average during the sampling years, with approximately 528 mm falling in 2010 and approximately 566 mm falling in 2011 (Bureau of Meteorology 2013). Average temperatures for the months of sampling (July — September) ranged from minima of about 5 o C to maxima of about 20 o C.

Figure 2.1 Map of the study region showing localities of sampled almond plantations.

11 2.2 Mallee woodlands and shrublands

The term ‘mallee’ was originally used to describe the open scrub growing on sandy plains of north-west Victoria and eastern South Australia, which was dominated by Eucalyptus dumosa , or white mallee (Westgarth 1848). Today, ‘mallee’ refers to semi -arid open heath and woodlands native to Mediterranean climate zones in southern Australia, specifically the southern coast of Western Australia, parts of South Australia, north-west Victoria and western New South Wales. Mallee ecosystems occur in regions receiving between 250 and 500 mm of average annual rainfall and are characterised by sandy dune systems and predominantly alkaline soils, often with a shallow surface layer overlying limestone bedrock (Noble & Bradstock 1989). Open woodlands are dominated by Eucalyptus species growing in the coppiced ‘mallee’ habit of shorter trees with multiple stems of similar age developing from a large subterranean lignotuber, as well as Acacia , Callitris and Casuarina species (Wood 1929). The understorey is open and highly variable, with patchy shrub and ground cover vegetation typically composed of saltbush and other halophytic plants, Triodia hummock grass, cryptogams, grasses and flowering forbs, and including a high number of rare species (Whittaker et al. 1979; Blakers & Macmillan 1988; Cheal et al. 2013). In addition, the mallee ground layer is characterised by mosaics of bare ground and dense mats of leaf litter and decaying wood (Holland 1969; Whittaker et al. 1979). Knowledge of pollinator insect species (Figure 2.2) and plant-pollinator relationships in the study region is limited (A.L. Yen, pers. comm., 6 July 2011). A search of the peer-reviewed literature (ISI Web of Knowledge, 1 May 2014) produced 11 published ecological studies on invertebrate fauna in the Victorian mallee – two of these were from the current thesis (Saunders & Luck 2013; Saunders et al. 2013; Chapter 2 and Appendix 1), five studied ant communities (e.g. Andersen 1983; Andersen & Yen 1992), and three studied introduced European honeybees (e.g. Oldroyd et al. 1994; Horskins & Turner 1999). Yen et al. (2006) provide an observational account of native bees, and suggest that native bee abundance in the Victorian mallee was high and estimating that the area could support about 10 % of Australian bee spe cies, which concurs with Michener’s (1979) conclusion that bees are most abundant in warm, temperate, xeric areas, such as the semi-arid mallee regions. A number of empirical and observational 12 studies have included accounts of various species of native Diptera and non-ant Hymenoptera visiting mallee flowers (e.g. Ford & Forde 1976; Alcock 1981; Hawkeswood 1982; Horskins & Turner 1999; Yen et al. 2006). Stephens et al. (2006) also found that a high proportion of wasp species collected in a South Australian mallee ecosystem were only collected on a single plant species. Like most semi-arid ecosystems, the mallee is highly influenced by fire and rainfall events, although little information is available on how these events affect pollinator communities in the mallee. Mallee ecosystems support a high proportion of leaf litter and dead wood, which are important habitats for many invertebrate species (Austin et al. 2005; New et al. 2010). Hence, intense wildfires could severely diminish local insect communities (New et al. 2010), or the characteristic mosaic distribution of these attributes could increase the availability of unburned refuge sites during patchy fire events (Majer et al. 1997; Andrew et al. 2000). Andersen & Yen (1985) found that wildfire had immediate effects on ant communities in the Victorian mallee, including a shift toward greater compositional evenness; however, Friend & Williams (1996) studied the long- term effects of fire in a Western Australian mallee ecosystem and found that variation in native bee, wasp and fly communities appeared to be more influenced by general seasonal effects than fire-related trends.

Figure 2.2 Wild insect pollinators of the Victorian mallee: (left) A hoverfly (Diptera: Syrphidae) resting on weedy Brassicaceae spp. in a Wemen mallee remnant; (middle) A bee fly (Diptera: Bombyliidae) resting on leaf litter, Murray- Kulkyne National Park; (right) Tiphiid wasps (Hymenoptera: Tiphiidae) in copula on a Leptospermum coriaceum flower, Hattah-Kulkyne National Park (see also Appendix 2).

Australian mallee regions provide ideal habitat for many pollinator insect species (Figure 2.3). Most pollinator taxa, particularly solitary and eusocial bees, rely on

13 multiple proximate habitats that provide different, but complementary food and habitat resources needed throughout their life cycle – this has been called ‘landscape complementation ’ by Dunning et al. (1992), the problem of ‘ partial habitats ’ by Westrich (1996) and ‘functional connectivity’ by Williams & Kremen (2007). Mild winters, low mallee tree canopies (Andersen & Yen 1992), diverse floral resources, particularly in the herb and shrub layers (Yen et al. 2006), and open vegetation structure create an optimal foraging environment for pollinator insects (Kremen et al. 2007; Winfree et al. 2011). In addition, the patchy distribution of bare, sandy soil and leaf litter banks are preferred substrates for many species of overwintering and ground-nesting bees, flies and wasps (Knerer & Schwarz 1976; Leather et al. 1993; Potts et al. 2005; Houston & Maynard 2012).

Figure 2.3 Typical mallee plant communities of the study region: (top) mallee woodland interior and (bottom) mallee heath and shrubland, Annuello State Flora and Fauna Reserve.

14 2.3 Almond Cultivation in the Mallee

Large-scale clearing and cultivation began in the Victorian mallee in the early 1900s after failed attempts to establish a pastoral industry, and the region has subsequently developed into one of Australia’s main irrigated agricultural regions, the Sunraysia district, relying on intensive water extraction from the Murray River (Duncan & Dorrough 2009). Agriculture in the mallee region is environmentally risky (Sadras et al. 2003) because the highly erratic rainfall, poor soil fertility, and fast evaporation and infiltration rates mean that surface water is rare (Blakers & Macmillan 1988). Since European settlement, native mallee flora and fauna communities have been severely affected by clearing and fragmentation (Cunningham 2000; Driscoll & Weir 2005), rabbit and mouse plagues (Onans & Parsons 1980; Singleton et al. 2001), and increased soil salinity and groundwater salt leakage (Cole et al. 1989). Almond (Rosaceae: Prunus dulcis Mill.) production has been particularly successful in the Victorian mallee, with the region supporting 64 % of the country’s almond plantings (Almond Board of Australia 2013). Almond originated in the foothills and desert areas of central and southwest Asia Minor, and is adapted to mild, wet winters and hot, dry summers (Kester & Ross 1996; Ladizinsky 1999), typical of Australia’s mallee regions. The first small -scale commercial almond plantings were in South Australia in the late 1800s (Wilkinson 2012) and Australia is now the second-largest almond producer in the world, with almonds contributing more to the total annual nut production than the native macadamia (Almond Board of Australia 2013). Almond trees require 100% cross-pollination to set fruit (Hill et al. 1985; Weinbaum 1985), which means that managed European honeybee ( Apis mellifera ) hives have become an essential, and expensive, part of production for commercial almond growers (vanEngelsdorp et al. 2008). This reliance on commercial honeybees raises serious concerns for Australian almond production as impacts such as Varroa mite, climate change, and agricultural intensification increasingly affect the global honeybee industry (vanEngelsdorp et al. 2008; Spivak et al. 2011; Henry et al. 2012). Yet, only a handful of studies have investigated almond pollination in Australia (Hill et al. 1985; Rattigan & Hill 1987; Vezvaei 1994; Vezvaei & Jackson 1995; Jackson

15 1996), and these have looked at the reproductive biology, gene flow or phenology of the flowers and make little reference to pollinating organisms. A particular challenge in managing almond pollination services is that individual flowers are receptive to pollination for an extremely brief period (approximately 4-5 days) in late winter (Griggs & Iwakiri 1975; Hill et al. 1985), when most insect activity is limited. Because of this, it is likely that many growers have assumed European honeybees are the only pollinator option (Klein et al. 2012). However, Mediterranean plant communities in other parts of the world typically support a high number of insect-pollinated species, and pollinator taxa and flowering plants are both common (although less abundant) during cooler months (Petanidou & Vokou 1990; Dafni 1996; Bosch et al. 1997). In fact, despite many individual plant species having specific flowering periods, the complementarity of each one’s phenology can provide the temporal resource connectivity needed to sustain some pollinator taxa, particularly Diptera, throughout the year (Petanidou & Lamborn 2005).

2.4 Sampling Protocol

Potential wild pollinators were sampled in almond plantations and native mallee vegetation during the almond flowering seasons between July and September, 2010 and 2011. In 2010, sites were located in broadacre monoculture plantations at Wemen and Liparoo (n = 30), small, plant-diverse orchards at Colignan and Nangiloc (n = 27) and native mallee vegetation (n = 45) (Chapters 3 and 4). In 2011, sampling focused on broadacre monoculture plantations (n = 50) and adjacent native mallee vegetation (n = 18) at Wemen and Liparoo (Chapters 5 and 6). Site selection on the 2010 sampling trip was limited by access to farms in the region that maintained living ground cover across orchard floors. New South Wales government extension advice recommends that growers maintain bare soil orchard floors to minimise frost damage and competition for soil moisture (NSW Agriculture 2002; Wilkinson 2012). Hence, most commercial almond growers in the study region use intensive mowing and herbicide application to control herbaceous ground cover throughout plantations. Monoculture plantations and plant-diverse orchards were selected to maximise the similarity between sites of each management type. All plant-diverse orchards at Colignan and Nangiloc were 16 owned and managed by the same family-owned company (Manna Farms). Two of these orchards were biodynamically-managed (a type of intensive organic management) and one was managed as a low-intensity conventional farm with no chemical use (see Chapter 3 for details). The two monoculture plantations were owned and managed by the same corporation (Select Harvests) and management practices were identical at both plantations. No insecticides were used in these plantations, but limited application of herbicide or fungicide was used at specific times of the year (see Chapter 3). Chemical applications did not occur during sampling. In both years, site selection was also limited by logistics and weather conditions, due to widespread flooding and above-average rainfall across much of the study region during the sampling years. Low-lying areas of plantations were flooded and access to some parts of the national parks used for native vegetation sites was also limited, particularly during the 2011 sampling trips. Native vegetation sites were located in the Annuello State Flora and Fauna Reserve, the Murray-Kulkyne National Park (Department of Sustainability and Environment, Victoria: Research Permit Number 10005774) and various ungazetted patches of remnant mallee near almond blocks. Insects were collected at each site using sets of pan traps (2010: blue, yellow and white; 2011: yellow and white). Traps were made from plastic picnic bowls (Woolworths Home Brand, Bella Vista NSW) painted with UV-bright fluorescent paint (White Knight Paints, Lansvale NSW) or left white (dimensions: 18 cm diameter, 3 cm depth). Pan traps can underestimate the full composition of insect assemblages; however this method was used because the priority was to maximise sampling time per site and minimise collector bias (Westphal et al. 2008). To increase the likelihood that traps provide ecologically meaningful results, trap colour and placement should be designed to suit the context of the study system and research questions (Tuell & Isaacs 2009; Vrdoljak & Samways 2012; Saunders & Luck 2013; see Appendix 1). Hence, yellow and white were chosen because these were the most common flower colours at my sites (in mallee and almond respectively). In 2010, blue traps were also included as this colour is commonly thought to attract bees (usually European honeybees), although blue traps have not been widely tested for attracting native bees in Australia (see Appendix 1 for discussion of this). In 2011, only yellow and white were used, based on the results from 2010 sampling that indicated the target insects (non- Apis 17 bees, wasps and flies) were captured more frequently in yellow and white traps (Appendix 1). At each site, pan traps were placed together on the ground about 1 m from a flowering tree to ensure maximum visibility to flying insects. Ground placement was also chosen because the late winter sampling period meant that some insects would be emerging from pupae or overwintering sites, and many of my target species overwinter or nest in the ground or in leaf litter and ground cover vegetation (see Chapter 6). Each site was sampled once per sampling trip; traps were set out for 8-10 hours during the warmest part of the day to capture maximum insect activity and were collected in the sequence they were set out. In 2010, flower visitors on open flowers were also recorded as baseline observations, but these data have not been used in analyses due to the low individual counts. Visual surveys were conducted at almond and mallee trap sites by random walks within 10 metres of trap locations for a 10 minute period. All insects seen visiting open flowers were counted and identified to the highest possible taxonomic level (see Appendix 2 for data and photographs). Upon collection from pan traps, insects were stored in 70 % ethanol and counted and sorted in the Charles Sturt University Ecology laboratory using an Olympus SZ51 microscope. The full collection, including voucher specimens, was stored at Charles Sturt University laboratory. Because of the limited ecological and taxonomic information available on invertebrate fauna in the Victorian mallee, my aim was to inform future work by providing a broad inventory of Diptera and Hymenoptera in my study system, as these groups are the most commonly recognised insect providers of ecosystem services, i.e. pollinators and biological control agents. The focus of my work is on Diptera and Hymenoptera that could provide critical pollination services to almond growers; however, because of lack of knowledge on almond pollinators in Australia, I have sampled the two orders generally and have included other families in my data that are not commonly categorised as pollinators (e.g. parasitic or predatory flies and wasps), as species from these families can pollinate flowers in some contexts (Armstrong 1979; Larson et al. 2001; Wäckers et al. 2005; Ssymank et al. 2008) and recent evidence suggests that pollination and biological control services provided by insects are synergistically linked (e.g. Bos et al. 2007; Lundin et al. 2013).

18 European honeybees ( Apis mellifera ) were removed from samples prior to analysis because my focus was on non-honeybee potential pollinators. Evidence from previous studies suggests that honeybees and non-honeybee pollinator insects often show spatial complementarity in nesting and foraging locations, especially when resources are abundant, as was the case in my system (Markwell et al. 1993; Roubik & Wolda 2001; Paini et al. 2005; Brittain et al. 2013; Rollin et al. 2013). Thus, the presence of honeybees in my system was unlikely to have had a major impact on the abundance and richness of wild bees, wasps and flies found at each site. After sorting insect samples into taxonomic orders, non- Apis bees were identified to species or subgenus by a taxonomic expert (K. Walker, Museum Victoria) and I identified wasp and fly individuals to family using taxon- specific identification keys (Hamilton et al. 2006; Stevens et al. 2006). Family- level analyses may provide coarser estimates of richness than species-level approaches (Krell 2004; Lovell et al. 2007). However, given the largely undescribed Australian insect fauna and the lack of taxonomic expertise or knowledge on these groups (Austin et al. 2004; Raven & Yeates 2007; Batley & Hogendoorn 2009; Cardoso et al. 2011), employing taxonomic surrogacy (i.e. analysing orders, families, genera or morphospecies instead of species) is useful in studies that aim to provide broad inventories or comparisons between communities of insect fauna (Oliver & Beattie 1996; Pik et al. 1999; Obrist & Duelli 2010).

19 20 PART II: ANALYSIS

21 22 Chapter 3: Almond orchards with living ground cover host more wild insect pollinators

Abstract

Wild pollinators are becoming more valuable to global agriculture as the commercial honeybee industry is increasingly affected by disease and other stressors. Perennial tree crops are particularly reliant on insect pollination, and are often pollen limited. Research on how different tree crop production systems influence the richness and abundance of wild pollinators is, however, limited. I investigated, for the first time, the richness and abundance of potential wild pollinators in commercial temperate almond orchards in Australia, and compared them to potential pollinator communities in proximate native vegetation. I quantified ground cover variables at each site, and assessed the value of ground cover on the richness and abundance of potential wild pollinators in commercial almond systems focussing on three common taxa: bees, wasps and flies. More insects were caught in orchards with living ground cover than in native vegetation or orchards without ground cover, although overall species richness was highest in native vegetation. Percent ground cover was positively associated with wasp richness and abundance, and native bee richness, but flies showed no association with ground cover. The strongest positive relationship was between native bee abundance and the richness of ground cover plants. My results suggest that maintaining living ground cover within commercial almond orchards could provide habitat and resources for potential wild pollinators, particularly native bees. These insects have the potential to provide a valuable ecosystem service to pollinator-dependent crops such as almond.

*This chapter has been published as Saunders ME, Luck GW, Mayfield MM (2013 ) Journal of Insect Conservation 17:1011-1025.

23 3.1 Introduction

The importance of plant diversity to insect pollinators has been discussed broadly in the literature (e.g. Carreck & Williams 2002; Potts et al. 2003; Kremen et al. 2007; Hagan & Kraemer 2010; Ebeling et al. 2011; Pywell et al. 2011). In natural environments, the spatial and temporal diversity of floral resources is often positively correlated with the richness and abundance of wild insect pollinators (Potts et al. 2003; Ebeling et al. 2008; Fründ et al. 2010; Grundel et al. 2010). Evidence from agricultural systems also shows that insect pollinator abundance increases in response to greater plant diversity in the landscape, such as proximate remnants of native vegetation (see Ricketts et al. 2008; also Blanche et al. 2006; Chacoff & Aizen 2006; Gámez-Virués et al. 2009; Carvalheiro et al. 2010; Kennedy et al. 2013), managed intercropping (Altieri 2004; Thevathasan & Gordon 2004; Letourneau et al. 2011), or management practices dedicated to maintaining weeds/wildflowers in field margins (Carreck & Williams 2002; Haaland et al. 2011). Living ground cover is not common in conventional agricultural systems, but is often an integral part of organic, multicropped or traditional farming systems (Altieri 2004; Gomiero et al. 2011). The environmental benefits of ground cover in agricultural ecosystems are numerous and include improving the physical, chemical and biotic quality of the soil (Sharley et al. 2008; Grasswitz & James 2009; Ramos et al. 2010), minimising erosion and excessive runoff (Gómez et al. 2011; Kremer & Kussman 2011; Ruiz-Colmenero et al. 2011), and increasing the number of beneficial insects and decreasing herbivorous pests (Hooks & Johnson 2004; Gámez-Virués et al. 2009; Walton & Isaacs 2011). Yet there are few studies linking wild insect pollinators in agricultural crop systems with ground cover floral resource diversity, particularly in perennial tree crop systems. Historically, studies of ground cover in orchard crops have focused on natural enemies of crop pests rather than potential crop pollinators (e.g. Colloff et al. 2003; Horton et al. 2003; Brown 2012). Perennial tree crop systems are becoming more dominant in the global agricultural landscape, with plantations of oil palm, rubber, coffee, cocoa and tree nuts (e.g. almond, macadamia, cashew etc.) expanding rapidly, particularly throughout tropical regions (Hartemink 2005) and in Australia specifically 24 (Australian Nut Industry Council n.d.). The area under commercial tree nut cultivation in Australia was nearly 55,000 hectares in 2011 and future expansion of this industry is likely, given current industry development efforts (Australian Nut Industry Council n.d.). In particular, almond ( Prunus dulcis Mill.) is becoming increasingly important to Australia’s agricultural economy. The crop contributes more to Australian nut production than the native macadamia and had a farm-gate value of more than AU$300 million in 2012 (Almond Board of Australia 2013). Almond is one of the few crops that require 100% cross-pollination for fruit production (Hill et al. 1985; Weinbaum 1985; Cunningham et al. 2002), which means that managed European honeybee ( Apis mellifera L.) hives have become an essential part of production for commercial almond growers (vanEngelsdorp et al. 2008). In addition, many of the other tree crops grown in Australia on a commercial scale are reliant on pollinators for at least 50 % of their production value (Cunningham et al. 2002). Yet there is a dearth of scientific research into ecological and management issues associated with Australian tree crop production, especially pollination ecology and the potential for wild pollinators to benefit commercial systems. The reliance on honeybees in Australia raises serious concerns for the future of pollinator-dependent crops, as impacts such as the Varroa destructor mite, climate change, agricultural intensification, and colony collapse disorder (CCD) increasingly affect the global honeybee industry (vanEngelsdorp et al. 2008; Spivak et al. 2011; Henry et al. 2012) and are likely to worsen in Australia in coming decades. Also, a recent meta-analysis has shown that maximal fruit yields are not generally possible without the combined services provided by managed honey bees and diverse wild pollinators (Garibaldi et al. 2013). Despite increased global interest in wild pollinators (Klein et al. 2012; Garibaldi et al. 2013; Lebuhn et al. 2013) and the burgeoning research on pollination as an essential ecosystem service (e.g. Klein et al. 2007; Kremen et al. 2007; Potts et al. 2010; Kennedy et al. 2013), current ecological knowledge of almond pollination is dominated by research conducted in the United States, particularly involving European honeybees (Degrandi-Hoffman 2001; Thomson & Goodell 2001; Klein et al. 2012; Yong et al. 2012; Brittain et al. 2013). Non- honeybee pollinators have proven successful in commercial almond orchards in 25 the United States, Japan and Spain (Bosch & Blas 1994), yet only a handful of studies have investigated almond pollination in Australia (Hill et al. 1985; Rattigan & Hill 1987; Vezvaei 1994; Vezvaei & Jackson 1995; Jackson 1996) and these focus on the reproductive biology, gene flow or phenology of the flowers, rather than non- Apis sources of pollination services. Australia has a unique and endemic-rich insect fauna (Michener 1979; Austin et al. 2004; Batley & Hogendoorn 2009), including many species important for the pollination of a variety of crops (Heard 1999; Blanche et al. 2006; Gaffney et al. 2011; Lentini et al. 2012). The dynamics of Australian almond systems are likely to differ substantially from other major growing regions in California and Spain, because of different local climates and ecological communities, and also from other semi- arid locations where the almond is a native species (e.g. Israel; Mandelik & Roll 2009). Considering the economic value of the crop to Australia's economy, and the possibility of disruption to managed pollination services if Varroa reaches Australia, knowledge of potential wild pollinators in Australian almond production systems is needed. Research into the links between management activities and pollinator communities is particularly vital for the ecologically sustainable management of almond crops, and will increase understanding of the importance of wild pollinators to crop production worldwide (Potts et al. 2010; Garibaldi et al. 2011; Mayer et al. 2011). To my knowledge, this study is the first to sample wild pollinators in commercial almond orchards in Australia, and one of the first to investigate the relationship between wild pollinators and living ground cover in tree crop systems anywhere in the world. Specifically, I asked the following questions: (1) How does the richness and abundance of insect pollinators in commercial almond orchards compare to wild pollinator communities present in proximate native vegetation? (2) Is there a relationship between living ground cover and insect pollinator richness and abundance within commercial almond orchards? I refer to native bees, wasps and flies as potential pollinators (hereafter, pollinators). Although I did not quantify pollination efficiency in this study, my target species are the most recognised and frequent flower visitors and pollinators in a variety of ecosystems (Kevan & Baker 1983; Kearns 2001; Larson et al. 2001) and I observed flies and wasps visiting almond flowers during sampling.

26 3.2 Methods

In this paper, the following terms are used: ‘broadacre monoculture’ refers to a cultivation system in which a crop is grown in a continuous, strict monoculture (i.e. no other crop or managed plants within the physical boundaries of the system) across an area of land greater than 100 hectares; ‘living ground cover’ refers to a carpet of living native and/or non-native herbs and grasses that is sown or naturally-seeded, and is maintained within a cultivated orchard system.

3.2.1 Study area and system

This study was conducted in 2010 in northwest Victoria, Australia, in the Victorian Murray Mallee Bioregion, specifically in the neighbouring Wemen — Liparoo and the Colignan —Nangiloc districts (34° S 142° E). The study area is semi-arid and is dominated by irrigated agriculture and areas of native mallee vegetation on sandy, calcareous soils. During the sampling year, the region recorded wetter than average rainfall – approximately 528 mm fell near Wemen — Liparoo in 2010 (station 76000, long-term average = 314.9 mm p.a.), while Colignan —Nangiloc received approximately 427 mm (station 76052, long-term average = 290.4 mm p.a.). Average temperatures across both districts in the months of sampling (August-September 2010) ranged from minimums of 5-7 oC to maximums of 15-20 oC (Bureau of Meteorology 2013). Sampling sites were located within five commercial almond orchards and proximate native mallee vegetation, which is the dominant native vegetation type in this region (Figure 3.1). Orchards were managed as either broadacre monoculture orchards with bare- ground floors (hereafter MONO) or plant-diverse orchards with living ground cover throughout (hereafter GRASS) (Table 3.1). Native mallee sites were located within patches of remnant mallee vegetation greater than 50 metres across and within 500 m of an almond orchard (hereafter Native Liparoo (NL), Native Wemen (NV) and Native Colignan (NC)). All mallee patches were either adjacent to one of the sampled almond orchards or separated from an orchard by a roadway, field headland or barren area. The three GRASS orchards were all within 3 km of each other and the two MONO orchards were 7.5 km apart. All orchards were located within the Sunraysia agricultural region, which is a mosaic 27 ivated ivated 3.9 (0.65) 3.6 (1.38) Absolute plant Absolute richness 0.3 (0.33) 0.4 (0.43) 76.4 (11.01) 92.0 (6.8) % ground ground % cover 0.007 (0.01) 0.02 (0.03) 9 8 No. of No. of sampling sites 15 15 None None Complete Complete Ground Ground cover Native vegetation, Native agricultural vegetation, Native agricultural vegetation, Native agricultural Agricultural Surrounding Surrounding Matrix Landscape Conventional (no Conventional or pesticide insecticide) (no Conventional or pesticide insecticide) Biodynamic Biodynamic Management 165 76 57 Area Area (ha) 1002 Small, mixed farm Small, mixed farm (GRASS) Broadacre Broadacre monoculture (MONO) Small, mixed farm (GRASS) Broadacre Broadacre monoculture (MONO) Vegetation Type Vegetation Almond orchards and mallee sites used for sampling. ‘Mixed farm’ means that almond blocks were part of larger of that cult farms part were blocks almond that sites used sampling. ‘Mixed means mallee farm’ for and orchards Almond multiple crops within their boundaries (e.g. citrus, other nuts or vegetables). Area, management, management, and (e.g. vegetables). citrus, nuts matrixArea, or are other sites, boundaries their information within multiple for crops Native not given sites within in values plant richness Ground with sampling across all varying averages locations. nativecover are and vegetation sites patches were as Methods for sampling location details of sampling). (see the relevant Nangiloc Colignan B Colignan Wemen location Liparoo Sampling Sampling Table 3.1 28 3.0 (0.58) 3.0 (0.56) 2.1 (1.84) 3.2 (1.14) 60.1 (14.7) 63.4 (13.11) 46.3 (43.4) 57.4 (22.8) 13 10 15 15 Inter-row - - - Agricultural ------Conventional Conventional (low-intensity) - - - 160 Native mallee mallee Native (Native) mallee Native (Native) mallee Native (Native) Small, mixed farm (GRASS) (NC) Colignan Colignan Wemen (NV) Native Liparoo (NL) Liparoo Native Native Native Colignan C Colignan 29 landscape of irrigated crop systems and remnant areas of native mallee woodland, black box ( Eucalyptus largiflorens ) woodland and river red gum ( E. camaldulensis ) forests. The average proportion of natural vegetation within a 1 km radius around GRASS sites was 0.21 ± SD 0.08, and the average proportion around MONO sites was 0.36 ± SD 0.17. The minimum distance between a GRASS orchard and a MONO orchard was 35.1 km. Study locations were limited by the availability of almond orchards maintaining ground cover – most conventional almond growers maintain bare orchard floors, as this has historically been advocated to prevent frost damage and competition for soil moisture (NSW Agriculture, 2002; Wilkinson, 2012). A total of 30 MONO sites, 27 GRASS sites, and 43 Native sites were successfully sampled during my study. All sampling sites were chosen a priori , to ensure site independence, and all were at least 200 m apart. Sampling sites within orchards were spaced as broadly as possible so that the entire area of each orchard was represented in the samples.

Figure 3.1 Representative photographs of the three vegetation types sampled in this study: (top left) broadacre monoculture almond orchards (MONO); (top right) plant-diverse orchards with permanent living ground cover (GRASS); and (bottom) native mallee vegetation (Native).

30 Liparoo and Wemen farms were owned and operated by a multinational company, and were broadacre, conventional monoculture plantations that were representative of commercial almond orchards common to the region. Herbicides were applied (Glyphosate, Spray.Seed and occasionally Striker®) at label rates as required by seasonal management regimes (Richmond, M. 2011, pers. comm., 10 June). No pesticides or insecticides were used. Bare compact soil was maintained across the orchard floors in both these farms (Figure 3.1). The Colignan and Nangiloc farms were owned and operated by a local family-run farm business. Nangiloc and Colignan B were small, multifunctional biodynamic farms (a type of holistic organic agriculture). Permanent living ground cover was consistent throughout these orchards (Figure 3.1) and was managed by mowing when necessary for access, usually before harvest (January-March). Colignan C was a small, multifunctional conventional (low-intensity) orchard. Bare soil was maintained along almond tree lines, comprising a strip approximately 1 m wide surrounding the tree trunks, and permanent inter-row ground cover was managed with mowing. No herbicides or pesticides were used in these orchards (Keens, I. 2010, pers. comm., 19 August). Mowing schedules were not recorded in this study, but ground cover plants were well-established and flowered along tree lines and throughout inter-row sections during sampling, as detailed below. I did not include a biodynamic-conventional management comparison in this study, as the sampled GRASS orchards were all small-scale farms operated by the same family, with similar management strategies and in the same geographical area, and the sampled MONO orchards were managed with very low chemical usage. Some low-impact or small-scale farms operate under similar principles and exhibit similar ecological characteristics to organic farms (e.g. maintenance of in situ plant diversity; Francis & Porter 2011), but cannot be labelled as ‘organic’ due to policy and certification systems. Hence, I focus my investigation on the relationships between ecological characteristics in the crop systems (Letourneau & Bothwell 2008; Winqvist et al. 2012), rather than the anthropocentric label of ‘management type’.

31 3.2.2 Insect Sampling

Insects were sampled during the almond flowering period (August-September) over 10 days in 2010. Almond flowers for a very short time (typically 2-3 weeks per tree) in response to weather cues (Hill et al. 1985), restricting the most suitable sampling time. Each sampling site was sampled once for approximately 8-10 daylight hours (depending on seasonal conditions) using a set of three pan traps – one blue, one yellow and one white bowl. Traps were made from plastic picnic bowls (Woolworths Home Brand, Bella Vista NSW) painted with UV- bright fluorescent blue or yellow paint (White Knight Paints, Lansvale NSW) or left white, and filled with 200 mL of water and a drop of dishwashing detergent to break surface tension. Because of the cool winter temperatures during sampling, I set traps out soon after sunrise (8-10 am) and collected them just before nightfall (3.30-6 pm) in the order they were set, to capture maximum insect activity during the warmest part of the day. All sampling days were fine, sunny or partly cloudy, with light or no breeze. Pan trapping may not provide full representation of the sampled pollinator community, as the effectiveness of the traps can be influenced by trap placement, surrounding habitat structure, proximate resource availability or even insect colour vision (Cane et al. 2000; Baum & Wallen 2011; Saunders & Luck 2013; see Appendix 1). However, the method can be more useful than other insect collection methods, such as on-site visual identification, as it does not suffer from ‘collector bias’ (Westphal et al. 2008) and is efficient for establishing baseline data for previously unstudied ecosystems. To increase the likelihood that pan trap data are ecologically meaningful, it is important to ensure that bowl colour and field placement are consistent and relevant to the context of the study and research questions (Tuell & Isaacs 2009; Vrdoljak & Samways 2012; Saunders & Luck 2013). I chose white, yellow and blue traps, as white was the predominant flower colour in orchards and yellow was the most common flower colour in local mallee woodlands. I also included blue traps, because this colour has been recommended for attracting bees in some environments, but has not been widely used to collect native bees in Australia (see Appendix 1). At each site, a set of traps (one of each colour) was placed in a triangular array on open ground 1 m from a flowering tree, to provide maximum visibility.

32 Upon collection of the traps, insects were removed from the water and placed into 70% ethanol, before being transported to the laboratory. I pooled catches from each set of traps (blue, yellow, white) to count individuals per site. Native bees were identified to species by a taxonomic expert (K. Walker, Museum Victoria) and Iidentified wasps and flies to family level using specialised Hymenoptera and Diptera identification keys (Hamilton et al. 2006; Stevens et al. 2007). Australia's invertebrate fauna is one of the most under-described in the world and many insect orders require critical taxonomic revision, including the Diptera (Austin et al. 2004; Raven & Yeates 2007). Family-level identification is an appropriate taxonomic surrogate when research goals include comparing broad estimates of ecosystem service providers (Oliver & Beattie 1996; Obrist & Duelli 2010), and I considered this a suitable level of identification for this study, given the challenges of identifying Australian wasp and fly taxa to the species level and the uncertainty about the taxonomy of these groups.

3.2.3 Ground cover sampling

Compositional differences in herbaceous plant species were not quantified in this study; however, many of the exotic (introduced to Australia) ground cover species within GRASS orchards were flowering during the study (e.g. Persian speedwell (Veronica persica ), Capeweed ( Arctotheca calendula ), dog violet ( Viola spp.), Oxalis spp.). In MONO orchards, the few ground cover species present were non- flowering seedlings (e.g. self-propagated almonds, stinging nettle ( Urtica dioica ), wild lettuce ( Lactuca spp.) and radish ( Raphanus spp.)). At native mallee sites, most available flowers were in middle- or upper-storey trees and shrubs. Percentage ground cover and plant species richness were quantified at each sampling site once on the same day that insects were collected. At each site, I used four 0.5 metre x 0.5 metre quadrats placed randomly 1-5 m from the centre of each set of pan traps, in each cardinal direction (N, S, E, W). Percentage ground cover (hereafter ground cover) and absolute plant species richness (hereafter plant richness) values from the four quadrats (N, S, E, W) in each site were averaged to obtain mean percentage ground cover and mean plant richness at each site (Table 1).

33 3.2.4 Data analysis

Analyses were conducted on combined Hymenoptera and Diptera samples (response variable: Total Insects), and each separate pollinator group (response variables: flies; native bees; wasps). I used PAST 2.17 (Hammer et al. 2001) for statistical analyses, unless indicated otherwise. Abundance data were overdispersed ecological count data with a non-normal distribution. Hence, I used nonparametric analysis where applicable rather than transforming the data to suit parametric analyses, as this approach is increasingly recommended (McArdle & Anderson 2004; O’Hara & K otze 2010; Warton & Hui 2011). For each vegetation type, I calculated the interquartile range for each insect group’s abundance (as a measure of statistical dispersion) and the number of taxa per group found in each vegetation type, as a proportion of the total number of taxa of each group collected across the study area. I tested for differences in insect abundance (total counts of individuals) between vegetation types with Mann-Whitney ( U) pairwise comparisons. To see how wild pollinator communities in native vegetation compared with communities in almond orchards, I calculated Whittaker’s measure of shared richness (β w) between vegetation types, where 0 means site pairs have the same species composition and 1 means site pairs have completely different communities. Because sampling effort was limited and sample sizes differed between vegetation types, I computed extrapolated species accumulation curves (EstimateS version 9.1; Colwell 2013) to compare estimated total richness of each insect group between vegetation types (Colwell & Coddington 1994; Gotelli & Colwell 2001; Colwell et al. 2004). Native sites had the largest reference sample (n = 43), so richness estimates for GRASS and MONO sites were extrapolated to a sample size of 43, which is less than the maximum recommended extrapolation limit (Melo et al. 2003; Colwell et al. 2012). Extrapolation curves were calculated using the Chao1 target richness estimator (Colwell et al. 2012). Using these estimates, I plotted estimated total family richness for flies and wasps and estimated total species richness for native bees (based on 100 randomisations) by vegetation type, as a function of the number of sampled individuals. This approach is suitable for analysing any taxonomic level and is recommended for sample-based abundance data, and for evaluating theoretical predictions or models (Gotelli & Colwell 2001; Colwell et al. 2012). 34 In addition to estimating pollinator richness, I also created diversity profiles for pollinator groups in each vegetation type using PAST 2.17 (Hammer et al. 2001). Flies and wasps were analysed as family groups, and bee species were combined into genera, as there is the potential for taxonomic similarity at the species-level to affect the results of diversity profiles (Leinster & Cobbold 2012). Diversity ordering (or 'profiling') measures and compares taxonomic richness between communities using multidimensional families of diversity indices rather than a single index (Patil & Taillie 1982; Tóthmérész 1995; Liu et al. 2007; Leinster & Cobbold 2012). Calculations based on one index can show much less complexity than is truly present in a study system (Magurran 2004; Gattone & Di Battista 2009), which can result in inconsistencies in analyses and interpretations. Diversity ordering reduces this effect and provides a more realistic comparison of community diversity by measuring a series of related indices and constructing a diversity ‘profile’ of the community (Tóthmérész 1995; Liu et al. 2007; Leinster & Cobbold 2012). I used the exponential of Rényi's diversity ordering index, which plots the community’s diversity order against a continuous sensitivity parameter (α) (Tóthmérész 1995; Hammer et al. 2001). The parameter is based on true diversities (Hill numbers) and represents how sensitive the diversity order is to the number of species (Jost 2006). The left side of the graph (α = 0) represents the absolute richness of the community and the number of rare species, while the right side of the graph (α = 4) represents dominance and the numbe r of common species (Hill 1973; Leinster & Cobbold 2012). Where a sample's profile sits completely above any other plotted lines, the higher sample is unequivocally more diverse than other samples. Where two lines intersect, the relevant samples cannot be explicitly separated from one another in terms of overall diversity. However, the intersection point of the two lines, when interpreted relative to α, shows how the communities differ in terms of dominance or richness (Leinster & Cobbold 2012). To explore the overall relationship between ground cover in almond orchards and abundance and richness of each insect group, I conducted generalised linear modelling in IBM SPSS 20 (IBM Corporation 2011). For pollinator richness, I calculated Menhinick’s diversity i ndex for fly and wasp families and bee species at each site, as this index takes individuals per species/family into account and is suitable for unequal sample sizes (Menhinick 35 1964). For abundance data, I used negative binomial regression due to the nature of the overdispersed count data (Seavy et al. 2005; Sileshi 2006; O’Hara & Kotze 2010). Richness data were analysed with linear models, as these data were normally distributed. The maximum number of parameters I could include in any model (to avoid over-fitting) was five, because of the small sample size: n[ GRASS+MONO ] = 57 (Burnham & Anderson 2002). Based on my research questions, the focal predictors were ‘ground cover’ and ‘plant richness’; however, as these two variables were strongly correlated (r = 0.90, p < 0.0001) they were not included in the same model. Orchard sites were distributed at different distances to natural vegetation, so I also included ‘distance to natural vegetation’ as a predictor to determine if landscape context influenced pollinator responses to ground cover. However, I do not consider landscape context further, as the goal was to identify the extent of the relationship between potential pollinators and ground cover vegetation on orchard floors in my study system. I computed models for each predictor alone, an additive and an interaction model for each ground cover variable with the landscape variable, and an intercept-only model for a baseline comparison, and used an information-theoretic approach (Burnham & Anderson 2002) to identify the best explanatory model for each insect group based on the research questions. For richness models, I calculated the second- or der Akaike’s Information Criterion for small samples (AIC c) and for abundance models, I modified this criterion for overdispersed count data (QAIC c); hereafter, both IC values are referred to as AIC c. I also calculated Akaike weights ( wi) for each model, which represent the probability that the model is the best model for the data. Within each model set, I compared differences between AIC c values (Δ

AIC c) to determine the explanatory model that provided the best fit for each insect data set and any other mo dels with substantial support (Δ AIC c ≤ 2; Burnham & Anderson 2002). To determine how pollinator assemblages compared within vegetation types, I calculated the following similarity/distance matrices, based on the 28 pairs of study locations (individual orchard or mallee patch; e.g. Wemen/Colignan

B; Wemen/Nangiloc; Wemen/NC etc.): (i) Bray-Curtis (S BC ) similarities for pollinator abundance data sets (bee species, fly and wasp families) (Faith et al. 1987; Clarke & Ainsworth 1993); and (ii) Euclidean distances for average percent ground cover and average plant richness (Clarke & Ainsworth 1993). I tested for 36 correlations between these indices with a Spearman's rank test, to determine if between-site similarity in ground cover was related to similarity of pollinator communities (Clarke & Ainsworth 1993). I also used hierarchical cluster analysis (Bray-Curtis similarity and unweighted paired-group linkages) to create dendrograms comparing taxonomic similarity between bee, fly and wasp assemblages at each study area.

3.3 Results

3.3.1 Comparison of wild potential pollinator communities between vegetation types

Across all sites, I collected 3251 fly individuals from 25 families, 144 wasp individuals from 19 families, and 90 native bee individuals from 12 species. Lasioglossum (Chilalictus) globosum (Smith, 1853) was the most common bee species overall (25 individuals) and was collected in both GRASS orchards and mallee vegetation. No native bees were collected in MONO orchards. Tachinidae and Calliphoridae were the most abundant fly families, and were collected in all vegetation types (Appendix 2 Table A2.2). The most abundant wasp families (Diapriidae and Braconidae) were also collected in all vegetation types, while six bee species and five wasp families collected in mallee vegetation were not present in almond orchards (Appendix 2 Table A2.2). The interquartile range for each pollinator insect group was highest in GRASS orchards: Flies – GRASS (104) MONO (9.75) Native (10.88); Wasps – GRASS (3) MONO (1) Native (1); Native bees – GRASS (2) Native (1). Based on the total number of taxa (family or species) collected across the study area, Native sites had the highest proportion of recorded taxa for each insect group, but the number of fly families at GRASS sites was equal to Native sites: Flies – GRASS (0.80) MONO (0.64) Native (0.80); Wasps – GRASS (0.63) MONO (0.37) Native (0.79); Native bees – GRASS (0.50) Native (0.92). On average, GRASS sites had higher total pollinator abundance (mean 92.3 ± 82.2) than either Native (mean 14.4 ± 12.2; p = 0.02) or MONO sites (mean 12.5 ± 8.6; p < 0.001). Average abundances for all insect groups were significantly higher in GRASS sites than in Native or MONO sites (Table 3.2). 37 Pairwise comparisons showed that the abundance of Total Insects in mallee vegetation was most similar to MONO orchards ( U = 620.5, z = -0.27, P = 0.788), as was wasp ( U = 587, z = -7.03, P = 0.482) and fly abundance ( U = 638, z = - 0.07, p = 0.94). Native bee abundance was significantly different between all three vegetation types (GRASS ≠ MONO U = 165, z = -4.84, P < 0.0001; GRASS ≠ Native U = 408, z = -2.33, P = 0.019; MONO ≠ Native U = 450, z = -3.27, P =

0.001). Wasp communities at Native sites were most similar to GRASS sites (β w =

0.33) and least similar to MONO sites (β w = 0.45). Wasp community similarity between GRASS and MONO sites was β w = 0.37. Fly communities at Native sites were more similar to MONO sites (β w = 0.22) than GRASS sites (β w = 0.25), and very similar between GRASS and MONO sites (β w = 0.17). Native bee community similarity between GRASS and Native sites was β w = 0.41.

Table 3.2 Average abundance and richness of potential wild pollinator insects collected per site, in native mallee vegetation (Native), broadacre monoculture almond plantations (MONO), and plant-diverse almond orchards (GRASS) in the Murray Mallee region of north-west Victoria. Richness values are based on the Menhinick diversity index (these were not used to compare richness between vegetation types, but are given here for reference). Asterisks denote where abundance was significantly higher than the other two vegetation types. Abundance, mean (± SD) Richness, mean (± SD) Fly Wasp Native bee Fly Wasp Native bee Native 12.8 (11.81) 0.9 (1.74) 0.7 (1.2) 1.4 (0.5) 0.5 (0.7) 0.4 (0.6) MONO 11.5 (8.5) 1.0 (1.35) 0 1.4 (0.6) 0.6 (0.6) 0 GRASS 87.2 (83.3)* 2.8 (2.1)* 2.3 (4.2)* 1.3 (0.6) 1.1 (0.8) 0.7 (0.6) * p < 0.05

Comparison of extrapolated richness curves suggested that my sampling effort may not have captured all taxa present in my study system, as only the curves for native bees at GRASS sites and flies at Native sites approached an asymptote. Native mallee sites had more unique bee species and fly and wasp families than either orchard type, and GRASS sites had consistently higher abundance across all insect groups (Figure 3.2). There was no significant differences in wasp richness between vegetation types (Figure 3.2a), but fly and bee richness were significantly higher at Native than at GRASS sites (Figure 3.2b-c).

38 25 (a)

20

Native 15 GRASS

10

MONO No. of wasp wasp of families No. 5

0 0 20 40 60 80 100 120 35 (b) 30

25 GRASS Native 20

15 MONO 10

No. of fly fly of families No. 5

0 0 500 1000 1500 2000 2500 3000 3500 4000 14 (c) 12 Native 10

8 GRASS 6

4

2 No. of native native of bee species No. 0 0 20 40 60 80 100 120 No. of collected individuals Figure 3.2 Accumulation curves showing the number of (a) wasp families (b) fly families and (c) native bee species per number of individuals collected in each vegetation type. Broken lines indicate the 95% confidence intervals for each curve.

39 Community diversity profiles were mostly consistent with these patterns (Figure 3.3). Wasp and bee communities were more diverse in native mallee vegetation than in almond orchards (Figure 3.3a,c). Fly diversity profiles from all three vegetation types overlapped, but Native sites had slightly higher richness of rare families (Figure 3.3b). Between orchard types, wasp and fly diversity were similar, but GRASS orchards had higher richness (more rare families) while MONO orchards had slightly higher dominance of common families (Figure 3.3a- b).

3.3.2 Insect pollinator and ground cover association

The predi ctor ‘distance to natural vegetation’ was present in highly -ranked models for fly abundance, wasp abundance and richness, and native bee richness, but there was no evidence that this predictor had a strong influence over pollinator relationships with ground cover in almond orchards (Table 3.3). Fly richness and abundance were not associated with either percent ground cover or plant richness. Percent ground cover was positively associated with bee and wasp richness and wasp abundance. Native bee abundance had a strong positive association with plant richness, and this predictor also had some influence on variation in wasp richness (Table 3.3). Taxonomic similarity between bee communities increased with similarity in percent ground cover (r s = 0.67, P < 0.001) and plant richness (r s = 0.71, P < 0.001) between sites. Between-site correlations of differences in ground cover with wasp and fly community similarity were weak (r s < 0.3) and not significant (p > 0.1). Wasp communities in the monoculture Wemen orchard were more similar to GRASS orchards (S BC : Wemen/Nangiloc = 0.74; Wemen/Colignan B =

0.51; Wemen/Colignan C = 0.59), than to the other MONO orchard, Liparoo (S BC = 0.41). Fly communities at the two biodynamic orchards (Colignan B and

Nangiloc) were more similar to each other (S BC = 0.68) than to communities at any other location (average between-site S BC = 0.25). Bee communities at Colignan C were significantly different to bee communities at any other location

(maximum between-site S BC = 0.18) (see Figure 3.4).

40 (a) 14 Native 12 GRASS 10 MONO 8

6

4 Wasp diversity Wasp 2

0

25 (b)

20

15

10 Fly diversity Fly 5

0

(c) 4.5 4 3.5 3 2.5 2 1.5 1

Native bee diversity bee Native 0.5 0 0 1 2 3 4 Sensitivity paramater ( α) Richness Dominance

Figure 3.3 Diversity profiles for (a) wasp families (b) fly families and (c) native bee genera collected in each vegetation type. The left hand side of the plotted line (α = 0) represents absolute richness and relative number of rare species in the community and the right hand side (α = 4) represents dominance and relative number of common species. 41 i 0.60 0.54 0.27 w 0.37 0.17 0.25 0.23 0.43 0.34 0.31 0.26 0.13 c 0 0 1.4 Δ AIC 0 1.5 0 0.2 0 0.4 0 0.4 1.8 c 88.0 72.2 73.6 AIC 325.5 327.1 107.9 108.1 201.9 202.3 127.9 128.3 129.7 -ranked model, only the second-highest model, -ranked only = 0.13 = 2 = 0.32 = 2 coefficients are given for linear models only. The models highest-ranked are linear modelsfor given coefficients 2 = 0.04 = 2 = 0.10 = 0.31 = 2 2 = 0.07 = 2 ) for each model indicate the probability that the given model is the best fit for the model fit that the is for best the given probability each the model indicate ) for i w GC (0.007, 0.004 to 0.01); R GC (0.007, 0.004 to 0.01); GC (0.006, 0.003 to 0.01) GC (0.006, 0.003 0 to 0.002) D (0.001, + GC (0.007, 0.003 to 0.01) R GC (0.006, 0.001 to 0.01); PR 0.52 to 1.8) (1.18, Model -0.14 (-0.11, to -0.09) Constant only 0 to 0) D (0, 0.001); to -0.007 GC (-0.003, R Constant only 1.18(1.33, to 1.49) Constant only GC (0.006, 0.002 to 0.01) + D (0.001, 0 to 0.002); D (0.001, + R GC (0.006, 0.002 to 0.01) 0.001); R 0 to D (0, + GC (0.007, 0.005 to 0.01) PR 0.008 to 0.22); R (0.12, Richness Abundance Richness Abundance Abundance Richness Parameter estimates with confidence intervals from negative binomial regression (abundance) and linear regression (richness) linear regression negative intervals (richness) and binomial (abundance) from estimates with confidence regression Parameter analysis ≤ 2) are presented for each insect response variable. Where the ‘constant only’ model was response Where ‘constant the highest model the insect each presented variable. only’ ≤ 2) are for c ranked model has been ( comparison. has model Akaike weights for given ranked GC = percent ground cover; PR = plant richness; D = distance to natural or distance cover; semi-natural PR richness; plant D = vegetation. = All parameter. percent models GC = the constant as a ground include Native bee Native Wasp Fly Insect response Insect Table 3.3 insect variables. the relationshipR between andcover groups testing ground data. (Δ AIC (Δ 42 native bee species per site species per native bee group. (right) fly families and and families fly (middle) families wasp (left) Bray-Curtis similarities between similarities between Bray-Curtis Native sites: NL, NC; NAN.NV, GRASS sites: NL, sites: MONO sites: Native COL, LIP; CC,WEM, Figure 3.4 Figure 43 3.4 Discussion

I found that overall richness of all three potential pollinator groups was highest in native mallee vegetation. However, almond orchards with permanent living ground cover throughout the orchard supported greater wild pollinator richness and abundance than orchards without ground cover. Total insect abundance, wasp abundance and fly richness and abundance at mallee vegetation sites were more similar to MONO orchards than GRASS orchards, but no native bees were caught in MONO orchards during the sampling period. Mallee wasp families were more similar to wasp families in GRASS orchards than to those in MONO orchards. Flies were not influenced by ground cover in almond orchards, but ground cover did have a positive association with wasp and native bee abundance and richness. The strongest positive relationship was between native bee abundance and richness of herbaceous ground cover plants. Native bee community similarity between sites was strongly correlated with similarity in ground cover attributes, but wasp and fly community similarity were not correlated with ground cover similarity between sites. Cluster analysis (Figure 3.4) showed variation in pollinator assemblages within vegetation types. Wasp assemblages at the monoculture Wemen orchard were more similar to GRASS orchards than the other monoculture orchard, while bee abundance in one GRASS orchard was higher than in any other study area. In addition, no native bees were caught at any MONO site, despite bees being present in proximate native vegetation. I tested for the influence of broader landscape factors on insect response but this did not override pollinator relationships with ground cover in my system. However, the uncertainty of some pollinator —ground cover relationships, the lack of association between ground cover and fly communities, and the pollinator community differences between orchards, indicate that other factors may be influencing some pollinator groups in this system. Wild insect pollinators often prefer open, heterogeneous, ‘moderately’ anthropogenic habitats, such as grasslands, fallow agricultural fields, meadows or cultivated gardens over more dense forests (Hagan & Kraemer 2010; Samnegård et al. 2011; Winfree et al. 2011). In particular, most solitary bee species require access to multiple heterogeneous habitats throughout the year (Mandelik et al. 2012), as they prefer herbaceous floral diversity for food sources and fallen wood, 44 bare soil or other structures for nesting (Hoehn et al. 2010; Gruber et al . 2011). Most modern conventional agricultural systems do not offer this heterogeneity of food and nesting resources, providing only one species of flower with a short blooming season, homogeneous soil conditions and trees pruned of any dead wood that may provide nesting substrate. This is especially true for perennial tree crop systems, such as fruit and nut orchards, when they are managed intensively as strict monocultures with no ground cover or intercropped vegetation. Despite intensive management, orchard systems are still structurally similar to some types of dense forests (such as temperate deciduous forests), which also generally harbour low bee abundance (Winfree et al. 2007; Hoehn et al. 2010; Proctor et al. 2012). Unlike orchard crops in North America and Europe, which are often structurally similar to native deciduous forest systems, Australian almond orchards are very different to surrounding evergreen mallee woodlands. These differences could potentially influence the extent of pollinator services that may come from insects living in mallee and are worth exploring further in the future. The monoculture orchards I sampled had bare orchard floors, which could provide suitable nesting substrate for some wasp and bee species, but had no floral resource diversity. In contrast, the biodynamic orchards had a carpet of floral/herbaceous diversity, but few areas of bare ground. Only one GRASS orchard, Colignan C, had inter-row herbaceous ground cover and bare ground along the tree line. Interestingly, I caught more wasps and native bees in Colignan C than at the other GRASS sites, even though Colignan C had less consistent ground cover than the other plant-diverse orchards. Pan traps were more visible at this orchard than in the other two GRASS orchards, which could have increased catch rates (Laubertie et al. 2006). However, it is unlikely that my results were unduly influenced by trap visibility, as traps were more visible in MONO orchards where ground cover and understorey vegetation were sparse and overall heterogeneity was very low, yet I caught no bees at any MONO site. These results more likely indicate that herbaceous ground cover can support many wild insect pollinators in commercial orchards and that a diverse array of within-orchard habitat elements may interact with the presence of ground cover to sustain insect communities in the long-term; however, more sites with similar attributes would need to be sampled to validate this possibility.

45 Although living ground cover in Australian almond orchards has historically been considered to have a negative influence on moisture availability, orchard microclimates and harvest efficiency (Wilkinson 2012), finding a balance between maintaining understorey vegetation and management efficiency could have positive implications for overall ecosystem function and productivity. Ground cover in almond orchards can improve soil quality and biological activity (Ramos et al. 2010), which is particularly important in arid ecosystems (Whitford 1996), and it can also support important natural control agents for almond pests like the navel orange worm (Eilers & Klein 2009). There has been very little research into direct links between permanent living ground cover in commercial orchards and pollinator richness and abundance; however, my results correlate with current knowledge of similar study systems. The only directly comparative study I am aware of found greater wild bee abundance in a weedy section of an almond orchard in Israel compared to a non-weedy part of the orchard, although the authors do not provide further details on how vegetation composition differed between the two sections of orchard (Mandelik & Roll 2009). Studies from other fruit or nut orchard systems have also shown that ‘natural enemy’ insect abundance (including wasps) increased as within-orchard plant diversity (cover crops or weedy ground cover) was enhanced or left to grow unmanaged (Tedders 1983; Horton et al . 2003; Brown 2012). Evidence from European agroecosystems showed that wild bee abundance in apple orchards responded positively to increased extent of fallow areas (unmanaged, grassy or weedy) surrounding orchards (Gruber et al. 2011) and that wasps were more abundant in abandoned (weedy) orchards than in managed orchards (Steffan-Dewenter & Leschke 2003). Crop pollinator abundance and diversity within vegetable or seed crop systems also responds positively to non-crop plant diversity within the field area (e.g. Jones & Gillett 2005; Gemmill-Herren & Ochieng 2008; Carvalheiro et al. 2011). Many studies have shown the positive effect of nearby native vegetation on pollinator richness or abundance in agricultural systems (e.g. Ricketts et al. 2008; Watson et al. 2011; Lentini et al. 2012; Lowenstein et al. 2012), including in North American almond orchards (Klein et al. 2012). However, very few of these studies provide comparative analysis of pollinator abundance in native vegetation relative to abundances within the crop system. A notable exception is Kehinde & Samways’ (2012) recent study from South Africa, which investigated 46 insect pollinators in organic and conventional vineyards as well as proximate natural vegetation. They showed higher pollinator abundance in both organic farms and natural vegetation than in conventional farms. Although their analysis focussed on the ‘organic vs. conventional’ responses of wild pollinators, the authors note that organic vineyards contained flowering cover crops, which were not present in conventional vineyards, and they suggest this may have been the cause of higher insect abundance in organic vineyards (Kehinde & Samways 2012). It is clear that plant-diverse crop systems harbour higher pollinator abundance, yet, historically, relevant studies have focused on the correlation of plant and insect diversity isolated within the crop system itself. Some authors ha ve hypothesised that ‘weedy’ crop systems (compared to intensive or monoculture systems) may have enormous conservation potential for local insect pollinators (Gemmill-Herren & Ochieng 2008; Hyvönen & Huusela-Veistola 2008; Pinke & Pal 2009), but there is scope for further detailed investigation into how plant-diverse crop systems actually compare to local native vegetation as suitable pollinator habitat. In conclusion, maintaining permanent living ground cover as part of commercial orchard management practices has the potential to support higher wild pollinator abundance, particularly native bees, which has important conservation implications and could also supplement pollination services for pollinator-dependent almond crops. The significance of my findings would be augmented by further investigation of pollinator visitation rates and pollen transfer potential. In addition, further research in a variety of orchard systems would determine whether certain insect pollinator species benefit more from living ground cover than others. It is also important to consider that a suite of interrelated environmental variables may be of combined benefit to pollinator communities, rather than one single variable on its own. There is abundant scope for further research, in both my study system and other agroecosystems, linking wild insect pollinator communities with combinations of related environmental variables (e.g. ground cover presence/diversity and habitat structure) in agroecosystems. Nonetheless, this study has shown that there is substantial conservation potential for orchards with permanent ground cover, as unmanaged potential insect pollinators, especially native bees, were more abundant in plant- diverse orchards than in native mallee vegetation. Long-term studies in multiple 47 types of plant-diverse orchard systems will be useful for determining whether this trend could motivate valuable pollinator conservation initiatives within the orchard industry.

48 Chapter 4: Relationships between wild pollinators in flowering almond orchards and proximate unmanaged vegetation

Abstract

Resource heterogeneity and connectivity is critical for maintaining diverse pollinator communities in landscapes dominated by tree crop plantations, which create large areas of spatial and temporal homogeneity. I collected potential wild pollinators (native bees, wasps, and flies) in flowering almond orchards within a heterogeneous (Complex) and homogeneous (Simple) agricultural landscape in southern Australia. I examined relationships between wild pollinators and vegetation types immediately surrounding each site, particularly natural vegetation that provided permanent floral resources. Abundance and richness of most individual pollinator groups were negatively associated with the proportion of natural vegetation near sites in the Complex landscape and positively associated with natural vegetation near sites in the Simple landscape, although the strength and significance of the relationships varied. In the Simple landscape, most individuals of the key pollinator taxa (native bees and hoverflies) were collected in patches of natural vegetation inside the almond plantation, rather than within blocks of almond trees. These results suggest that wild pollinators visiting crops in homogeneous landscapes are more dependent on proximity to permanent, unmanaged resources than wild pollinators using heterogeneous landscapes. More research is needed into how landscapes dominated by tree crop monocultures influence biodiversity and the provision of essential ecosystem services.

49 4.1 Introduction

Large-scale intensification of agricultural landscapes severely impacts ecological processes and farmland biodiversity (Matson et al. 1997; Tilman 1999) and can have widespread effects on community composition (e.g. Ekroos et al. 2010) and the provision of ecosystem services (e.g. Marino & Landis 1996). To address these effects, agricultural landscapes can be managed to increase vegetation complexity and diversity, which can enhance biodiversity levels and ecosystem function (Benton et al. 2003; Tscharntke et al. 2005). Vegetation heterogeneity is particularly important for wild insect pollinators in agricultural landscapes and, although some pollinators may be attracted to the temporary surplus of flowers in crop fields (Holzschuh et al. 2013), the long-term establishment of a diverse pollinator community depends on multiple foraging and nesting sites being available throughout the year (Kremen et al. 2007). Numerous studies have examined relationships between pollinator communities and the amount of particular habitat types within agricultural mosaics, such as semi-natural grasslands (Öckinger & Smith 2007), natural forest (Garibaldi et al. 2011 and references therein) or sown wildflower strips around crop fields (Haaland et al. 2010 and references therein) and retention of unfarmed habitat is considered essential for maintaining diverse pollinator communities around farmland. However, a matrix that provides multiple types of habitat with different, but complementary, levels of spatial and/or temporal heterogeneity is often more important for the long-term establishment of wildlife communities than a single permanent habitat type (Westrich 1996; Williams & Kremen 2007; Vasseur et al. 2013). This type of matrix, providing resource heterogeneity and functional connectivity, is particularly important in landscapes dominated by perennial tree crop plantations, such as orchard fruits, because most fruit varieties depend on insect pollination to set fruit (Kevan 1999); therefore, the presence of a diverse pollinator community during crop flowering can be essential for some growers (Garibaldi et al. 2013). Yet, unlike arable crop mosaics under short rotation that can provide temporal heterogeneity in the absence of spatial variation (Vasseur et al. 2013), tree crop plantations are generally embedded in the landscape for more than 15 years, creating a comparatively ecologically stable, semi-permanent 50 ecosystem (Tedders 1983; Altieri 1999). If plantations are managed as intensive monocultures, they create areas of permanent spatial and temporal homogeneity in the landscape that are inhospitable to pollinators for most of the year (Sheffield et al. 2008; Marini et al. 2012) and could potentially influence the structure of invertebrate communities in the long-term (Harding et al. 1998; Tscharntke et al. 2002). Although the conservation value of tree crop agriculture has long been recognised (Smith 1916; Altieri et al. 1983; Ryszkowski & Kędziora 1987), the ecological role of tree crop plantations in agricultural landscapes has received less attention in the scientific literature than annual crops (Hartemink 2005). Historically, studies of orchard crop systems have focused on within-orchard management practices to support beneficial ecosystem services like biological pest control (e.g. Bugg & Waddington 1994; Brown 1999; Horton et al. 2003). To inform biodiversity conservation and ecological management goals, there is a need for more studies that investigate how established orchard crop plantations influence communities of ecosystem service providers through interactions with the surrounding landscape and ecosystems, particularly local pollinator communities present during the tree crop’s brief flowering period ( sensu Watson et al. 2011; Marini et al. 2013). Here, I focus on plantations of almond ( Prunus dulcis Mill.) trees in a semi-arid region of southern Australia. Almond production contributes to more than half of Australia’s annual nut production and the country is the second- largest almond producer in the world, with broadacre plantations expanding across Australia’s major agricultural regions in the last few decades (Wilkinson 2012; Almond Board of Australia 2013). Cultivated almond trees flower synchronously for 3-4 weeks at the end of winter (Hill et al. 1985) and are totally dependent on insect pollination to set fruit, yet there is little published information on wild pollinators of almond flowers in Australia or how communities of these insects are influenced by almond plantation management (but see Saunders et al. 2013). Over 60 % of Australian commercial plantings are established in the semi- arid mallee region of northwest Victoria (Almond Board of Australia 2013), an area of high conservation value (Mallee Catchment Management Authority 2008). Semi-arid landscapes are naturally dynamic and heterogeneous (Sala & Lauenroth 1982; Aguiar & Sala 1999), especially in Australia (Ludwig & Tongway 1995; Morton et al. 1995) where they are being increasingly impacted 51 by agricultural intensification. Yet, despite much discussion of how agricultural fragmentation impacts natural ecosystems in other parts of Australia (e.g. Lindenmayer & Hobbs 2004; Haslem & Bennett 2008; Smith et al. 2013), there is very little published research into how wildlife communities in dynamic, heterogeneous semi-arid ecosystems like the mallee are influenced by large areas of homogeneous crop plantations (e.g. Driscoll & Weir 2005; Luck et al. 2013). This study aims to contribute to some of these knowledge gaps and is part of the first investigation of potential wild pollinators available to almond crops in Australia’s main almond growing region, the Victorian Murray Mallee. In this chapter, I examine whether the abundance and richness of potential wild pollinators found in almond plantations during flowering are associated with different vegetation types in immediate proximity to sites. Due to the lack of ecological information available on my study system, I sampled flowering orchards in two different types of agricultural landscape (heterogeneous and homogeneous) and concentrated on documenting patterns of association between potential wild pollinators collected in flowering orchards and the presence of unmanaged vegetation immediately surrounding sites. Specifically, I asked two questions: (i) is pollinator community composition different at sites surrounded only by crops, compared to sites surrounded by a combination of crops and unmanaged vegetation? and (ii) is there a relationship between abundance or richness (species or family) of individual pollinator groups and proximity to either native mallee vegetation or semi-natural grassland?

4.2 Methods

The study area was located in the Sunraysia agricultural region of northwest Victoria, Australia (approximately -34.47° to -34.83° S; 142.35° to 142.71° E). The region is dominated by irrigated horticulture and large remnant areas of native mallee vegetation. Sampling was conducted over 10 days in late August – early September 2010 during the brief almond flowering period, which lasts approximately 2-3 weeks per tree. A total of 72 sites within commercial almond orchards were sampled in two distinct landscapes. One landscape was a heterogeneous agricultural mosaic (hereafter ‘Complex’) in which I sampled 27 sites within a landscape diameter of 5.3 km. This landscape was characterised by 52 small blocks of diverse organic and conventional farms (mostly tree fruits and vineyards) and semi-natural grassland, as well as large patches of natural vegetation (Figure 1a). The other landscape was a homogeneous landscape (hereafter ‘Simple’) dominated by broadacre monoculture almond plantations, large patches of natural vegetation, and occasional smaller patches of semi-natural grassland or other crops (Figure 1b). I sampled 45 sites in this landscape within a landscape diameter of 14.6 km. Sites in the Complex landscape were all within blocks of almond trees and sites in the Simple landscape were located in either blocks of almond trees or in remnant patches of mallee vegetation within almond orchard interiors or along orchard boundaries (all patch sites within 15 m of an almond block). The two landscapes were approximately 36 km apart, and the minimum distance between any two sites within a landscape was 200 m. Complex landscape orchards were managed by the same family-owned company and had a living ground cover of herbaceous vegetation. Simple landscape orchards were managed by the same corporation and had predominantly bare soil throughout (see Chapter 3). All orchards were managed without insecticide use.

(a) (b) Figure 4.1 Representative images showing landscape composition in the (a) Complex (diameter = 5.3 km) and (b) Simple (diameter = 14.6 km) landscapes. Natural vegetation is recognisable as patches of speckled grey, such as the right of image (a) and the bottom left of image (b). In the Complex landscape, crop fields are a diverse array of mostly citrus, avocado, almond, walnut, and table grape vineyards. In the Simple landscape, almond crops cover the bottom half of the circle, as well as the top right corner.

Vegetation types around each site were identified during sampling and the proportional area of each vegetation type within 100 m of each site was calculated in ArcMap 10 using spatial data of agricultural land uses in the study region (SunRISE 21 Inc. 2008). There was no physical overlap in 100 m radii around

53 each site. I focused on a small spatial scale, as mounting evidence suggests that small-scale attributes related to farm-specific management practices can have more influence over local insect populations than landscape-scale heterogeneity or diversity (e.g. Collinge et al. 2006; Kovács-Hostyánszki et al. 2011; Paredes et al. 2013). Pollinators may also travel much shorter distances in agroecosystems when floral resources are plentiful (Zurbuchen et al. 2010; Lander et al. 2011; Holzschuh et al. 2013), as they were in my study system. I did not calculate generalised heterogeneity indices, which often mask the influence of vegetation structure or resource availability (Cale & Hobbs 1994; Tews et al. 2004); instead, I documented the proportional availability of specific resources and vegetation types that were ecologically meaningful to pollinator communities.

4.2.1 Insect sampling and identification

To identify potential wild pollinators in my study system, I focused on individuals from the taxonomic orders Hymenoptera (non- Apis bees, wasps) and Diptera, as these taxa are common pollinators in many natural and agricultural systems (Armstrong 1979; Kevan & Baker 1983; Ssymank et al. 2008). Information on wild pollinator species in the study region is limited, but wasps and flies are known to pollinate native flowers in the area (e.g. Ford & Forde 1976; Horskins & Turner 1999) and individuals of these taxa were seen visiting almond and native flowers during sampling. I collected potential wild pollinators at each site using sets of pan traps and design and placement of traps was standardised across all sites. Each set consisted of one yellow, one white and one blue bowl filled with water and a drop of detergent to break surface tension. Bowls were made from white plastic picnic bowls (Woolworths Home Brand) that were either left white or painted with UV-bright fluorescent yellow or blue paint (White Knight Paints). Data collected from each coloured bowl per site were pooled for analysis, so the effect of trap colour is not considered further in this study. Although pan traps may underestimate the full composition of pollinator insect assemblages, I used this method to minimise collector bias (Westphal et al. 2008) and I selected trap colour and placement to increase the chance that the traps would provide ecologically meaningful results. Yellow and white were the most common flower colours at sites (in mallee and almond respectively) and blue traps are commonly 54 thought to attract bees, although this colour has not been widely used for native bees in Australia (see Appendix 1). At each site, the three bowls were placed together on open ground in a triangular array, adjacent to a flowering tree and away from fully shaded areas for the duration of the sampling period. Because many of my target species overwinter or nest in the ground, leaf litter or ground cover vegetation (Leather et al. 1993), this protocol was designed to ensure maximum visibility during the late winter sampling time, when many of these insects would be in the process of emerging or building nests. Each site was sampled once during the almond flowering period and traps were set out after sunrise (7.30-9.30 am) and collected before sunset (4.00-6.00 pm) in the sequence they were set out, so as to capture maximum insect activity during the warmest part of the day. Insect collection was carried out on days that were fine, calm, and sunny or partly cloudy with maximum daytime temperatures between 15 and 20° C. This sampling protocol was not intended to provide a comprehensive survey of all insect pollinators in this locality; rather, it was designed to provide a comparative sample of potential wild pollinators across a very large region, in particular those using almond orchards in different types of mosaic during the flowering period (Bennett et al. 2006). Upon collection, insects were stored in 70 % ethanol and counted and sorted in the laboratory. Native bees were identified to species or subgenus by a taxonomic expert (K. Walker, Museum Victoria) and I identified wasp and fly individuals to family level using taxon specific keys (Hamilton et al. 2006; Stevens et al. 2006). Managed honey bees ( Apis mellifera ) were present in both landscapes during sampling, but were not included in the data as the purpose of this study was to document wild potential pollinator insects available to almond crops. Wasp and fly individuals were further sorted into small (0-0.49 cm) and large (>0.5 cm) bodied family groups for analysis, based on the average thorax- abdomen length of the individuals in each family group. These size categories were chosen to identify the most likely candidates for almond pollination, based on the size of an almond blossom (3-5 cm) and position of its stamens; hence, individuals over 0.5 cm in length were considered more likely to transfer pollen while visiting blossoms than smaller bodied individuals. The fly family Syrphidae (hoverflies) was isolated as a separate taxonomic unit from other fly groups, as this dipteran family is known to be a common pollinator in agroecosystems 55 (Ssymank et al. 2008). All collected native bee and hoverfly individuals were of the same size class, so each taxon was analysed as a single group.

4.2.2 Data Analysis

Response variables were abundance counts of each insect group (native bee, small wasp, large wasp, small fly, large fly, hoverfly) and overall species (native bee) and family (wasp, fly) richness per site (Table 4.1). All data were analysed untransformed (O’Hara & Kotze 2010). Richness was represented by the bias - corrected Chao 1 abundance-based estimator. Because of the differences in orchard management practices and the lack of landscape-scale replication between the two landscapes (Complex and Simple), I used a non-metric multidimensional scaling (NMDS) ordination on the entire data set to identify if there was a difference in pollinator community composition between the two landscapes. Data were highly aggregated by landscape type (Figure 4.2), and because I was unable to determine if differences in insect diversity between the two landscapes were due to specific landscape structural factors rather than management approaches, I focused on examining relationships within, rather than across, landscapes. Hence, data were pooled by landscape type (Complex, Simple) for the remaining analyses. First, I assessed if overall pollinator community composition differed between sites surrounded by crops only and sites surrounded by crops and natural vegetation. I grouped sites based on their floral resource profile, which was determined by the combination of vegetation types within 100 m of each site: temporary – only perennial fruit crops with a single brief flowering period each year; or permanent – crops plus native vegetation or semi-natural grassland. For each landscape, I used NMDS plots with Bray-Curtis distances to identify similarity among sites based on their resource profiles. I then used one-way non- parametric multivariate analysis of variance with Bray-Curtis similarities (NPMANOVA; Anderson 2001) to test for differences in overall pollinator communi ty composition between groups of ‘permanent’ and ‘temporary’ sites in each landscape. I performed this analysis separately for Hymenoptera and Diptera groups, as previous studies have shown that flies and wasps can respond

56 differently to landscape or floral characteristics (e.g. Jauker et al. 2009; Robson 2013). Table 4.1 Average abundance and richness of pollinator insect groups per site and average proportion of each vegetation type within 100 m of each site (± SE), in Complex and Simple landscapes. Asterisks signify in which landscape each variable was significantly greater; no asterisk means there was no difference between landscapes. Complex Simple (n = 27) (n = 45) Pollinator Native bee 2.26 ± 4.2** 0.33 ± 0.9 abundance Large wasp 0.89 ± 1.2* 0.53 ± 1.6 Small wasp 1.93 ± 1.6** 0.58 ± 1.0 Large fly 57.04 ± 79.1** 7.71 ± 7.2 Small fly 29.96 ± 21.3** 4.49 ± 3.0 Hoverfly 0.15 ± 0.4 0.18 ± 0.7 Pollinator Native bee (species) 1.2 ± 1.4** 0.36 ± 1.0 richness Wasp (family) 2.89 ± 4.4** 0.86 ± 1.2 Fly (family) 11.43 ± 6.5** 7.36 ± 4.3 Vegetation Almond 0.66 ± 0.2 0.55 ± 0.4 Type Mallee 0.09 ± 0.2 0.23 ± 0.3^ Grass 0.08 ± 0.1 0.04 ± 0.1 Grape 0.10 ± 0.2 0.08 + Fruit 0.10 ± 0.1 0 ^ p < 0.09; * p = 0.05; ** p < 0.001 + proportion of ‘grape’ for the Simple landscape is based on one site

I then focused on relationships between individual insect groups and the proportion of native vegetation or semi-natural grassland around each site, as these were the most undisturbed of the recorded vegetation types and also represented the most permanent resource availability throughout the year. I identified the nature of the relationship (positive, negative or neutral) between richness and abundance of each insect group and each of the two focal vegetation types using negative binomial generalised linear models (GLMs). Native bee data from the Simple landscape, and hoverfly abundances from both landscapes, were not included in these analyses, due to the very low number of individuals caught in these groups. I did not employ a model selection procedure to rank multiple

57 different models, because the small sample sizes limited the number of parameters that could be fit to each model and my goal was to identify the nature of the relationship with the focal vegetation types, rather than to search for potential relationships among a suite of predictors. Because some sites in the Simple landscape were located within small mallee vegetation patches inside almond plantations, I also examined whether the presence of these patches inside the plantation influenced the overall composition of pollinator assemblages in the Simple landscape. To do this, I used a bootstrapping procedure to compare the Shannon diversity indices of Hymenoptera and Diptera families between almond block sites and within-plantation mallee sites in the Simple landscape. GLMs were conducted in IBM SPSS 20 (IBM Corporation 2011) and all other analyses were conducted in PAST 2.17 (Hammer et al. 2001).

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Figure 4.2 Overall pollinator community composition in almond orchards located in Complex (black) and Simple (grey) landscapes within the study region. Different symbols denote individual orchards sampled within each landscape. Stress value for the non-metric multidimensional scaling (based on Bray-Curtis similarities) was 0.12, indicating the ordination was a suitable representation of community composition.

58 4.3 Results

4.3.1 Overall landscape and wild pollinator community patterns

The average proportion of almond trees or semi-natural grassland within 100 m of each site was similar in both Complex and Simple landscapes, but the average proportion of native mallee vegetation surrounding sites was higher in the Simple landscape (Table 4.1). Complex landscape sites were surrounded by a small proportion of other fruit trees and vineyards, but in the Simple landscape there were no other fruit trees and only one site had grape vines within 100 m (Table 4.1). Across the whole region, I caught a total of 3117 potential wild pollinator individuals, including 76 native bees, 126 wasps, and 2915 flies (Appendix A2.2). Average abundance and richness per site of every pollinator group except hoverflies was highest in the Complex landscape (Table 4.1).

4.3.2 Relationships between wild pollinator communities and site resource profiles

NMDS ordination of pollinator communities within each landscape showed that overall pollinator community composition at sites surrounded by ‘temporary’ floral resource profiles (crops only) was very similar to community composition at sites surrounded by ‘permanent’ resources (crops + natural vegetation) (Figure 4.3). Results of the NPMANOVA tests for differences in the composition of Hymenoptera and Diptera communities between the two resource profiles reflected the overall community similarity. In the Complex landscape, there were no significant differences in community composition between ‘temporary’ and ‘permanent’ sites for either taxonomic group: Diptera, Total Sum of Squares (SS) = 4.51, Within-group SS = 4.34, F = 1.00, p = 0.36; Hymenoptera, Total SS = 6.58, Within-group SS = 6.35, F = 0.93, p = 0.46 (Figure 4.4a). In the Simple landscape, there was no significant difference in the composition of Diptera communities between sites of each resource profile (Total SS = 4.61, Within- group SS = 4.50, F = 1.08, p = 0.36), but the composition of Hymenoptera communities showed a difference between groups of sites (Total SS = 15.43, Within-group SS = 14.55, F = 2.61, p = 0.05) (Figure 4.4b). 59 0.2 (a)

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Figure 4.3 Ordination of pollinator communities at almond sites within (a) Complex and (b) Simple landscapes based on the availability of temporary (perennial fruit crops only) or permanent (crops plus native vegetation and/or semi-natural grassland) resources within 100 m of the site. Stress values for the ordination are Complex = 0.08, Simple = 0.14.

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Figure 4.4. (a) In the Complex landscape, community composition of Hymenoptera (top) and Diptera (bottom) groups was similar between sites surrounded by temporary floral resources and sites surrounded by permanent resources.

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Figure 4.4. (b) In the Simple landscape, only Diptera (bottom) community composition was similar between sites with permanent and temporary resources.

62 4.3.3 Relationships between wild pollinator groups and proximate unmanaged vegetation

Richness and abundance of individual pollinator groups in each landscape showed contrasting relationships with the proportion of the two focal vegetation types, native vegetation or semi-natural grassland, around each site (Table 4.2). In the Complex landscape, none of the relationships were significant and most pollinator groups were negatively associated with both vegetation types. The exceptions were large fly abundance, which had a weak positive relationship with both vegetation types, and small wasp abundance, which had a weak positive relationship with semi-natural grassland. In the Simple landscape, small wasp abundance and richness showed a significant negative association with the proportion of native vegetation near sites and small wasp abundance and fly richness were negatively associated with the proportion of grassland around sites. Richness and abundance of all other groups were positively associated with native vegetation and grassland, but the only significant relationships were between fly richness and native vegetation and large wasp abundance and grassland. In the Simple landscape, 88% of hoverflies (7 out of 8 individuals) and all native bees were collected within mallee patches inside almond orchards, rather than within blocks of almond trees (Figure 4.5). Diptera family diversity was higher in the mallee patches inside almond orchards (H = 2.25) than in almond block sites (H = 1.99; p = 0.02) but there was no difference in Hymenoptera family diversity between the two site types (mallee H = 1.47, almond H = 1.67, p = 0.37).

63 Table 4.2 Relationships between pollinator groups and the proportion of unmanaged vegetation within 100 m of each site (parameter estimates with standard error in parentheses). Chi-square goodness-of-fit test ( χ2) indicates whether the model is a better fit to the data than the constant-only model. Landscape Response NV χ2 GS χ2 Complex A Bee -0.34 (0.53) 0.53 -0.15 (0.47) 0.11 Lge wasp -2.84 (2.23) 2.71 -0.23 (1.10) 0.04 Sml wasp -1.38 (1.13) 1.94 0.03 (0.89) 0.001 Lge fly 0.01 (0.01) 0.13 0.02 (0.01) 1.65 Sml fly -0.13 (0.10) 2.38 -0.02 (0.07) 0.07 R Bee -0.72 (1.29) 0.37 -1.22 (1.59) 0.71 Wasp -0.53 (0.58) 1.15 -1.57 (1.20) 2.96* Fly -0.15 (0.26) 0.37 -0.46 (0.52) 1.04 Simple A Lge wasp 1.10 (1.50) 0.57 7.50 (3.97)* 3.78** Sml wasp -1.86 (1.02)* 3.63** -5.97 (4.37) 2.50 Lge fly 0.10 (0.44) 0.05 1.53 (1.15) 1.90 Sml fly 0.63 (0.40) 2.42 0.99 (1.20) 0.68 R Wasp 0.10 (0.71) 0.02 0.21 (2.25) 0.01 Fly 0.82 (0.42)** 3.58* -1.06 (1.94) 0.29 *p < 0.09; **p = 0.05 NV = native vegetation; GS = semi-natural grassland

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Proportional abundance Proportional 0.1 0 Bee Large Wasp Small Wasp Hoverfly Small Fly Large Fly

Almond Block Mallee Patch

Figure 4.5 Abundance of each pollinator group in Simple landscape almond blocks (grey) and native mallee vegetation patches inside orchards (black), as a proportion of the total abundance collected across all sites in the Simple landscape (n = 45). 64 4.4 Discussion

All pollinator groups, except hoverflies, had higher average abundance and richness in Complex landscape orchards than Simple landscape orchards. This result was likely influenced by the different orchard floor management practices in orchards of each landscape type (Chapter 3) as well as the overall structural differences between the two landscape types. I was not able to separate the relative influence of these factors on the pattern seen here; however, weedy ground cover in crop fields and orchards is often considered to be an important component of landscape complexity (Roschewitz et al. 2005; Pal et al. 2013; Paredes et al. 2013). Similar differences in pollinator assemblages have been found in other studies comparing pollinators between simple and complex agricultural landscapes (e.g. Steffan-Dewenter et al. 2001; Haenke et al. 2009; Concepción et al. 2012; Persson & Smith 2013) and further sampling across my study region would enable a more robust comparison between these two landscape types.

4.4.1 Complex landscape

None of the potential wild pollinator groups in Complex landscape almond orchards showed significant relationships with the vegetation types surrounding sites. This could have been a result of the small sample size, or it could indicate that environmental factors other than these attributes were having greater influence over pollinator communities. Large flies were the only pollinator group that were more abundant at sites located near native vegetation or semi-natural grassland (Figure 4.4a), although this result was largely driven by the extremely high abundance of the most common fly family, Tachinidae, at a few sites that were surrounded by more established grassy/weedy ground cover compared to other sites. This concurs with other studies that have found a strong association between increased tachinid abundance and perennial or weedy vegetation cover in farmland (e.g. Abate 1991; Letourneau et al. 2012). Examination of the raw data showed that abundance and richness of bees and wasps were generally higher at sites that were only surrounded by temporary resources, i.e. almond, citrus, avocado trees and table grape vines. This reflected the negative relationships I 65 identified between individual pollinator groups and the proportion of unmanaged habitats (native vegetation and semi-natural grassland) surrounding sites. Some of the other fruit crops were flowering during sampling (although not as abundantly as almond trees), but diverse native floral resources were also available in native vegetation. These results suggest that hymenopteran pollinators may have been more attracted to the abundant and diverse floral resources available in the Complex landscape farrmland, compared to the patchy flowers available in mallee vegetation, and other studies have also found bee communities to have a stronger positive association with flowering crop landscapes than with nearby natural or semi-natural vegetation (e.g. Westphal et al. 2003; Winfree et al. 2008). Most fruit crops flower in late winter-early spring when wild pollinator species are still emerging or in their reproductive stage, so agricultural landscapes dominated by these mass-flowering crop varieties may have a greater opportunity to support the resource needs of pollinators during this time (Westphal et al. 2009; Jauker et al. 2012). However, functional connectivity of complementary resources within the matrix around crop fields is also necessary to sustain beneficial insect communities in the long-term (Jules & Shahani 2003; Williams & Kremen 2007) and further sampling of sites in the Complex landscape across larger spatial and temporal scales would provide more information on how wild pollinators use the heterogeneous landscape around almond orchards after almond trees cease flowering.

4.4.2 Simple landscape

In the Simple landscape, most pollinator groups showed positive relationships with the two unmanaged vegetation types, native mallee vegetation or semi- natural grassland, surrounding sites. Small wasp abundance had a negative relationship with both these vegetation types, but both unmanaged vegetation types were negatively correlated with the proportion of almond trees around sites in the Simple landscape (almond/mallee, r = -0.95, p < 0.001; almond/grassland, r = -0.55, p < 0.001), so the negative relationship seen here was most likely a reflection of the high small wasp abundances collected at interior almond sites more than 100 m from unmanaged vegetation. This result is surprising, due to the very low plant diversity and structural complexity throughout orchards, which 66 wasps are often strongly associated with (Stephens et al. 2006; Arnan et al. 2011; Batista Matos et al. 2013). My results could have been influenced by placing pan traps at ground level, as samples could have been dominated by wasp species that inhabit leaf litter around tree bases, many of which are small parasitic species (Austin et al. 2005; Pucci 2008); however, the most common parasitic family groups I collected (e.g. Diapriidae, Pteromalidae) were more abundant inside almond orchards than in the within-orchard mallee patches, despite identical sampling protocols, indicating that these species might prefer the resources available in almond blocks. These species are unlikely to be useful for almond blossom pollination, due to their small body size, but they could be important post-pollination control agents for almond pests (Freeman Long et al. 1998; Eilers & Klein 2009) and there is scope for further investigation into the distribution of these wasps in Australian orchards. The relationship between large wasp abundance and the proportion of semi-natural grassland around sites was the strongest significant relationship identified in the Simple landscape. Some large wasp species may use grassland areas for nesting, and other studies have also found positive relationships between wasp assemblages and areas of grassland (e.g. Krewenka et al. 2011). However, in my study the majority of sites within 100 m of grassland were also sites located in remnant mallee patches within plantations and more sampling is necessary to isolate the relative influence of these two vegetation types. Interestingly, all bee individuals and all but one hoverfly individual caught in the Simple landscape were collected within the mallee patches inside orchard boundaries, rather than in almond blocks themselves. This shows that these key wild pollinator taxa are present in the landscape, but environmental factors within the plantations could be limiting their dispersal into the homogeneous interiors. It is possible that catches of potential pollinators within orchards were low because the abundant floral resources diminished the effectiveness of the pan traps (Baum & Wallen 2011); however, the sampling method used in the Simple landscape was identical to that used in the Complex landscapes, where numerous individuals were caught within almond blocks, so the low pollinator abundance inside monoculture plantations was more likely to be influenced by habitat attributes common to these plantations. Although some studies have found pollinator abundance to increase in mass-flowering monoculture crop fields, most of these 67 study systems were ground-level or mid-height crops like canola or arable crops/pasture (e.g. Westphal et al. 2003; Jauker et al. 2009; Holzschuh et al. 2013). In contrast, studies of wild pollinators in intensively-managed tree crops have found lower pollinator abundance or richness in homogeneous orchard interiors (e.g. Klein et al. 2012; Marini et al. 2012). Many pollinator species often prefer to forage in open areas dominated by early successional vegetation (Kremen et al. 2007; Robinson & Lundholm 2012) and this could explain why pollinators respond positively to field crop monocultures and negatively to tree monocultures. Hoverflies and wild bees can also show strong positive relationships with natural or unmanaged vegetation in intensively-managed monoculture agriculture landscapes because resource richness is relative to the local context; therefore, natural vegetation in homogeneous landscapes functions as resource-rich patches that provide a more obvious contrast with adjacent broadacre monoculture crop fields than they would with the crop diversity present in heterogeneous landscapes (Kohler et al. 2008; Haenke et al. 2009). In particular, Kohler et al. (2008) found that wild bees may not move more than 25- 50 m from favourable habitat patches if the surrounding matrix does not provide necessary resources. Hence, the establishment of natural vegetation ‘reservoirs’ within farm boundaries to support pollinator communities (Brosi et al. 2008) may not have much success in increasing the provision of essential unmanaged pollination services if the farm itself remains an inhospitable environment to pollinators.

4.4.3 Conclusions and management implications

The results presented here suggest that more intensive management across agricultural landscapes increases the need for unmanaged habitats providing resource refuges for potential wild pollinators and other beneficial insects. This study was limited by the number of suitable replicate orchards available in the study region, and by the short sampling time, but the findings agree with other studies that have found patch-scale attributes, like farm management intensity and proximity of suitable resources, influence within-field pollinator communities with greater magnitude in homogeneous landscapes compared to complex, heterogeneous landscapes (e.g. Kleijn & van Langevelde 2006; Kohler et al. 2008; 68 Rundlöf et al. 2008; Persson & Smith 2013). Similar studies of tree fruit crops have found that generalist wild pollinators are often frequent visitors to orchards in the United States and Europe, but their presence is mostly limited to parts of the orchard with proximity to natural or semi-natural habitat patches that can sustain insect resource needs before and after orchard trees are in flower (Watson et al. 2011; Klein et al. 2012; Marini et al. 2012). Thus, agricultural landscapes dominated by tree crop plantations have the potential to support diverse wild pollinator assemblages, but less intensive management practices adopted at the landscape or regional scale are necessary to provide permanent spatial and temporary resources that are essential for wild pollinator communities to persist (Williams & Kremen 2007; Holzschuh et al. 2008). These findings have implications for conservation and agricultural production. Irrigated agricultural areas in arid landscapes, like the almond plantations in my study system, have the ability to support diverse wild insect communities, potentially providing essential resources during extended dry periods. However, intensive management of these areas reduces their conservation potential, as many wild pollinators and other beneficial insects may be unable to persist inside monoculture fields and orchards. This could affect the long-term composition of local insect assemblages and, therefore, the availability of insects that provide essential pollination services to pollinator-dependent crops and native flowering plants. Functional diversity has more influence over community assembly and ecosystem functions than abundance or richness of a particular species group (Mayfield et al. 2010; Cadotte et al. 2011; Lavorel et al. 2011); hence, studies like mine that focus on functional groups of beneficial insects in broadacre monoculture landscapes, rather than only commonly-recognised taxa like wild bees, are necessary to identify how structural homogeneity in agroecosystems can influence the provision of ecosystem services.

69 70 Chapter 5: The influence of a semi-arid woodland —almond plantation edge on wild pollinator communities

Abstract

Agricultural landscape elements, such as field edges, are not always a barrier to insect movement but can influence their distribution and dispersal behaviour. I investigated spatial and temporal patterns in wild pollinator (fly, wasp and native bee) distribution across an ecotone between natural mallee woodland and monoculture almond plantations in southern Australia, during the critical almond flowering period. This is the first study of variation in pollinator community distribution on both sides of the habitat boundary between natural woodlands and flowering tree crop plantations. I also use SHE analysis (species richness, diversity and evenness) to identify how the edge influenced pollinator community structure across the flowering season. I show that the spatial distribution and structure of pollinator communities can vary across a habitat boundary with temporal changes in resources. The physical habitat boundary was not a barrier to wild pollinators, but appeared to be influencing their movement into plantation interiors. Results suggest that pollinator communities were more likely influenced by vegetation heterogeneity and phenological changes in resources, than by the physical presence of the edge. My findings contribute to knowledge of edge responses by insects in managed landscapes and may also encourage the adoption of ecological management practices in commercial plantations of pollinator- dependent crops.

*This chapter has been published as Saunders ME, Luck GW ( 2014 ) Spatial and temporal variation in pollinator community structure relative to a woodland — almond plantation boundary. Agricultural and Forest Entomology , doi: 10.1111/afe.12067.

71 5.1 Introduction

Wild insect pollinators are keystone organisms and, globally, are being increasingly affected by the intensification of agriculture and the modification of natural habitats and landscapes (Winfree et al. 2011; Wratten et al. 2012). Landscape structure strongly moderates biodiversity patterns and ecological processes (Tscharntke et al. 2012) and boundaries between farmland and natural vegetation can be dynamic ecosystems themselves, influencing insect populations and trophic interactions across time and space (Hunter 2002). The terms ‘boundary’, ‘ecotone’, and ‘edge’ may be defined in different ways (van der Maarel 1990; Cadenasso et al. 2003; Yarrow & Marín 2007); in this paper, I use the terms ‘boundary’ or ‘edge’ to refer to a physical point of change between two adjacent vegetation types, and the term ‘ecotone’ to refer to a diffuse zone surrounding the boundary on both sides, where the potential for ecological influence from edge processes exists. Anthropogenically-created edges can have different dynamics to natural edges – they have a sharp gradient, little or no transition zone between the plant communities sharing the edge (Farina 1998), and are regularly influenced by human activities (Dutoit et al. 2007; José-María et al. 2010). These types of edges are characteristic of agroecosystems, where each field, paddock or plantation is often delineated by fence lines or roadways, and they are becoming more common worldwide as intensively cultivated landscapes expand (Blitzer et al. 2012). Therefore, understanding how wild pollinators are influenced by physical boundaries in agroecosystems contributes valuable information to pollinator conservation programs and the ecological management of agroecosystems. Although anthropogenic edges in agroecosystems can affect the movement of birds and mammals (e.g. Yahner 1988; Luck et al. 1999; King et al. 2011), they may not always be a physical barrier to insect movement (Forman & Godron 1986). However, they can still affect insect life cycles, ecological interactions, and, ultimately, the distribution and dispersal of insect communities across the landscape (Fagan et al. 1999; Haslett 2001; Hunter 2002). Many studies that have investigated wild pollinators near natural/agricultural boundaries have focused on pollinator communities within particular crops, and therefore have only sampled on the farmland side of the boundary (e.g. Chacoff & Aizen 2006; Jauker et al. 72 2009; Carvalheiro et al. 2010). However, the ecological effects of edges are not one-sided and, because ecological processes in the surrounding matrix can influence edge responses in conjunction with processes occurring in the focal patch, ‘two -sided’ sampling provides a more rigorous estimate of how an edge might influence adjacent ecosystems (Ries et al. 2004; Ewers & Didham 2006; Fonseca & Joner 2007). Many insects found in farmland will travel there, often temporarily, from nearby native vegetation (Blitzer et al. 2012) and, as in some natural ecosystems, may rarely be confined to one side of a landscape boundary (Dangerfield et al. 2003). More studies that specifically quantify insect abundance or diversity on both sides of farmland/natural vegetation edges will improve understanding of how these edge types influence insect communities ( sensu Forister 2009; Campbell et al. 2011; Lucey & Hill 2012). Empirical studies of natural edges suggest that a species' edge response is often related to the specific ecological and landscape context (Campbell et al. 2011; Macfadyen & Muller 2013). Insect movements at boundaries may be influenced by patch size or shape (With & Pavuk 2011), boundary permeability or complexity (Haslett 2001; Stasek et al. 2008), or an interaction between these variables (Collinge & Palmer 2002), and different insect taxa can show contrasting responses to the same edge (Olson & Andow 2008; Pryke & Samways 2012). For example, Jauker et al. (2009) found that wild bee abundance declined while hoverfly abundance increased along field margins as the distance to natural habitat increased, because of the differences in dispersal behaviour and resource requirements between the two taxa. Individual species responses ultimately affect community composition at the edge, although very few studies have considered community-level responses to habitat edges (Ries et al. 2004; Pryke & Samways 2012). Spatial patterns are often linked to temporal variation within the system, although these relationships are also contextual (Preston 1960; MacArthur & Wilson 1963; Adler & Lauenroth 2003; Soininen 2010). The interaction between spatial and temporal variation of species distributions is especially pertinent when considering ecosystem service provision in agroecosystems (Kremen 2005; Bianchi et al. 2010; Holland et al. 2011), where temporal changes in relevant farmland attributes, such as floral resources, often happen abruptly and with greater magnitude than in natural ecosystems. For example, floral resources in a 73 large monoculture crop field will be spatially homogeneous, available only for a very short time period, and saturate a large area of land, compared to a natural ecosystem that may support diverse and patchy floral resources throughout the year with constant spatial variation of flower location. Transient, large-scale mass-flowering events common to farmland have the potential to lure wild pollinators away from natural habitats (the ‘Circe Princ iple’ , Bartomeus & Winfree 2011; Lander et al. 2011) and this can have negative impacts on native plant pollination as well as insect reproduction, especially for those species that rely on permanent, abundant floral resources (Westphal et al. 2009; Holzschuh et al. 2011). Thus, examining the distribution patterns of ecosystem service providers (e.g. pollinators) in agroecosystems will benefit from considering both spatial and temporal variation of species patterns, especially in relation to the relevant life-stage of the crop (e.g. flowering period; sensu Mesa et al. 2013; Peters et al. 2013). Here, I investigate spatial and temporal patterns of wild pollinator abundance and diversity across an ecotone between two vegetation types in southern Australia: natural mallee woodland and monoculture almond plantations (Prunus dulcis , Mill.). Almond is a 100 % pollinator-dependent crop, yet there is currently no available information on potential wild pollinators available to almond crops in Australia (but see Saunders et al. 2013). I did not quantify pollination efficiency in this study, but I use the term 'pollinator' to refer to insects in the orders Hymenoptera and Diptera to maintain consistency with the pollination literature. Wasps and flies are important but understudied pollinators in a number of natural and agricultural systems (Kevan & Baker 1983; Larson et al. 2001; Ssymank et al. 2008), particularly in Australia (Armstrong 1979; Pickering & Stock 2003) and individuals of these orders were seen visiting flowers during sampling. Because of the limited information on pollinator taxa in my study system, I focus on documenting spatial and temporal patterns to initiate future research and hypothesis generation (e.g. exploring the drivers behind these patterns), as these patterns are fundamental to understanding ecological processes and ecosystem function (Turner 1989; Levin 1992). I also use SHE analysis (Buzas & Hayek 1996) to examine how the habitat edge influenced pollinator communities by identifying changes in community structure along the physical

74 woodland —plantation gradient and comparing these taxonomic boundaries with the location of the habitat boundary.

5.2 Methods

5.2.1 Study area and sampling

The study area is located in the Victorian Murray Mallee Bioregion, in the neighbouring districts of Wemen and Liparoo, northwest Victoria, Australia (34° S 142° E). The Murray Mallee Bioregion is semi-arid and average annual rainfall in the study area is approximately 315 mm (Bureau of Meteorology 2013). Vegetation is dominated by Eucalyptus, Acacia , Callitris and Casuarina short trees (approx. 3-12 m) and tall shrubs, while the understorey is highly variable, with patchy ground cover vegetation typically including hummocks of Triodia , halophytic plants, and a range of herbs and annual species (Wood 1929; Cheal & Parkes 1989). I sampled within two conventional monoculture almond plantations (Liparoo, Wemen) and two mallee woodlands. To minimise the effects of different edge orientations or contrasts (Ries et al. 2004) I chose plantations in the same geographical region with large patches of adjacent native mallee vegetation that had similar orientation and structure (Figure 5.1). Both mallee —almond boundaries were aligned east-west, with mallee vegetation on the southern side of the edge and almond plantation on the northern side, and both were delineated by a fence line and a two lane road approximately 5 m wide (an unpaved farm road at Liparoo and a paved municipal road at Wemen). Liparoo is adjacent to the Annuello Flora and Fauna Reserve (hereafter Annuello) and Wemen is adjacent to an unnamed patch of remnant mallee vegetation (hereafter Collins). Both plantations are broadacre monocultures, intensively managed by the same company, using the same practices. The linear distance between the two plantations is approximately 7.5 km. Plantations differed in their age and size, and mallee patches differed in their size and management history (Appendix 4 Table A4.1).

75 Figure 5.1 The study area in northwest Victoria, Australia, showing the sampled mallee/almond edges at Liparoo (left) and Wemen (right) plantations – mallee vegetation patches are on the south side of each edge and almond plantations are on the north side of the edge.

Insects were collected on three occasions in 2011, coinciding with the flowering of the almond crop in late winter – before (July), during (August) and after (September) almond flowering, which lasts approximately 3-4 weeks. July is typically the coldest month of the year in my study region, and deciduous almond trees were completely bare at this time (Figure 5.2). Almond flowering occurred across the entire plantation in August, with each tree still predominantly leafless, but completely covered in blossom (approx. 50,000 individual flowers per tree). In September, the first month of spring, the majority of almond blossoms had dropped and trees were fully leaved. In mallee woodlands, phenological changes were much less obvious, with evergreen plants and trees and diverse, patchy floral resources available in all months (Figure 5.2). Hence, I present a novel assessment of temporal changes in pollinator communities across a mallee —almond ecotone, in the context of a very brief, but substantial, resource pulse with the potential to

76 influence pollinator distribution, i.e. landscape-scale mass-flowering of monoculture almond plantations.

Figure 5.2 Representative photographs of (top left) almond plantations in July; (top right) plantations in August; (bottom left) plantations in September; (bottom right) mallee woodlands.

Ries et al. (2004) suggested that the depth of an edge's influence on invertebrates can be up to 100 m; hence, we considered 300 m from the edge to be an acceptable distance for interior sites, given average pollinator foraging ranges, which can be highly variable among taxa and under different environmental conditions (Nielsen et al. 2012; Essenberg 2013; Jha & Kremen 2013). Non- Apis bees also exhibit small-scale responses to floral resources, especially when resources are at higher densities (Greenleaf et al. 2007; Jha & Vandermeer 2009; Essenberg 2013). To account for this, I sampled more sites in plantations than in mallee woodlands, because we predicted that changes in pollinator communities might occur more rapidly on the almond side of the edge. On each sampling occasion, I sampled 50 almond sites (25 in each plantation) and 18 mallee sites (nine in each mallee patch) along parallel transects perpendicular to the edge. The number and location of transects were chosen a priori from satellite maps and were limited by access and cadastral boundaries. Sites were located at 10 m (edge), 40 m (intermediate) and 300 m (interior) on both sides of the edge. On the plantation side of the edge only, I sampled additional intermediate distance classes 77 at 70 m and 100 m. Thus, each plantation transect consisted of five sites and each woodland transect consisted of three sites. All transects were at least 200 m apart and the transect was considered the replicate in time and space. Pan traps may underestimate some taxa, but I used this method to minimise collector bias that can arise from other direct sampling methods (Westphal et al. 2008; Popic et al. 2013) and designed bowl colour and placement to increase the likelihood that data would be ecologically meaningful (see Appendix 1). I used UV-bright yellow and white bowls, as white was the predominant flower colour in plantations and yellow the most common in woodlands. Because of the winter sampling time, traps at each site were open for 8-10 hours in each month to capture maximum insect activity during the warmest part of the day. Traps were placed on the ground next to a flowering tree for maximum visibility and were set just after sunrise and collected just before nightfall in the order they were placed. Insects were stored in 70% ethanol and transported to the laboratory, where they were counted and sorted into taxonomic order (Diptera (flies) and Hymenoptera (native bees and wasps)). I identified native bees to species (where possible, although most Lasioglossum individuals could only be classified to subgenus) and flies and wasps to family level using specialised identification keys (Hamilton et al. 2006; Stevens et al. 2007) along with a reference collection provided by a taxonomic expert (K. Walker, Museum Victoria).

5.2.2 Data analysis

I analysed differences in monthly pollinator abundance and richness between vegetation types, and compared how insect community structure changed relative to the edge. The insect abundance data were overdispersed ecological counts and were analysed untransformed, as recommended for these type of data (McArdle & Anderson 2004; O’Hara & Kotze 2010; Warton & Hui 2011). All statistical analyses were conducted in PAST 2.17 (Hammer et al. 2001) using raw abundance counts, a species richness index (hereafter simply 'richness'), or other diversity measures as described below. I calculated richness with the Menhinick index, which takes the number of individuals per species into account and is robust to unequal sample sizes (Menhinick 1964). Analyses on the native bee data 78 were conducted on September counts only as I caught zero individuals in July and 4 individuals in August. I first looked at overall distribution patterns of potential wild pollinators across the whole study area, regardless of month, and then examined spatial and temporal trends in more detail, specifically how monthly populations changed in each vegetation type and how spatial distribution changed across time. To examine overall distribution patterns relative to the sampled distance gradient, I compared community similarity between distance classes, as this approach is recommended for comparing site diversity relative to a distance gradient (Tuomisto & Ruokolainen 2006; Soininen 2010). I calculated average Bray-Curtis similarity coefficients for overall community similarity (total Diptera and total Hymenoptera) at each possible pair of distance classes, using all combinations of individual sites. I used linear regression (ordinary least squares) to test whether community similarity declined as geographic distance between distance classes increased along the transect (the 'distance-decay' phenomenon; Nekola & White 1999; Soininen 2010). The fit of the model was verified by examining residuals for autocorrelation and heteroscedasticity. I then tested whether heterogeneity of vegetation type contributed to heterogeneity of pollinator communities by grouping similarity coefficients based on the three possible combinations of vegetation pairs (mallee/mallee, almond/almond, and mallee/almond) and comparing the mean similarity between each group using a standard t-test. I then examined monthly turnover in abundance and richness of each pollinator group per site, relative to the distance gradient. Wilcoxon signed-rank tests were used to look for significant differences in fly and wasp richness and abundance between sequential months (July-August, August-September) at each distance class, within each vegetation type (native bees were not included in this analysis because nearly all individuals were caught in September). To identify whether the physical edge was influencing pollinator community composition, I separated data from each woodland/plantation pair (Annuello —Liparoo and Collins —Wemen) to account for differences in patch area (see Appendix 4 Table A4.1). I tested whether changes in Hymenoptera and Diptera community composition along each ecotone gradient coincided with the physical boundary between vegetation types using SHE analysis (Buzas & Hayek 1996; Hayek & Buzas 1997). Interior (300 m) mallee sites were designated as the 79 starting point of the ecotone, under the assumption that mallee woodland was the 'source' habitat for native insects in the study area, and I quantified the gradient as follows: -300 m, -40 m, -10 m, 10 m, 40 m, 70 m, 100 m, 300 m, with negative values indicating mallee woodland sites and positive values indicating almond plantation sites. I analysed bees and wasps separately because exploratory analysis showed that the presence of an extreme outlier at one distance class in the bee data skewed the analysis and masked the patterns in the wasp data set. SHE analysis examines the relationship between three core measures of biodiversity, species richness (S), diversity (H) and species evenness (E) as they accumulate through quadrats, transects or time. Mathematically, this technique is similar to other methods for analysing species richness, such as species accumulation curves (Buzas & Hayek 1996; Buzas & Hayek 1998); hence, it can also be applied to assemblages identified to higher taxonomic levels, e.g. genus or family (Gotelli & Colwell 2011). SHE analysis has two main applications: as an investigation of the diversity and underlying distribution of the sample population (Buzas & Hayek 1996; Magurran 2004); and as a robust measure for analysing zonation in community structure along a spatial or temporal gradient (Buzas & Hayek 1998; Osterman et al. 2002; Magurran 2004). SHE analysis has been used mainly in the analysis of marine foraminiferal assemblages and palaeo-communities (e.g. Buzas & Hayek 1998; Osterman et al. 2002), but has also been applied to a range of terrestrial systems (e.g. Small & McCarthy 2002; Leponce et al. 2004; Parra-H & Nates-Parra 2012). Because the analysis follows the sampled spatial or temporal gradient in a sequential fashion, rather than analysing pairwise comparisons between all pairs of samples, it is recommended for identifying boundaries within faunal communities over ordination or clustering techniques (Osterman et al. 2002), which can mask the data's true complexity, overlook important environmental or temporal relationships and confound dispersion and location effects in the data (Osterman et al. 2002; Warton 2008; Warton et al. 2012). SHE analysis assumes that a single 'closed-system' community has a log- series distribution (Buzas & Hayek 2005), for which a plot of lnE vs lnN should exhibit a decreasing linear trend (Buzas & Hayek 1998). Where the line departs from this trend, a statistically significant change in the faunal assemblage has occurred and the communities on either side of the break point can be assumed to 80 be separated in time or space, whichever is the focus of the sampled gradient (Buzas & Hayek 1998; Osterman et al. 2002; Wilson 2008). I calculated S, H and E for accumulated abundances (N) in each sequential sample along each ecotone gradient (600 m), and plotted lnE vs lnN for the entire transect (following Buzas & Hayek 1998). I examined the initial lnE vs lnN plot, and when a break in the linear trend occurred, the samples up to and including the break point were discarded and lnE vs lnN for the remaining samples was replotted. This process continued until all samples were accounted for. The resulting wasp, fly, and bee 'abundance zones' were then compared with the distance classes along the physical mallee —almond ecotone gradient.

5.3 Results

5.3.1 General patterns

I caught a total of 2394 fly individuals in 20 family groups, 336 wasp individuals in 23 families, and 171 non- Apis bee individuals in 2 genera ( Lasioglossum (Chilalictus ) and Homalictus (Hymenoptera: Halictidae)) across the whole sampling period (Appendix 2 Table A2.3). Per trap site, average bee abundance was highest at Annuello (mean 8 ± 13.3), average wasp abundance was highest at Collins (mean 8.4 ± 10.04) and average fly abundance was highest at Wemen (mean 21.42 ± 43.02). Average fly richness was highest at Annuello (mean 1.92 ± 0.73), and average wasp (mean 1 ± 0.54) and bee (mean 0.65 ± 0.5) richness were highest at Collins. Five Hymenoptera families (Halictidae, Ichneumonidae, Mymaridae, Pteromalidae, Scelionidae) and seven Diptera families (Calliphoridae, Chironomidae, Chloropidae, Dolichopodidae, Heleomyzidae, Phoridae, Tachinidae) were collected at every distance class along the ecotone (Appendix 4 Table A4.2).

5.3.2 Distance decay and monthly turnover

Pollinator community similarity between sites declined significantly as the distance between them increased, but the slope of the regression was very weak (Figure 5.3). Heterogeneity of vegetation types influenced community similarity 81 between sites. On average, almond/almond site pairs had higher community similarity (mean 0.75 ± 0.14) than either almond/mallee (mean 0.69 ± 0.18; F = 1.59, t = 7.25, p < 0.001) or mallee/mallee site pairs (mean 0.69 ± 0.17; F = 1.53, t = -3.45, p < 0.001). Similarity between mallee/mallee site pairs was not different to almond/mallee site pairs (F = 1.04, t = -0.25, p = 0.8) (Figure 5.4).

0.8

0.75

0.7

0.65 Curtis similarity - 0.6 Bray

0.55

r² = 0.3945, similarity = -0.0002(distance) + 0.7437 0.5 0 100 200 300 400 500 600 700

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Figure 5.3 Distance-decay graph showing the decline in pollinator community similarity with linear distance between sites along the 600 m mallee —almond ecotone.

Average bee and wasp abundance and richness increased gradually across the sampling period, except for wasp richness in mallee woodland, which declined slightly in August before another increase in September. Average fly abundance increased each month in almond plantations, but fly abundance in mallee and fly richness in both vegetation types peaked in August and declined again September. Fly and wasp richness and abundance along the mallee section of the ecotone did not change significantly between months (Figure 5.5). On the plantation side of the boundary, there were no significant increases in wasp richness and abundance from July-August at any distance class, but both richness and abundance increased

82 significantly from August to September at 10 m, 70 m and 300 m (Figure 5.5a). Fly abundance at all distance classes in almond and richness at 10 m, 100 m and 300 m increased significantly from July to August and then remained similar from August to September (Figure 5.5b).

5.3.3 Influence of the edge

The SHE analysis identified changes in community structure (community zones) relative to the physical distance gradient. At Collins —Wemen, there were three distinct community zones, while only two were apparent at Annuello-Liparoo (Table 5.1). Wasp and fly community composition at Collins —Wemen changed at the boundary in August and September, and then again around 100 m inside the plantation, while the native bee community changed on the mallee side of the boundary in September (between 40 and 10 m). In July, wasp community composition in the Collins (mallee) interior remained similar across the boundary and into Wemen (almond), changing between 70 and 100 m inside the plantation, and fly community boundaries were identified between 10 and 40 m and after 100 m on the almond side. At Annuello —Liparoo, wasp and fly communities also changed near the physical boundary in September, while native bee communities changed structure further inside the plantation (between 40 and 70 m). In July and August, wasp and fly community boundaries were identified on the almond side of the edge. Wasp zones changed between 70 and 100 m inside the plantation in both months, and fly zones changed around 40 m inside the plantation (Table 5.1).

83 1

b 0.9 a a

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0.6

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0 Mallee -Mallee Almond -Almond Almond -Mallee

Figure 5.4 Mean (± SD) pollinator community similarity (Bray-Curtis) between site pairs of each combination of vegetation type. Lower-case letters above bars indicate whether each group of site pairs was significantly different from other groups (p < 0.01).

84 (a) Jul 4 Aug

3.5 Sep * * 3

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Figure 5.5 (a) Monthly average wasp abundance and richness at each distance along the ecotone throughout the sampling period (July-September). Asterisks indicate a significant increase/decrease from the previous month (p < 0.05).

85 (b) Jul 30 Aug

Sep 25

* 20 *

15 * * * Average abundance Average 10

5

0

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0 -300 -40 -10 10 40 70 100 300 Distance along ecotone (m)

Figure 5.5 (b) Monthly average fly abundance and richness at each distance along the ecotone throughout the sampling period (July-September). Asterisks indicate a significant increase/decrease from the previous month (p < 0.05).

86 Table 5.1 Community zones identified from SHE analysis for wasp, fly and native bee communities, relative to the distance along the physical ecotone at each location (negative values indicate mallee sites and positive values indicate almond sites). Shading indicates the different community zones. Only September community zones are shown for native bees. Wasp Distance along ecotone gradient (m) Ann —Lip -300 -40 -10 10 40 70 100 300 July August September Coll —Wem July August September

Fly Distance along ecotone gradient (m) Ann —Lip -300 -40 -10 10 40 70 100 300 July August September Coll —Wem July August September

Distance along ecotone gradient (m) Native bee -300 -40 -10 10 40 70 100 300 Ann —Lip Coll —Wem Ann —Lip = ecotone between Annuello (mallee) and Liparoo (almond); Coll — Wem = ecotone between Collins (mallee) and Wemen (almond)

87 5.4 Discussion

5.4.1 Distance decay

Across the whole sampling period Hymenoptera were, on average, more abundant and more diverse in mallee woodland and Diptera were more abundant in almond plantations but more diverse in mallee woodland. There was an overall distance decay of pollinator community similarity between distance classes along the sampled distance gradient. Nekola & White (1999) suggest two possible causes for this phenomenon: a decrease in environmental/habitat similarity with distance, which influences niche availability; or changes in spatial configuration and landscape context across space and time, which affect species' dispersal. These factors are inherently linked and it can be difficult to separate the underlying causes of observed variation in a distance decay model (Nekola & White 1999). Mallee woodland is highly-complex and heterogeneous in structure compared to the monoculture plantations, where herbicides are used to maintain bare ground throughout and blocks of almond trees are nearly identical in row width and tree height. I found that the homogeneity in plantations was reflected in pollinator communities – similarity of insect communities between site pairs was highest among almond sites, while variation between pollinator communities within mallee vegetation was just as great as it was between the two different vegetation types. Habitat heterogeneity and complexity increase niche availability, and can also have a significant influence over invertebrate communities, trophic webs and overall ecosystem function (Srivastava 2006; Randlkofer 2010a), particularly in agroecosystems (Altieri 1999; Letourneau et al. 2011). Lower rates of distance decay, as seen in the regression analysis here, can occur because of minimal changes in the environment along the sampled gradient or because the focal taxa are highly mobile and less affected by landscape barriers such as edges (Nekola & White 1999). This latter explanation is most likely for my results, as vegetation structure and heterogeneity changed significantly along the ecotone and influenced community similarity of my focal taxa, highly-mobile insect pollinators. Further sampling at more distance classes along the ecotone, combined with a comparison of distance decay rates between insect groups,

88 vegetation types and months would yield more information on the significance of the distance-similarity relationship in this study system.

5.4.2 Temporal variation

Each pollinator group showed different rates of temporal turnover, reflecting the variation in resource needs of individual taxa. Wasps showed the weakest responses in my system, despite evidence that some wasps may be more sensitive to anthropogenic change than bees or other insects (Batista Matos et al. 2013). Wasp abundance and family richness increased steadily, and at a lower rate, in both vegetation types from July through to September, but were also consistently higher in woodlands than plantations. Fly family richness and abundance showed much stronger fluctuations throughout the sampling period, and flies were also more abundant overall in my study system. Dipteran species can be more cold- tolerant than hymenopterans and are often the most frequent flower-visitors in colder climate zones and higher latitudes (Elberling & Olesen 1999; Pickering & Stock 2003; Brown & McNeil 2009) so the low abundances of hymenopterans most likely reflects the season I sampled in. Native bees were absent in samples in July and rarely collected in August during almond flowering, even though sampling from 2010 showed that native bees are typically present in the study region during these months, including in plant-diverse almond orchards managed with lower intensity (Chapter 3). Many non- Apis bees need access to multiple heterogeneous habitats throughout the year (Winfree et al. 2011; Mandelik et al. 2012) so permanent spatial homogeneity and lack of floral diversity within perennial monocultures could prevent bees from establishing inside plantations, regardless of the resources provided by the short-term floral pulse, but more sampling is needed to verify this effect. Although pollinator abundance increased naturally across the sampling period in response to seasonal warming, I also found evidence that some insects may have been influenced by the phenological changes occurring in almond plantations. In July, mallee sites had higher fly and wasp abundance and richness than almond plantations, most likely because of greater availability and heterogeneity of resources in woodlands. Pollinator abundance was also higher in the mallee interior than the almond interior in July, despite the mallee having 89 higher structural density at the edge, an attribute that is considered to prevent movement of some insects into forest interiors (Cadenasso & Pickett 2000). These results suggest that consistent resource availability beyond an edge may override the qualities of the edge itself, especially for pollinators (Kohler et al. 2008; Stasek et al. 2008) or in agroecosystems with high temporal variability of resources (Rand et al. 2006). During the almond flowering event in August, fly abundance and richness increased significantly in plantations, and then remained at similar levels into September. In contrast, wasp abundance and richness did not increase significantly in the plantations until September – although there was a small increase in wasp populations in August. This could indicate a delayed response to the flowering event (Thomson 1981; Ries et al. 2004), which often occurs with short, substantial resource pulses (Ostfeld & Keesing 2000). Alternatively, wasp assemblages could have been influenced by the additional resources available in plantations after flowering. As well as the spent flowers, most trees were fully-leaved and nut formation had begun, and this coincided with an increase in herbivorous insects such as planthoppers, aphids (Hemiptera) and psocids (Psocoptera), which are potential prey or host species for many wasps and flies (see Chapter 6). Temporal increases in pollinator abundance and richness on the mallee side of the boundary were not significant, so the increases I observed at almond sites were more likely to be influenced by the simultaneous changes in resource availability, rather than seasonal changes in weather conditions. Although managed European honeybees were present in plantations during the August sampling event, it is unlikely that the increase in honeybee abundance significantly influenced my results, due to the abundant floral resources (e.g. Markwell et al. 1993). As discussed in Chapter 2, evidence suggests that honeybee and non- Apis pollinator insects can show spatial complementarity in foraging and nesting locations. In particular, a study in North American almond plantations found that wild pollinators and European honeybees partitioned themselves across different parts of the tree while foraging, rather than being deterred althogether (Brittain et al. 2013).

90 5.4.3 Influence of the edge

The results of the SHE analysis suggested that the physical presence of the edge was not a barrier for pollinators, but the spatial and temporal changes in vegetation structure and resources at the woodland/plantation boundary did have an influence on community structure. Pollinators are highly-mobile, but can also be sensitive to changes in habitat that do not provide the food or structural resources they need (Kremen et al. 2007; Kohler et al. 2008). Therefore, edge responses for these species can be highly-variable, related to contextual attributes of the studied habitat (Jauker et al. 2009; Azevedo et al. 2011; Coudrain et al. 2013; Macfadyen & Muller 2013) or specific to each taxa, with flies, bees and wasps all showing differing responses within a single ecological context (Klein et al. 2004; Jauker et al. 2009; Rader et al. 2011; Batista Matos et al. 2013). I identified distinct community zones (i.e. significant changes in community structure or composition) along the mallee/almond gradients, and the boundaries between these zones shifted across the sampling season and also differed between plantations. Most importantly, these boundaries did not always reflect the location of the woodland/plantation edge. Two distinct community zones were identified along the mallee —almond ecotone between Annuello woodland and Liparoo plantation. All changes in community structure of each pollinator group occurred between the physical edge and 100 m inside the plantation (Table 5.1), suggesting that this could be the maximum distance from the source habitat (mallee) that many pollinators would travel. For example, the wasp families Ichneumonidae and Tiphiidae, both common flower-visitors (Kevan 1973) and important pollinators for some Australian native flowers (Armstrong 1979; Peakall & Handel 1993), were only found up to 70 m inside the plantation (Appendix 4 Table A4.2). In addition, although both bee species collected at Annuello and Liparoo were found across the whole ecotone, 83 % of the total individuals were collected at mallee and almond edge (10 m) sites and almond intermediate (40 m) sites. This suggests that pollinators living in Annuello were utilising resources in plantations throughout the flowering season, but environmental factors were inhibiting their movement beyond 100 m. This hypot hesis agrees with Ries et al.’s (2004) finding that invertebrate responses to edges generally extend up to 100 m from the actual edge, as well as established knowledge that pollinators, 91 particularly bees, require access to multiple different resources to complete their life cycle (Kremen et al. 2007; Mandelik et al. 2012). In contrast to the Annuello —Liparoo ecotone, three distinct community zones were identified along the Collins —Wemen gradient. All groups, except July wasp communities, changed structure on the mallee side of the boundary, between 40 m and the edge, and then again between 70 and 300 m inside the plantation. This could indicate that some pollinators were congregating near to the edge, creating a community structure distinct from the interior sites, but this was not the case with all taxa (Appendix 4 Table A4.2). For example, the bee species L. (Chilalictus) erythrurum was found at 100 m inside Wemen plantation and this species was not collected at all along the Annuello —Liparoo ecotone. In addition, 55 % of bee individuals collected at Collins —Wemen were found between 70 and 300 m inside the almond plantation. Because the two mallee/almond edges were similar in their orientation, geographic location, edge contrast and general vegetation structure, these results suggest that other ecological factors within the plantation are influencing pollinator movement into plantation interiors. Both Wemen and Liparoo plantations have a high proportion of native vegetation in the surrounding landscape (mallee woodland along the studied edge and floodplain red gum ( Eucalyptus camaldulensis ) forests bordering the other side of the plantation); however, Wemen is much smaller in area than Liparoo (Appendix 4 Table A4.1) and the greatest distance from an edge to plantation interior is approximately 600m, compared to about 1.5 km at Liparoo. It is possible that the smaller plantation patch size at Wemen contributed to the increased abundances and enhanced community structure I saw here; however, more detailed sampling would be necessary to isolate the effects of ‘edge’ and ‘area’ in this system (Fletcher et al. 2007).

5.4.4 Conclusions

In my study system, I found that the woodland/plantation boundary was not a barrier to wild pollinators, and spatiotemporal changes in resources and vegetation heterogeneity across the ecotone were more likely influencing pollinator distribution patterns and movement into the interior of the plantation. In particular, I found that pollinator community similarity between sites was 92 associated with the relative level of heterogeneity between vegetation types, and that each insect group's response to the edge and overall distribution patterns varied across time – before, during and after the mass-flowering event in almond plantations. My results concur with studies of similar focus that have found greater influence from context-specific environmental variables (e.g. light availability, canopy or shrub cover) (Yu et al. 2007; Muff et al. 2009) than distance from the edge alone, and this interaction may be more pertinent when considering highly-mobile insects like pollinators (Haslett 2001). The significance of my findings would be augmented by more intense sampling, beyond the 300 m distance limit used here and across other woodland/plantation boundaries, as well as further consideration of changes in environmental variables relative to the edge and surrounding landscape. Nonetheless, my results provide valuable information for the ecological management of conventional almond plantations, as well as pollinator conservation in agroecosystems generally, and also contribute to knowledge of how edges influence beneficial insects in managed landscapes. I showed that the spatial distribution and structure of potential almond pollinator communities can vary in response to spatiotemporal changes in resources, which is critical information for developing effective pollinator conservation initiatives in agroecosystems centred on pollinator-dependent crops. Future studies of how habitat boundaries in agroecosystems influence important insect taxa should survey both sides of the boundary and consider the complex interactions of physical landscape configuration, spatial and temporal variation in resource availability, and the unique responses of specific insect groups to these elements.

93 94 Chapter 6: Food and habitat resources available to wild pollinators in a winter-flowering crop

Abstract

Homogeneous tree crop plantations can adversely impact wild pollinator communities by limiting temporal continuity of food and the availability of nesting sites. Identifying structural differences between plantations and adjacent natural vegetation, and how these differences influence pollinator communities, can inform ecological management goals. Communities of potential wild pollinators (native bees, wasps, flies) were compared between monoculture almond plantations and native mallee vegetation in a semi-arid region of temperate Australia, before, during and after the late winter almond flowering period. I examined whether abundance and richness of each insect group were related to overall site heterogeneity or individual structural attributes of vegetation at each site, focusing on food and nesting resources. Relationships with site heterogeneity varied temporally, and between taxa, before, during and after almond flowering. Insect response variables were often influenced by particular habitat attributes when they were not associated with overall heterogeneity. Fly and wasp richness and wasp abundance were strongly associated with leaf litter and ground cover vegetation before almond flowering, regardless of vegetation type. After flowering ceased, native bee abundance had a strong negative relationship with canopy cover and leaf litter. This study provides novel insights into an understudied ecosystem and highlights the value in focusing on specific resources when investigating relationships between insects and habitat structure, rather than generalised ‘heterogeneity’ metrics. In particular, mainta ining keystone resources in agroecosystems can enhance the conservation of insects and ecosystem services, especially in areas where agricultural systems overlap dynamic natural ecosystems.

*This chapter has been published as Saunders ME, Luck GW, Gurr GG ( in press ) Keystone resources available to wild pollinators in a winter-flowering tree crop plantation. Agricultural and Forest Entomology .

95 6.1 Introduction

Heterogeneity in vegetation structure is essential for supporting biodiversity and ecosystem functions at multiple scales (e.g. Srivastava 2006; Diacon-Bolli et al. 2012), especially in agricultural systems (Altieri 1999; Benton et al. 2003; Letourneau et al. 2011). At small scales, variation in vegetation structure and resource availability can influence population dynamics and distribution patterns of invertebrates, including ecosystem service providers like pollinators (Williams & Kremen 2007; Murray et al. 2012) and natural enemies (Landis et al. 2000; Gámez-Virués et al. 2010). Maintaining pollination and pest control ecosystem services is vital for minimising ‘yield gaps’ in agricultural production (Bommarco et al. 2013). Yet, modern agroecosystems are dominated by large patches of homogeneous vegetation (e.g. broadacre crop monocultures) that can disrupt the provision of these services (Tscharntke et al. 2005) and negatively impact local wildlife populations (Krebs et al. 1999; Tilman 1999; Stoate et al. 2009). The provision of ecosystem services in agroecosystems depends on complex trophic interactions between multiple individuals, species and communities (Vandermeer et al. 2010; Lundin et al. 2013) and vegetation homogeneity can disrupt these interactions in agroecosystems. A number of studies have found that conversion of heterogeneous landscapes to monoculture crop systems has been linked to increased pests and diseases and subsequent declines in crop yield (Marshall & Foot 1979; Altieri & Nicholls 2002; Aragona & Orr 2011; Bennett et al. 2012). Wild pollinators are particularly vulnerable to intensive management, as they require access to multiple heterogeneous resources throughout the year and prefer to forage in plant-diverse systems (Williams & Kremen 2007; Winfree et al . 2011). Numerous studies have shown that pollinator richness and abundance generally increases with greater plant diversity or structural complexity in managed crop systems (Nicholls & Altieri 2013), but relationships with these factors can vary between taxa (Steffan-Dewenter et al. 2002; Holzschuh et al. 2010; also Chapter 3) and across different spatial and temporal scales (Kleijn & van Langevelde 2006; Vasseur et al. 2013). Temporal variation in resources is particularly relevant to studies of pollination services, as many pollinator- dependent crops grown in monocultures exhibit a brief, highly-concentrated resource pulse during flowering, yet provide very few resources for pollinators for 96 the remainder of the year. This phenological pattern may provide temporary benefits for pollinator insects (Bartomeus & Winfree 2011; Lander et al. 2011), but can have longer-term negative consequences for nearby native plant pollination (Holzschuh et al. 2011) and insect reproduction (Westphal et al. 2009), especially for species that emerge after the crop has ceased flowering (Jauker et al. 2012). Perennial tree crop plantations (e.g. orchard fruits) are an interesting study system, as they are usually managed as monocultures across large areas, but are more permanent in the landscape compared to annual grain or vegetable crop systems (Hartemink 2005). Because of this, tree crops may have underestimated potential for wildlife conservation, as long as production goals are balanced by ecological management objectives (Hartley 2002; Barlow et al. 2007; Quine & Humphrey 2010; Tscheulin et al. 2011). Many economically-valuable tree crops are also pollinator-dependent (Cunningham et al . 2002; Losey & Vaughan 2006; Calderone 2012), so knowledge of relationships between wild pollinators and vegetation structure within these systems will aid in prioritising conservation and management needs. The ability of tree crop plantations to support wild pollinator communities is particularly important for pollinator-dependent orchard fruits that flower in late winter, as most orchard fruits do. Pollinator activity can be limited at this time, affecting fruit set of pollinator-dependent crops (Stoddard & Bond 1987; Bosch & Kemp 2002), yet few studies have investigated the capacity of these systems to provide food or nesting resources that support overwintering and emerging wild pollinator species. Here, I sampled potential wild pollinator insects in monoculture almond plantations and adjacent native vegetation in northwest Victoria, Australia, during the almond flowering period. Almond ( Prunus dulcis , Mill.) is the biggest contributor to Australia’s total annual nut production, and production is centred around the semi-arid mallee regions of northwest Victoria and eastern South Australia (Almond Board of Australia 2013), which include areas of high conservation value (Mallee Catchment Management Authority 2008; Cheal et al. 2013). Almond flowers require cross-pollination to set fruit and, because trees flower in late winter when insect activity is limited, growers rely on imported managed honeybee hives to provide pollination services and information on potential wild pollinators available in Australia’s growing regions is limited. In 97 general, research on invertebrate ecology in the Victorian mallee is dominated by studies of ants and information on the ecology of other species is limited; however, the region is considered to have high invertebrate species richness, particularly hymenopterans, and high phenological diversity in invertebrate activity patterns, sometimes across very short time periods (Yen et al. 2006). In addition, knowledge from other Mediterranean regions suggests that insect pollinators could be active throughout the year (Michener 1979; Petanidou & Lamborn 2008) and there is a need for studies that identify how the expansion of tree crop plantations in the Australian mallee regions might influence local pollinator communities. I refer to individuals in the taxonomic orders of Diptera and Hymenoptera as potential pollinators, hereafter simply pollinators, although I did not quantify almond pollination. Fly and wasp species have been documented as pollinators of native flowers in the study region (e.g. Ford & Forde 1976; Horskins & Turner 1999) and I observed many of these individuals visiting almond flowers during sampling. In addition, Diptera and non-bee Hymenoptera species are important, but understudied, generalist pollinators in a variety of natural and agricultural systems (Larson et al. 2001; Rader et al . 2013), especially in Australia (Armstrong 1979; Pickering & Stock 2003), and many dipteran and hymenopteran species that are generally categorised as ‘natural enemies’ are also common, but unacknowledged, flower visitors and pollinators (Larson et al. , 2001; Wäckers et al ., 2005). I investigated the relationships between these insect groups and site attributes relevant to vegetation structure and resource factors, before, during and after almond flowering. I also explored associations between September abundance counts of herbivore insect groups collected at each site (Psocoptera; Hemiptera: Aphididae, Cicadellidae) and abundance of flies and wasps in each vegetation type, as these insects are common host or prey species for many flies and wasps and are therefore another potential resource factor in my study system. In particular, I considered whether monoculture almond plantations provide sufficient food and habitat resources for overwintering and emerging pollinator species by asking the following questions: (i) How do vegetation structure and resource attributes differ between mallee woodlands and almond plantations, throughout the flowering period? (ii) Does the relationship between site heterogeneity and abundance or richness of pollinator insect groups vary across 98 the almond flowering period? (iii) How is the abundance and richness of pollinators related to the availability of understorey attributes and resources?

6.2 Methods

6.2.1 Study area and vegetation structure

I sampled 68 sites across two conventional monoculture almond plantations (n = 50) and adjacent mallee woodlands (n = 18) in the Victorian Murray Mallee Bioregion, northwest Victoria, Australia (-34.8 °, 142.6°). Average annual rainfall in the study area is 314.9 mm (Station 076000; Bureau of Meteorology 2013). I sampled on three occasions in 2011, before (July), during (August) and after (September) the mass-flowering event in almond plantations, which typically lasts 3-4 weeks. Plantations that were adjacent to large patches of remnant mallee vegetation were chosen for sampling (see Appendix 5 Table A5.1). Liparoo plantation is adjacent to the Annuello State Flora and Fauna Reserve and Wemen plantation is adjacent to an unnamed patch of remnant woodland. Both plantations are broadacre monocultures, intensively managed by the same company and the linear distance between the two plantations is approximately 7.5 km. Plantations had identical row widths (5-6 m) and tree spacing (5 m) and herbicides were used to maintain bare ground across plantation floors. Almond trees are deciduous and plantations were completely bare of leaves or flowers before flowering (July). In August, mass-flowering occurred across the entire plantation, while each tree was still predominantly leafless (approx. 50,000 individual blossoms per tree). In September, the majority of almond blossoms had dropped and trees were fully leaved. Mallee woodlands are open, shrubby evergreen woodlands on alkaline, semi-arid soils and are restricted to Mediterranean climate zones of southern Australia. They have heterogeneous middle- and upper-storey vegetation, dominated by Eucalyptus species growing in the characteristic ‘mallee’ habit of shorter trees with multiple trunks, as well as Acacia , Callitris and Casuarina tree and shrub species. The understorey is highly variable, with areas of bare ground interspersed with patchy ground cover typically including hummocks of Triodia , halophytic plants, herbs and grasses, and dense mats of leaf litter near eucalypt trees. Phenological changes in 99 woodland structure were less obvious across the sampling period, compared to plantations, with evergreen vegetation and diverse, patchy floral resources available in all months (see Figure 5.2 for representative photographs).

6.2.2 Data Collection

Flying insects were collected using pan traps and data on local habitat attributes were recorded at each site. Pan trapping was chosen over on-site visual identification methods, as it does not suffer from the ‘collector bias’ inherent in the latter (Westphal et al ., 2008); however, colour and placement of traps was designed relevant to the context of the system and research questions, to increase the chance that the data collected were ecologically meaningful (see Saunders & Luck 2013; Appendix 1). One white and one yellow pan trap were placed at each site, as white was the predominant flower colour in plantations and yellow the most common in woodlands. Pan traps were placed on the ground under flowering trees for maximum visibility, and because I was interested in associations between ground-level resources (leaf litter, ground cover vegetation) and pollinator insects that use these resources for foraging and nesting sites. As discussed in section 6.2.1, mats of leaf litter and herbaceous/weedy ground cover are the most common ground-level resources in both vegetation types, and these elements are eseential nesting and foraging habitat for flies, non- Apis bees and wasps (Leather et al. 1993; Austin et al. 2005; Michener 2007). Traps were open for 8 hours on fine, calm days, and were set just after sunrise and collected just before nightfall in the order they were placed. Insects were transported to the laboratory in 70% ethanol, counted and sorted into taxonomic groups (Diptera; Hymenoptera (non- Apis bees and wasps); Hemiptera (Aphididae, Cicadellidae); Psocoptera). I identified native bees to species (where possible, although most Lasioglossum individuals could only be classified to subgenus) and wasps and flies to family groups using identification keys (Hamilton et al. 2006; Stevens et al. 2007) along with a reference collection provided by a taxonomic expert (K. Walker, Museum Victoria). At each trap site, I collected data on vegetation structure, as well as canopy cover in September, as almond plantations were bare of leaves during July and August. I did not quantify floral diversity or abundance in this study, because 100 plantations contained a single plant species (almond) that flowered in August only and no flowering weedy plants were present under almond trees, so statistical comparisons with woodland floral diversity across months would not be possible. Small-scale vegetation structure was quantified using a line intercept method. At each trap site, a 10 m line transect was marked out in a randomly-chosen direction and the proportion of the transect length covered by the following structural attributes was recorded: bare ground; leaf litter or decaying wood/nuts; herbaceous ground cover vegetation (forb, succulent, moss); annual/perennial grass; Triodia hummock grass; shrub <1 m; tree 1 —2.9 m; tree 3 —4.9 m; tree >5 m. In September, canopy cover was measured by placing a digital camera facing upwards on the ground beside each trap and photographing the canopy above the trap site. Images were then converted to black and white and the percentage of the image area that was covered with canopy vegetation was quantified using ImageTool 3.0, a freeware image processing and analysis program (UTHSCSA 2002, available from URL http://compdent.uthscsa.edu/dig/itdesc.html).

6.2.3 Data Analysis

Analyses of pollinator groups were conducted on abundance counts and a richness index. Richness was calculated using the Menhinick index (Menhinick 1964). Insect abundance counts were not transformed, as this approach is not recommended when analysing overdispersed ecological count data (O’Hara & Kotze 2010; Warton & Hui 2011). Raw data from the vegetation structure transects were used to calculate a heterogeneity score for each site using the complement of Simpson's Index (1-Simpson's Dominance measure): a score of 1 means that all habitat attributes were equally present along the measured transect (high heterogeneity) and a score of 0 means one habitat attribute dominated the entire transect (low heterogeneity). Differences between the two vegetation types were identified for each habitat attribute and insect group using Mann-Whitney ( U) pairwise tests, and then pollinator community composition was compared between woodlands and plantations using ordination and an associated permutation test for significance. I used non-metric multidimensional scaling (NMDS) based on Bray-Curtis similarities between sites to illustrate overall pollinator community composition 101 for the whole sampling period (pooled monthly abundance counts of each pollinator insect group). The 'goodness-of-fit' for the configuration was confirmed with an accompanying Shepard plot and STRESS value (values < 0.1 indicate that the plot shows an ideal configuration). To identify whether the pattern shown in the NMDS ordination was representative of the whole sampling period, a one-way ANOSIM (analysis of similarities; Clarke 1993) was used to test for significant differences in monthly pollinator communities grouped by the two vegetation types, mallee or almond. The analysis provides an R statistic between -1 and +1: values < 0.25 indicate that similarity coefficients within the compared groups are similar to coefficients between groups, so the groups cannot be separated; values > 0.50 indicate that the groups can be separated, but there is some overlap; and values > 0.75 indicate distinct separation between groups (Chapman & Underwood 1999).

6.2.4 Relationships between potential pollinators and habitat attributes

Relationships between pollinator groups and habitat attributes were identified using generalised linear models (GLMs). First, I examined the relationship between site heterogeneity scores and monthly richness and abundance of each insect group, and compared the fit of each insect/heterogeneity model with the intercept-only model. I then examined relationships between insect richness and abundance and the understorey attributes that were present in both vegetation types, ‘leaf litter’ and ‘ground cover’. I focused on these attributes as they were the most biologically meaningful to my study questions, because many of the target insect species nest or overwinter in the ground, leaf litter or ground cover vegetation (Leather et al. 1993). ‘Canopy cover’ was also included in models of September insect assemblages, as this attribute affects understorey light availability and can influence insect abundance (Michener 2007; Abrahamczyk et al. 2010). GLMs were based on combinations of selected predictors. Monthly insect abundances were modelled with a negative binomial distribution and richness data were modelled with a linear distribution. The number of parameters (inclusive of the intercept) in each model was limited to a maximum of six, to avoid over- fitting. I included vegetation type (mallee, almond) as a categorical factor, and 102 percent ground cover, leaf litter (all months) and canopy cover (September models only) as covariates. Interaction terms between vegetation type and each attribute were included, as I believed that the importance of ground cover, leaf litter and canopy cover may differ in plantations compared to mallee woodlands. I included the intercept-only model for all response variables as a baseline comparison. Competing explanatory models for each response were compared using an information-theoretic model selection process. I compared information criterion values within each set of models to rank models based on goodness-of-fit and complexity. Richness models were compa red by the modified Akaike’s

Information Criterion for small samples (AIC c) and abundance models by the quasi-likelihood adjusted AIC c (QAIC c) for overdispersed count data. Hereafter, both AIC c and QAIC c values are referred to as simply AIC c. Differences in AIC c

(Δ i) were calculated relative to the smallest AIC c value in each model set and a candidate model set for each response (all models with Δ i < 10) was examined.

Most of the relationships were explained by the Δ i < 2 model set, so only these are presented here (see Appendix 5 Table A5.2 for full list of candidate models). I calculated relative log likelihoods, L(g i) = exp(- 0.5*Δ i), and AIC c weights, wi, for each model to assess the probability of each model being a better fit to the data relative to other models in the set and summed wi over all models in the set containing that predictor to obtain an estimate of the relative importance of each explanatory variable. In September model sets, the ‘Veg Type’ variable was included in only five models, while all other variables were included in six models. Therefore, all September variable weights were calculated from only the five highest-ranked models for each variable, to balance the number of models containing each variable. To explore associations between Hymenoptera and Diptera and herbivorous insect groups I used association analysis as an exploratory tool for future hypothesis-generation, as I did not identify herbivores to species, nor did I measure trophic interactions between parasitoids and prey species in my system. To minimise the potential distortion of species associations arising from a high number of zeros in the abundance data, I converted abundances for all five insect groups to presence/absence data. I used Sørensen’s coefficient ( Ssor ) for presence/absence data, as my focus was on co-occurrence of species per site (Legendre & Legendre 1998). All analyses involving native bees and herbivores 103 were conducted on September data only, as these insect groups were mostly absent in July and August. GLM analyses were conducted in IBM SPSS 20 (IBM Corporation 2011) and I used PAST 2.17 (Hammer et al., 2001) for all other analyses.

6.3 Results

6.3.1 General observations and overall community composition

I caught 2476 flies in 20 family groups, 336 wasps in 23 families, and 171 native bees in 2 genera (see Appendix 2 Table A2.3). Fly abundance was significantly higher in the plantations in all months, but monthly richness was not significantly different between vegetation types. Wasp abundance and richness in July were significantly higher in mallee, but wasp and bee abundance and richness in other months were not significantly different between vegetation types (Table 6.1). The cover of canopy and bare ground were higher at almond sites, while overall habitat heterogeneity and all other vegetation attributes were higher at mallee sites (Table 6.1). Cicadellidae and Psocoptera abundance were higher in plantations than in woodlands, but Aphididae abundance was not different between vegetation types (Table 6.1). Overall pollinator community composition across the sampling period was different between vegetation types, although there was some overlap between sites (Figure 6.1). Results of the ANOSIM test showed that mallee and almond pollinator communities differed significantly in all months, but the most variation in similarity coefficients occurred in September ( R = 0.44, p < 0.001). Community similarity between vegetation types was only weakly separated in July ( R = 0.15, p = 0.02) and August ( R = 0.16, p = 0.01).

104 Table 6.1 Mean (±SD) monthly insect abundance (A) and richness (R) of each pollinator group and mean (±SD) values for all resource attributes per trap site in mallee vegetation and almond plantations. Mallee (n = 18) Almond (n = 50) Pollinator group Flies Jul (A) 7.2 (5.4)* (A) 3.5 (2.5) (R) 1.7 (0.6) (R) 1.5 (0.7) Aug (A) 10.3 (7.3) (A) 17.1 (11.5)* (R) 2.1 (0.7) (R) 2.1 (0.6) Sep (A) 7.4 (5.5) (A) 19.9 (10.8)* (R) 1.9 (0.5) (R) 1.9 (0.6) Wasps Jul (A) 1.6 (1.4)* (A) 0.6 (1.2) (R) 1.0 (0.6)* (R) 0.4 (0.6) Aug (A) 2.4 (4.3) (A) 0.9 (1.0) (R) 0.7 (0.6) (R) 0.6 (0.6) Sep (A) 3.5 (3.9) (A) 2.5 (2.4) (R) 1.3 (0.5) (R) 1.2 (0.8) Native bees Sep (A) 4.5 (9.8) (A) 1.7 (2.7) (R) 0.6 (0.5) (R) 0.5 (0.5) Resource attributes Overall habitat heterogeneity 0.73 (0.2)* 0.19 (0.1) Bare ground 0.15 (0.1) 0.89 (0.1)* Leaf litter/decaying wood 0.21 (0.1)* 0.04 (0.04) Ground cover vegetation 0.1 (0.1)* 0.01 (0.03) Shrub < 1 m 0.18 (0.3) 0 Grasses 0.06 (0.07)* 0.007 (0.05) Triodia hummock 0.03 (0.07) 0 Tree 1-3 m 0.1 (0.1) 0 Tree 3-5 m 0.09 (0.1)* 0.01 (0.02) Tree > 5 m 0.09 (0.1) 0.04 (0.03) Canopy cover^ 0.26 (0.1) 0.58 (0.2)*

105 Herbivorous insects

Cicadellidae^ (A) 0.56 (0.8) (A) 3.28 (3.2)* Psocoptera^ (A) 0.22 (0.4) (A) 1.92 (2.7)* Aphididae^ (A) 0.22 (0.6) (A) 0.48 (1.1) ^ data are from September only; Asterisks indicate in which vegetation type each value was significantly greater, * p < 0.01. Overall habitat heterogeneity = Simpson’s index, 1 -D; Vegetation resource attributes = proportion of cover along 10 m transect

Mallee Almond

Figure 6.1 Non-metric multidimensional scaling ordination of overall pollinator community similarity (Bray-Curtis) between mallee woodlands and almond plantations. The configuration shown is an ideal representation of the data (STRESS value = 0.07).

6.3.2 Site heterogeneity

The influence of overall habitat heterogeneity at each site on pollinator groups varied across the sampling period (Table 6.2). Fly abundance was higher at more heterogeneous sites in July, but was associated with increased homogeneity in August and September. Fly richness in all months was not influenced by habitat heterogeneity. Wasp abundance in July and August, as well as richness in July,

106 were associated with higher habitat heterogeneity. The following groups were not associated with site heterogeneity: August wasp richness; September wasp richness and abundance; native bee richness and abundance.

6.3.3 Understorey attributes and resource factors

The responses of each insect group to understorey attributes varied across the flowering season (Tables 6.3 & 6.4). In July, fly abundance showed the strongest association with ground cover vegetation, which had a summed Akaike weight of 0.84 (Table 6.4). However, the parameter estimates for ‘ground cover’ in the two highest-ranked models had broad confidence intervals encompassing zero and the vegetation type/ground cover interaction term in both models showed that fly responses to ground cover differed between vegetation types (Table 6.3). Pearson correlations between abundance and ground cover for each vegetation type confirmed that the presence of ground cover was positively correlated with fly abundance in mallee (r = 0.43, p = 0.07), but was not associated with abundance in almond plantations (r = -0.17, p = 0.24). The third-highest ranked model suggested a positive relationship between leaf litter and fly abundance in July, regardless of vegetation type, but the likelihood of this model was low ( L(g i) = 0.39) and the relative importance of leaf litter was lower than either ground cover or vegetation type ( wi = 0.45; Tables 6.3 & 6.4). In August, the model containing only vegetation type as a predictor provided the best fit for fly abundance data (Table 6.3), but leaf litter had the highest relative influence compared to the other predictors (Table 6.4) and this attribute had a strong negative relationship with fly abundance (Table 6.3). Leaf litter and ground cover were associated with fly abundance in September, but vegetation type had the highest relative influence ( wi = 1) (Table 6.4). Vegetation type also affected the fly —ground cover relationship and correlation analysis showed that fly abundance was positively associated with ground cover vegetation in almond plantations (r = 0.35, p = 0.01) but was not associated with ground cover in mallee woodlands (r = -0.22, p = 0.38). Fly richness showed a weak positive association with leaf litter in July and ground cover in August (Tables 6.3 & 6.4), but the intercept-only model was ranked as the best fit to the fly richness data in both months. September fly richness was negatively associated with 107 i w 0.43 0.34 0.29 0.99 0.25 0.30 0.32 2 χ 1.6 0.9 0.4 19.9** 0.03 0.5 0.70 Richness Model 0.35 (0.28) (0.28) -0.26 (0.25) -0.15 1.16 (0.24) (0.28) -0.05 (0.30) -0.21 0.16 (0.20) i 0.93 0.99 0.92 0.78 0.26 0.35 w 0.99 2 8.5** 18.9** 11.6** 5.1* 0.02 1.7 χ 18.2** : likelihood ratio test statistic, indicates if the model provides a significantly better fit for the data better for significantly fit a model provides if the statistic, indicates : likelihood test ratio 2 -12.36 (3.60) -12.36 (3.50) -19.39 1.30 (0.44) 0.52 (0.24) 0.03 (0.16) (1.41) -2.13 Abundance Model 0.59 (0.17) : Akaike weight for the the probability is for the best relationship that the model the heterogeneity fit weight model, gives for : Akaike i w September September Month July August September July August Relationships between overall site heterogeneity and abundance and richness of each pollinator insect group in each of abundance pollinator insect overall month. and each richness group site heterogeneity and between Relationships * p < 0.05; 0.01* p < ** p < estimates χ SE with Model: parameter in parentheses; Bee Wasp Fly Pollinator group Table 6.2 set than the ‘constant only’ model; only’ the ‘constant than set

108 i 0.28 0.24 0.22 0.11 w 0.41 0.21 0.16 ) i (g 1 0.86 0.80 0.41 L 1 0.51 0.39 c AIC i 0 0.31 0.46 1.81 Δ 0 1.36 1.87 nked the highest, onlynked the second- the highest, ≤ 2) in each candidate set are shown. Where the constant model is ra shown. the constant setonly are ≤ 2) in Where candidate each c AIC i Veg (0.47; 1.20) + (49.04; 1.20) + (0.47; GC (-15.65; 17.14) Veg*GC Veg 9.76) + (49.19; 2.17) + (1.64; (-13.81;GC (16.78; Veg*GC Veg 9.62) + 9.72) + LL (-19.6; Veg*LL + 18.86) 13.95) (11.23; 5.60) + GC (15.5; 8.35) LL 2.19) (-6.78; Veg (-28.99; 8.20) LL 4.29) + (-94.53; (72.76; Veg*LL (0.63; + 50.13) Veg LL 51.81) Model GC (-17.36; 16.54) + LL 9.22) 16.54) + (-25.65; GC (-17.36; LL ranked models (Δ ranked July August Month Relationships between resource attributes and monthly pollinator insect abundance (A) or richness (R) across all almond (R) across all (A) or pollinator mallee and resource abundance attributes richness monthly and insect between sites (n Relationships highest model is listed for comparison. All models include the Intercept. See model is set candidate All models listed the full A5.2 Appendix for the 5 Table for comparison. highest include Intercept. each models for of variable.response Response (A) Fly Table 6.3 highest- The 68). =

109 0.29 0.19 0.26 0.12 0.12 0.35 0.29 0.18 0.14 0.30 0.19 0.16 1 0.65 1 0.46 0.45 1 0.83 0.51 0.40 1 0.62 0.53 0 0.88 0 1.54 1.58 0 0.38 1.34 1.84 0 0.96 1.26 LL (1.27; 0.71) (1.27; LL 0.08) Con (1.50; (1.18; 0.72) + GC (0.69; 1.14) LL 0.08) Con (2.12; 0.22) (-0.13; GC (-7.84; Veg*GC (7.96;+ + Veg 3.18) 3.50) 0.03) (-0.8; CC 0.91) + (-1.05; GC 1.22)Veg (2.06; (0.19; 0.45) + + LL Veg (-0.76; 0.21) (-7.37; (-0.76; GC (5.56;+ Veg*GC Veg + 2.60) 3.0) 0.30) (3.07;(-1.06; Veg*LL (-1.32; 1.91) + + Veg 2.21) LL 0.16) (-0.99; Veg 0.99) (2.63; GC (1.39; 1.34) 0.33) (-1.54; CC 0.48) + + (0.12; + Veg LL Veg (-0.63; 0.27) + LL (0.12; 1.94) + Veg*LL 0.27) (2.47; (0.12; Veg*LL (-0.63; + 2.18) 1.94) + Veg LL 0.07) Con (1.92; July August September September (R)

110 0.41 0.18 0.27 0.14 0.30 0.29 0.15 0.28 0.26 0.23 0.19 0.14 0.53 1 0.43 1 0.53 1 0.98 0.49 1 0.93 0.83 1 0.73 1 0 1.71 0 1.27 0 0.04 1.43 0 0.15 0.37 0 0.63 0 Con (-0.49; 0.12) Con (-0.49; 0.16) + (0.54; Veg*LL (0.38; 1.49) + Veg (-1.95;LL 1.66) (1.93; 0.66) + GC (2.36; 1.04) LL 0.08) Con (0.64; GC (0.79; 1.13) 0.08) Con (1.20; GC 1.23) (-1.19; LL (4.45; 2.03) (4.45; LL 0.43) (0.96; Veg (3.90; 1.94) + GC (2.57; 2.67) LL GC (1.95; 0.68) 0.26) + (0.26;(-1.89; Veg*LL (0.71; 2.93) + Veg LL 3.07) 0.16) (0.41; Veg September July August September July August (R) Wasp (A)

111 0.37 0.23 0.23 0.21 0.15 1 0.63 0.62 1 0.73 ) = relative likelihood of the) = relative i (g 0 0.94 0.97 0 0.62 L values, relative to the highest-ranked model; to the highest-ranked values, relative c = difference in AIC difference = c AIC i LL (-4.72; CC (-4.74; 1.84) + 0.91) LL Con (0.50; Con (0.50; 0.06) GC (1.02; 0.81) CC (-4.83; 0.88) + LL (-4.05; GC 1.79) + CC + 0.88) (-4.83; LL (-5.45; 2.74) CC (-3.89; 0.63) + (-5.58; (0.78; GC (-6.61; 2.14) + 1.08) + Veg 2.85) LL September September = Akaike weight for the model. weight for Akaike = i w Veg = vegetation type; CC = canopy cover; LL = leaf litter; GC = ground cover; Con = constant only type only model. Vegetation leaf GC = litter; cover; is = Con = constant ground cover; CC vegetationLL type; canopy = = factor, Veg categorical a Model: SE predictor in and each between relationshipsestimates parameter types. for the two vegetation whether pollinator-habitat differed testing Δ term; an * indicates interaction parentheses; (R) Native Bee (A) Bee Native model;

112 1 0.61 0.34 0.31 0.22 0.25 0.24 0.22 0.75 0.56 0.25 0.40 0.50 0.38 Vegetation Type Vegetation 0.39 0.36 0.78 0.23 0.23 0.82 0.25 0.45 0.60 0.43 0.52 0.27 0.53 0.50 Leaf Litter Leaf 0.84 0.22 0.49 0.35 0.45 0.47 0.27 0.50 0.27 0.65 0.26 0.36 0.72 0.34 Ground Cover Ground 0.17 0.50 0.25 0.23 0.99 0.30 Canopy Cover Canopy i w ) for each resource attribute included as an explanatory variable in generalised linear models, indicating the linear variable models, indicating included an explanatory resource in generalised attribute as each ) for i w September September September August September July August September July August Month July August September July Summed Akaike weights ( weights Summed Akaike Canopy cover was not included in July and August models. All September variable weights were calculated from the five highest-ranked models for each variable, to to variable, each for models the highest-ranked five from calculated were weights variable September All models. and August July in included not was cover Canopy to calculate used of models the number balance Bee (abundance) Bee (richness) (richness) Wasp (abundance) (richness) Response (abundance) Fly Table 6.4 relative importance of each attribute for explaining variation in the response variables (insect abundance or richness). or abundance richness). variation for (insect of eachvariables response in the explaining importance attribute relative

113 canopy cover and positively associated with leaf litter (Table 6.3), but all four predictors had relatively equal importance (Table 6.4). Wasp richness in July showed the strongest (positive) relationship with both ground cover and leaf litter and these relationships were not affected by vegetation type (Tables 6.3 & 6.4). Wasp abundance in July and August showed the strongest (positive) relationship with leaf litter and ground cover respectively, regardless of vegetation type, although the model containing vegetation type alone was also ranked highly in both months (Table 6.3). None of the predictors were related to wasp richness in August and September or abundance in September (Tables 6.3 & 6.4). September native bee abundance had a strong negative relationship with canopy cover, ground cover and leaf litter, but was not influenced by vegetation type (Table 6.3). Canopy cover had the strongest relative influence on bee abundance compared to the other attributes ( wi = 0.99; Table 6.4). Bee richness showed a weak association with ground cover (Tables 6.3 & 6.4). At mallee sites, fly and wasp presence showed a moderate association with

Cicadellidae presence (wasp, Ssor = 0.58; fly, Ssor = 0.56), but co-occurrence with

Psocoptera (wasp, Ssor = 0.38; fly, Ssor = 0.36) and Aphididae (wasp, Ssor = 0.30; fly, Ssor = 0.29) was much weaker. In almond plantations, wasps and flies were strongly associated with Cicadellidae (wasp, Ssor = 0.85; fly, Ssor = 0.91) and

Psocoptera presence (wasp, Ssor = 0.69; fly, Ssor = 0.70), but Aphididae presence was not strongly associated with either wasps ( Ssor = 0.39) or flies ( Ssor = 0.39).

6.4 Discussion

6.4.1 Site heterogeneity

Although the composition of potential wild pollinator assemblages differed significantly between mallee woodlands and almond plantations in each month, there was a lot of overlap in assemblages for the whole sampling period, suggesting that pollinator community composition in mallee or almond vegetation was more likely associated with variation in smaller-scale environmental factors rather than the overall vegetation type. The strongest compositional difference between mallee and almond assemblages was in September, possibly influenced

114 by the significant increase in the number of insects collected in plantations in that month. Some of the increased abundance is likely accounted for by warmer spring temperatures which would have prompted the emergence of more adult insects, especially bees (average daily min-max temperature range for study area: 4.3 °C – 15.1 °C in July compared to 6.7 °C – 20.2 °C in September; Bureau of Meteorology 2013). The relationship between each pollinator group and site heterogeneity also varied across months. Abundance and richness of flies and wasps were associated with higher heterogeneity in July, and wasps were also more abundant at heterogeneous sites in August, during the almond flowering event. However, fly abundance was negatively related to site heterogeneity in August, as well as in September, after flowering had finished, when they were the only pollinator group to show a relationship with site heterogeneity. This variation in insect-site heterogeneity associations across such a short time period reinforces the idea that the relationship between farmland biodiversity and agricultural intensification is not a simple linear one (Burel et al. 1998) and an insect’s response to agricultural environments can be more influenced by its life stage (e.g. Delettre 2005) or seasonal environmental changes (e.g. Reese & Philpott 2010) than on simple metrics of overall habitat heterogeneity. Tews et al. (2004) highlight the importance of considering how ‘heterogeneity’ metrics are measured, as the attributes used to calculate the metric may create beneficial ‘heterogeneity’ or detrimental ‘fragmentation’ for different taxa, depending on the context. For example, my habitat heterogeneity score was based on the number of different surface elements across a given stretch of land. Consequently, an area of completely bare ground would receive a similar heterogeneity score as an area completely covered by leaf litter, yet the latter would provide structural heterogeneity and diverse micro-habitats and climates for a variety of taxa. Hence, it would be reasonable to consider relationships between organisms and specific structural attributes. The regression models for individual habitat attributes corroborated this, as abundance or richness of some insect groups were strongly related to individual attributes even when they were not related to overall site heterogeneity (Tables 6.2 & 6.3).

115 6.4.2 Understorey attributes: winter

Many Diptera and Hymenoptera species are dependent on the thermal protection provided by the litter layer or ground cover vegetation during winter, as they either overwinter or nest there themselves, or prey on other species that live in these microhabitats (Leather et al. 1993; Landis et al. 2000; Austin et al. 2005; Michener 2007; Bale & Hayward 2010). This was reflected in my results, as a high proportion of leaf litter and ground cover was particularly important for flies and wasps in July, when almond plantations were devoid of resources in the upper vegetation layer. Wasp abundance and richness were associated with leaf litter and ground cover regardless of vegetation type, but the relationship between fly abundance and ground cover was strongest in mallee woodlands, where the proportion of ground cover vegetation was higher than in plantations. This suggests that there may have been other resources or environmental factors specific to mallee sites that influenced fly assemblages more than wasps. Wasp richness in July was the only response variable to show the strongest relationship with both ground cover and leaf litter together, but leaf litter had the greatest relative importance. There is a paucity of knowledge on the ecology of wasp species in my study system specifically; however, leaf litter provides vital habitat and foraging substrate for various Australian wasp families, including scelionid species (Austin et al. 2005), which were common in samples in July, and litter patches are known to be essential habitat for other hymenopteran taxa, i.e. ants, in the Victorian mallee (Andersen 1983; Yen et al. 2006). Although some of my sampled wasps were likely parasitoid species, many carnivorous wasps are also nectar-feeders and their effectiveness as pollinators is often underestimated (Wäckers et al. 2005). In addition, pollination and pest control services in agroecosystems are synergistically linked (Bos et al. 2007; Lundin et al. 2013) and more detailed investigation of this study system would contribute valuable information to our knowledge of these interactions. I was unable to determine if understorey attributes were important for bees in this study, as I collected no bees in July and only four individuals in August; however, this was most likely a result of the passive sampling method, which was unable to detect hibernating or larval- stage insects. Many of the bee species I collected are known to nest in the ground (Knerer & Schwarz 1976; Houston & Maynard 2012) and active sampling for nest 116 sites and brood cells would reveal more information about their nesting habits in my system.

6.4.3 Understorey attributes: late winter-spring

The relationships between wild pollinators and ground-level attributes in August and September were weaker than in July, suggesting that the importance of these attributes declined as the weather warmed and resources increased in both vegetation types. There was no evidence of a convincing relationship between August fly richness and any predictor, but the candidate set of models for August fly abundance revealed a strong negative relationship with leaf litter. However, vegetation type was ranked nearly as highly as leaf litter for explaining fly abundance and, given that fly relationships with leaf litter in other months were either positive or neutral, this most likely reflected that abundance was extremely skewed toward plantation sites in August. The negative relationship with leaf litter could be reflecting a positive relationship with ‘bare ground’, the most dominant attribute in plantations (leaf litter was negatively correlated with the proportion of bare ground, r = -0.73, p < 0.001). However, it is also possible that the negative relationship indicated fly species dependent on leaf litter habitats were using the plantation litter habitats more than mallee litter habitats. This could be because of the additional resource supply available from nearby almond flowers (Fayt et al. 2006), or because of the increased connectivity of litter patches in plantations, which were configured in continuous strips of litter along rows of almond trees (Schiegg 2000; Randlkofer et al. 2010b). In mallee woodlands, areas of litter were significantly larger, but more patchy, with areas of bare ground or vegetation between litter patches, and this raises interesting questions for investigations of potential pollinator insects using litter habitats in my system. Wasps showed the strongest association with ground cover vegetation in August, which had greater cover at mallee sites. Although I did not record plant species at each site, many of the native understorey plants were flowering at mallee sites during sampling and it is possible that these flowers were preferred by foraging wasp species over almond flowers. There are many examples of local adaptation and co-evolution between plants and their insect pollinators (Fægri & van der Pijl 1979; Kalske et al. 2012), particularly among Australian 117 hymenopterans (Adams & Lawson 1993; Peakall & Handel 1993; Dyer et al. 2012). Mallee ecosystems are typical of Mediterranean bioregions in other parts of the world, with a high diversity of annual and perennial herbs (Naveh & Whittaker 1979) and flowering tree canopies close to the ground (Andersen & Yen 1992), and wasps are common visitors to flowering herbs in European Mediterranean ecosystems (Bosch et al. 1997). Hence, non-bee hymenopterans native to this region may have co-evolved with, and therefore prefer, these flowering plant species (Kummerow 1983) and this is an area with scope for further research. In September, wasps were not associated with any of the measured habitat attributes, but wasp presence was related to the presence of Cicadellidae and Psocoptera in plantations, as was fly presence. This result could indicate parasitoid-host or predator-prey relationships, or simply a mutual preference for similar habitat attributes, but further sampling and identification of potential interactions between these insect groups is necessary to confirm the nature of this effect. For example, fly abundance and richness were positively associated with leaf litter (Table 6.3), as was Psocoptera abundance (r = 0.38, p = 0.007), which could partly explain the fly-Psocoptera relationship I found. Fly abundance showed a positive relationship with ground cover in September, but only in plantations, and this result was most likely influenced by differences between the two plantations I sampled. Ground cover was more common at Wemen plantation, which has been established longer than Liparoo and contained predominantly mature almond trees (approximately 25 years old). Stands of mature trees are critical habitat elements for forest/woodland insect species, as they provide established microclimates and habitat structures (e.g. dead wood, bark crevices, leaf litter banks) that support diverse communities and complex food webs (Warren & Key 1991). This link between invertebrate communities and mature trees has also been found in orchard systems (Dubois et al. 2009; Herrmann et al. 2012) and predatory flies and wasps in particular may respond to the higher resource availability in these environments. Native bees showed the strongest response to vegetation structure in September, compared to other pollinator groups. Bee abundance was negatively associated with leaf litter, ground cover and canopy cover, suggesting a preference for lighter, more open areas. It is possible that this result was influenced by two of 118 the mallee sites which were extreme outliers – 35 % of the total bee abundance was collected at these two sites. Both sites had very low canopy cover and were dominated by shrubs and heath vegetation. However, when I removed the two outliers from the data set, overall bee abundance was still negatively correlated with canopy cover (r = -0.34, p = 0.006) and this effect was present in plantations (r = -0.65, p < 0.001), but not in woodlands (r = 0.12, p = 0.66). These results agree with the majority of findings that suggest bees prefer open areas over dense forests, particularly in rangeland, temperate or semi-arid systems (Grundel et al. 2010; Hoehn et al. 2010; Proctor et al. 2012). Leaf litter can be a limiting factor for bee communities in Mediterranean environments because many species prefer to excavate their nests into bare ground (Potts et al. 2005), including species in my system (Knerer & Schwarz 1976; Houston & Maynard 2012), and this could explain the negative relationship seen here. A similar effect could also explain the negative relationship between bee abundance and ground cover. In contrast, I found that bee richness was positively associated with ground cover vegetation, although the relationship was very weak. This agrees with the strong wild bee- ground cover relationships I found in Chapter 3. In addition, Yen et al. (2006) observed that Halictine bees in Victorian mallee (which dominated my collected bee fauna) appear to prefer understorey perennial flowering plants, particularly around disturbed, weedy sites, and this is a promising area for further research with the potential to inform management goals.

6.4.4 Conclusions and implications

This study has highlighted gaps in ecological knowledge of the species rich mallee insect fauna. My findings would be augmented by further investigation of the ecology and life history of pollinator insect species in the mallee, especially around the rapidly expanding almond plantations. Further sampling across more sites and over a longer time period, as well as more detailed taxonomic identification and examination of species-habitat relationships, would enable researchers to determine the contribution of these insects to ecosystem function in mallee woodlands and adjacent almond plantations. In relation to this, my study has provided two major insights. Firstly, I have shown that insects may be strongly associated with particular habitat attributes, or keystone structures (Tews 119 et al. 2004), despite showing no relationship with overall heterogeneity. This highlights the value in considering specific habitat and resource requirements of the focal species when investigating relationships between ecosystem service providers and habitat structure, rather than focusing on generalised ‘heterogeneity’ metrics. Secondly, although almond plantations supported a diverse group of potential wild pollinators, the relationships between different groups of pollinators and habitat attributes at each site varied among taxa, and also with seasonal and phenological changes in the local environment. This knowledge will be beneficial to plantation managers and may encourage the adoption of holistic, ecologically-focused management strategies that provide resource diversity within plantations to support multiple wild pollinator taxa, and other ecosystem service providers, throughout the year. Hence, given the conservation value of the mallee bioregions where Australian almond production is concentrated and the gaps I have highlighted in our knowledge of habitat use by mallee pollinator communities, this study system provides a unique opportunity for further investigation of these issues and the potential to align conservation and management goals.

120 PART III: SYNTHESIS

121 122 Chapter 7: Synthesis and Conclusions

7.1 Synthesis and general discussion

Ecological management of agroecosystems relies on understanding the biological properties and ecological processes of the natural environment surrounding the system (Pimentel et al. 1989). In particular, knowledge of local insect communities and the way they interact with their environment can have widespread benefits for biodiversity conservation and the productive capacity of the agroecosystem. The work presented here has achieved two aims: it provides insight into potential wild pollinator taxa living in the Victorian Murray Mallee that could provide pollination and pest control services to almond growers; and it gives credence to my overall thesis that intensively-managed monoculture tree crop plantations do not provide the vegetation heterogeneity necessary to support diverse native insect communities in the long-term. The data presented here were limited in seasonal scope, being relevant to the almond flowering season in late winter. The purpose of this was to provide information on the most critical life stages for both the crop variety and the focal insect taxa. Almond trees rely completely on insect pollination to set fruit, so the ability of a commercial almond orchard to sustain a diverse pollinator community in the long-term is paramount. This limitation is even more challenging for growers because almond trees flower in winter when insect activity is limited. Additionally, given the natural dynamics of semi-arid mallee ecosystems, where resource availability can be unreliable and prone to extreme weather events, resource-rich agroecosystems could provide a consistent resource supply for some insect species. Thus, prioritising ecological management of these agroecosystems has potential to enhance the conservation of both wild insects and the ecosystem services they provide. My results have shown that potential wild pollinator taxa are using almond plantations during flowering, as well as immediately before and after, but their distribution throughout plantations appears to be limited by homogeneous vegetation structure and lack of permanent resources. A number of results were of particular interest:  The highest bee and wasp abundances were collected in a plantation that combined living ground cover along the middle of rows with bare

123 ground along the tree line (Chapter 3). Given our knowledge of the nesting habits of many hymenopteran species, particularly in Mediterranean ecosystems, this result is not surprising. A contiguous carpet of ground cover across the plantation floor can provide diverse floral resources, but not areas of bare soil for ground-nesting bees and wasps to excavate their nests into. Hence, almond plantations may be more capable of supporting permanent pollinator communities if they are managed with a combination of weedy ground cover and bare soil, rather than only one of these elements. This provides valuable baseline information for future research, as previous studies examining relationships between pollinator taxa and ground cover within fruit orchards have rarely provided any detail on the level of contiguity of ground cover vegetation (e.g. Mandelik & Roll 2009), or have only compared ‘bare’ orchards with ‘weedy’ orchards (e.g. Brown 2012). More studies that compare pollinator communities across orchards with multiple different types of ground cover configurations and specifically examine relationships between these two variables will increase our understanding of the optimal configuration of this important habitat attribute.  In broadacre monoculture plantations, native bees and hoverflies were only present in patches of remnant mallee vegetation within and adjacent to blocks of almond trees (Chapter 4). Native bees and hoverflies are the most recognised pollinators and this result has significant potential to motivate changes toward ecological management practices in the almond industry. It is possible that the sampling method underrepresented the abundance and richness of the pollinator taxa collected in plantations, as plentiful floral resources may divert insects from pan traps (Baum & Wallen 2011) and the single sampling event in this study could not account for temporal variation in pollinator abundance. However, the clear differences in abundance and richness of pollinator taxa caught in the plant-diverse orchards during flowering, compared to the abundance and richness of the same taxa caught in monoculture plantations (Chapters 3 & 4), shows that the method was able to provide comparative information on how pollinators were using the two plantation types during the critical flowering period. The pattern seen here 124 agrees with other studies suggesting that bees and hoverflies may be highly-sensitive to severe homogeneity and changes in vegetation contrast (Wratten et al. 2003; Kohler et al. 2008), as well as Brosi et al.’s (2008) recommendation to maintain small patches of natural habitat interspersed through intensively-managed landscapes. A long-term comparison of pollinator communities across plantations containing different sizes and configurations of internal mallee patches would shed more light on the importance of these landscape elements for supporting wild pollinators in broadacre plantation landscapes.  Of the two monoculture plantations, Wemen plantation had consistently more abundant and diverse pollinator communities than Liparoo plantation (Chapters 3-6). Wemen plantation was smaller than Liparoo, and also had more average ground cover vegetation across the orchard floor (Chapter 3). The implications of these results have been discussed in relevant chapters; however, it is likely that the smaller area gave foraging pollinator insect fauna greater access to the interior of the plantation and the presence of sparse ground cover also provided a limited amount of non-crop resources for pollinators. Given the overall similarity between the two plantations compared to other sampled vegetation types, this is unlikely to have had a significant impact on my results as a whole, but the ecological and management implications of this pattern requires more robust investigation. In combination, the results presented here provide incentive for further detailed research programs, because of the s tudy system’s two -fold significance. The mallee bioregions are unique ecosystems with high conservation value (Mallee Catchment Management Authority 2008; Cheal et al. 2013), but our knowledge of flying invertebrate fauna is limited (Yen et al. 2006). Additionally, almond is an economically- significant crop for Australia’s economy and the current pressures on the commercial honeybee industry warrant serious investigation of alternative pollination services available to growers. Based on the findings presented in this work, the limitations of my research can be expanded on through a number of detailed studies. In particular, my sampling method was chosen to maximise sampling time per site and to minimise bias from inexperience in visual identification and netting techniques 125 (Westphal et al. 2008; Popic et al. 2013). However, my sampling method did not account for potential Lepidoptera (moths and butterflies) or Formicidae (ant) pollinators, although individuals of both these groups were seen visiting flowers during sampling (see Appendix 2) and very small numbers of other known pollinator insect groups, Thysanoptera (thrips) and Coleoptera (beetles), were also caught in pan traps during sampling. Furthermore, family-level identification of insect samples is useful for providing broad estimates of ecosystem service providers, especially when reliable taxonomic information is limited, as was the case with my system; however, higher-level taxonomic identification may increase the application of these results after specific taxa of conservation and management interest to the system have been identified. Hence, experienced net sampling of the flowering almond canopy, combined with detailed flower visitor observations and species-level identification of samples, would expand my results with a more robust estimate of wild pollinator taxa available to pollinate almond flowers in this system. In addition to the sampling limitations, the logistical restrictions on the number of sites I could sample on each trip, particularly in the mallee vegetation, would have limited the breadth of the pollinator community I sampled. The accumulation curves shown in Chapter 3 (page 38) indicated that I had most likely not captured the full representation of wild hymenopteran taxa, although the curves for mallee dipteran communities and orchard hymenopteran communities did approach an asymptote. In addition, a number of taxa across all pollinator groups, and in both years, were only collected at mallee sites (Appendix 2) and the wetter than average weather conditions across the region during the two sampling years most likely influenced the abundance and richness of my target insects (Chapter 2). Given the lack of ecological information available on Hymenoptera and Diptera of the Victorian mallee regions, these are exciting areas for further investigation.

7.2 Conclusions The results presented in this thesis suggest that plant-diverse almond plantations established in heterogeneous landscapes contribute to local biodiversity conservation, and thus benefit from wild pollinators, more than intensively- managed plantations cultivated as broadacre monocultures. My results concur with numerous studies that have shown that: (a) weedy ground cover in crop 126 systems increases the abundance and richness of pollinator taxa (e.g. Mandelik & Roll 2009; Brown 2012); (b) ecological management practices on individual farms are often more responsible for creating resource heterogeneity and connectivity than the presence of particular vegetation types in the surrounding landscape (e.g. Kleijn & van Langevelde 2006; Persson & Smith 2013); (c) physical habitat boundaries in agroecosystems are not barriers to wild pollinator movement, but limited structure, complexity and resources available beyond the boundary may hinder their movement into homogeneous patches (e.g. Kohler et al.2008); and (d) the availability of nesting and overwintering resources is crucial for supporting beneficial insect communities in agroecosystems (e.g. Pfiffner & Luka 2000) and this is especially pertinent to winter-flowering crops like almond. Thus, the ability of a plantation to benefit from unmanaged pollination services during the flowering period may depend on its capacity to support pollinators throughout the plantation before flowering, i.e. overwintering or larval stages from the previous season, or after flowering, i.e. during the post-emergence reproductive stage. The work presented in this thesis has highlighted ecological differences, and similarities, between commercial almond plantations and local native mallee woodlands, with particular relevance to the conservation of wild insect pollinators. Overall, I have shown that ecologically-managed almond plantations have the potential to support a diverse wild pollinator community, which has important implications for both almond production and biodiversity conservation in the mallee regions. In addition, my research has highlighted a number of gaps in our knowledge of the ecological role of tree crop plantations and how they influence pollinator communities. This is an essential area for future research, given the global expansion of economically-important tree crop plantations and their potential to contribute to the conservation of multiple ecosystem services in agricultural landscapes. Most importantly, I have contributed to knowledge of the pollinator insect fauna of the Victorian mallee, a unique but understudied ecosystem, and presented data showing how these taxa interact with agroecosystems in the region. These results will hopefully motivate further scientific and public discourse and more detailed research into the importance of conserving wild pollinators, particularly in and around agroecosystems established in regions of high conservation value. 127 128 PART IV: REFERENCE MATERIAL

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162 APPENDICES

Appendix 1: Pan trap catches of pollinator insects vary with habitat

ABSTRACT Coloured pan trapping is a simple and efficient method for collecting flying insects, yet there is still discussion over the most effective bowl colour to use for particular target groups (e.g. pollinator insects). The success of particular colours can vary across bioregions and habitat contexts. Most published pan trap studies have been conducted in the northern hemisphere and very few investigated the effects of habitat context on pan trap catches. Our study is one of the first to (a) sample for potential pollinators in Australian mallee vegetation and almond orchards and (b) to investigate whether habitat context interacts with trap colour to influence pan trap catches. We sampled Hymenoptera and Diptera using yellow, white and blue pan traps in native mallee vegetation and two types of managed almond orchards (monoculture and plant-diverse) in the Murray Mallee bioregion of northwest Victoria, Australia. Yellow traps caught the most insects across all habitats, although catches in each coloured trap varied with habitat context. For all insect groups combined, blue traps caught more individuals in mallee habitats than in almond orchards. For native hymenopterans, yellow traps caught more individuals in plant-diverse orchards than in native sites, while blue traps caught more individuals in native sites. Our results highlight the importance of considering the habitat context of individual pan trapping surveys, as no one trap colour is likely to be suitable for trapping target insects across all habitat contexts.

*This appendix is published as Saunders ME, Luck GW ( 2013 ) Pan trap catches of pollinator insects vary with habitat. Australian Journal of Entomology 52:106- 113. (Formatting is per journal style.)

163 INTRODUCTION Coloured pan (or bowl) trapping is considered a simple, efficient method for collecting flying insects, and its lack of collector bias (compared to, for example, sweep netting) makes it particularly useful for comparing invertebrate communities across time, locations or when collector experience varies (Cane et al. 2000; Westphal et al. 2008). Pan trapping was traditionally used for sampling agricultural pests and phytophagous insects (e.g. Evans & Medler 1967; Boiteau 1983), but now is also used for collecting bees (Hymenoptera: Apoidea) and other pollinating insects (Leong & Thorp 1999; Campbell & Hanula 2007; Gollan et al. 2011). Despite the benefits of pan trapping as a sampling method, examination of its use for sampling pollinator insects remains limited. Potential sampling biases associated with the technique have been identified recently (Roulston et al. 2007; Baum & Wallen 2011) and it is not clear which bowl colour is most effective in particular contexts (i.e. the colour that attracts the highest abundance and diversity of the target species). Blue traps have been endorsed as the best bee attractant (Cane et al. 2000; Stephen & Rao 2005), yet the findings of research using pan traps to sample bees and other pollinator insects do not show a consistent preference for a particular bowl colour (e.g. Campbell & Hanula 2007; Gollan et al. 2011; Grundel et al. 2011). The success of certain pan trap colours in attracting pollinators may vary across bioregions, habitat contexts or topography. For example, Grundel et al. (2011) sampled sites in northwest Indiana, USA, using blue, yellow and white traps, and caught the most bees in white pan traps. Campbell and Hanula (2007) sampled sites in south-eastern USA, including coastal plain and southern Appalachian Mountain ecosystems, with red, blue, yellow and white pan traps and caught the most bees in blue traps. Gollan et al. (2011) sampled sites in the New South Wales North Coast, Sydney Basin and South Western Slopes regions in eastern Australia, using yellow and white traps and caught the most bees in yellow pans. To our knowledge, there is only one published study from pan trapping pollinator insects in Australian habitats (Gollan et al. 2011). Given the wide variation in results from pan trap sampling in the northern hemisphere, more pan trap studies are needed from the southern hemisphere to determine whether 164 catches in particular coloured traps are likely to vary with habitat or ecological context. Our study is one of the first to sample potential pollinators in the Murray Mallee region of northwest Victoria (Australia) using coloured pan traps and also one of the first to investigate whether habitat context interacts with trap colour to influence pan trap catches (see Missa et al . 2009; Abrahamczyk et al. 2010; Droege et al. 2010 for relevant discussions on habitat context). In this paper, the term ‘pollinator insect’ refers to any species of insect belonging to the taxonomic order Hymenoptera or Diptera. Although we did not quantify pollination efficiency in this study, we use the term to refer to flower- visiting species of these orders so as to maintain consistency with the pollination literature. Bees are the most recognised pollinators, yet wasps and flies are also known to be frequent flower visitors and pollinators (Kevan & Baker 1983; Larson et al . 2001) and many of these species are considered important, yet understudied, generalist pollinators in a variety of natural (Pickering & Stock 2003; Brunet 2009) and crop systems (Klein et al . 2007; Jauker et al . 2009; Rader et al . 2011). Hence, we focussed on Hymenoptera and Diptera, as the broader scope of our research was to quantify potential native pollination services for almond orchards that could complement current commercial European honeybee (Apis mellifera L.) stocks. The questions addressed in the following study are:  Is there a difference in the number of pollinator insects caught in different coloured pan traps?  Does pollinator insect abundance in each trap colour vary with habitat context?

MATERIALS AND METHODS Study area and system The study area is located in northwest Victoria, Australia, in the Victorian Murray Mallee Bioregion, specifically in the Wemen/Liparoo (hereafter Wemen) district and the Colignan/Nangiloc (hereafter Colignan) district (see Figure 2.1). The Murray Mallee bioregion is classified as semi-arid and annual rainfall is between 250-300 mm per year (Natural Resources & Environment n.d.). Sampling was conducted in 2010, during which the region recorded wetter than average rainfall. Approximately 528 mm fell in Wemen and 427 mm in Colignan. Average

165 temperatures for the months of sampling (July —September 2010) ranged from minimums of 5 —7oC to maximums of 15 —20 oC (Bureau of Meteorology 2011). One natural and one agricultural habitat were sampled – native mallee vegetation and commercial almond orchards respectively. Native vegetation sites were located within patches of remnant mallee vegetation that were greater than 50 m across and proximate to an almond orchard. Almond orchards were classified as either broadacre monoculture (MONO) orchards or plant-diverse (GRASS) orchards. MONO orchards are managed intensively and have completely bare ground across the entire orchard. They operate on a large, broadacre scale, occupying thousands of hectares of continuous land with small, infrequent patches of native vegetation interspersed between blocks of almond trees. GRASS orchards are managed with lower intensity and have a living carpet of grass and weedy ground cover across the orchard throughout the year. These orchards are situated within a heterogeneous agricultural landscape, with smaller block sizes (<1000 ha) interspersed among a variety of crops and land uses, including small, frequent patches of remnant vegetation.

Figure A1.1 The study region and sampling localities in northwest Victoria.

Sampling Insect assemblages were sampled within almond orchards and native mallee vegetation prior to almond flowering in July 2010, and again during the final stages of flowering in August —September 2010. Due to weather conditions and

166 logistics, the first sampling period was 5 days and the second was 10 days. Insects were sampled at each location using pan trap sets consisting of one blue, one yellow and one white bowl. Traps were made from plastic picnic bowls painted with UV-bright fluorescent blue or yellow paint or left white (the reflectance of each colour was not quantified in this study). Data for July (pre-flowering) were collected from 24 sets of pan traps in almond orchards and 10 sets of pan traps in native vegetation in Wemen and Colignan. Data for August-September (during flowering) were collected from 57 sets of pan traps in almond orchards and 43 sets of pan traps in native vegetation. During the whole sampling period, a total of 19 individual pans (4.7%), five blue, seven yellow and seven white, were lost from wind, weather or animal disturbance. Sampling locations were at least 200 metres apart and were chosen a priori from satellite maps to ensure a representative sample of the entire area of almond orchards and so that native vegetation sites were located proximate to an almond orchard. At all sites, sets of traps were placed on the ground, as close as possible to a flowering tree, and filled with 200 mL of water and a drop of dishwashing detergent to break surface tension. Very few studies advise at what height pan traps were placed, and there appears to be no consensus on whether traps should be mounted on poles (e.g. Tuell & Isaacs 2009) or placed on the ground itself (e.g. Abrahamczyk et al. 2010). In this study, we placed pan traps on the ground as the broader scope of the research was to investigate interactions between potential pollinators and ground cover in almond orchards. Traps were set out early in the morning (8 —10 am) prior to maximum invertebrate activity, and collected late in the afternoon just before nightfall (3.30 —6 pm). Traps were collected in the order they were placed to ensure that all traps were available to insects for a similar time. After collection, insects were removed from the pan traps and placed into containers filled with 70% ethanol, before being transported back to the laboratory. Insects were subsequently counted and identified to taxonomic order and morphospecies, and stored in ethanol in refrigeration.

Data analysis Data from both sampling periods were pooled and analyses were conducted using PAST 2.14 (Hammer et al. 2001). The data set is ecological count data, with mild 167 heteroscedasticity (Brown-Forsythe test, P = 0.052) and a positively-skewed distribution. Hence, we used non-parametric analysis on the untransformed data, as evidence is mounting that it is preferable not to transform these type of data (McArdle & Anderson 2004; O’Hara & Kotze 2010). Analyses wer e carried out on combined counts of Hymenoptera and Diptera (Total Insects) and on Total Diptera, Total Hymenoptera and subsets of Hymenoptera (European Honeybees, Native Bees, and Total Native Hymenoptera).

Trap colour and insect catches Friedman tests were performed to determine if a particular pan trap colour caught more individuals across all habitat contexts for the following groups: Total Insects, Total Hymenoptera, Total Diptera, Honeybees, Native Bees and Total Native Hymenoptera. Post hoc multiple comparisons were conducted using Wilcoxon signed rank tests for each trap colour pair (blue-yellow; yellow-white; white-blue) to determine which colour caught the most individuals within each pollinator insect group.

Habitat context and trap colour preference The influence of habitat context on trap captures was analysed using two test groups, Total Insects and Total Native Hymenoptera. Diptera were not included as a separate group as 88% of our total captures were dipterans and initial analyses on Total Diptera produced highly-similar results to analyses on Total Insects. Native bees were combined with wasps to form the Total Native Hymenoptera group, owing to the high number of zeroes in the Native Bee data set. In addition, habitat context analysis of the Native Bee data set produced nearly identical results to analysis of the Native Hymenoptera data set. Analyses were conducted on the proportion of insects caught in a particular trap colour across habitat contexts (native vegetation, MONO or GRASS orchards). Proportions for each test group were calculated by dividing the number of captures in a particular trap colour in a given habitat context by the total number of trap captures (across all colours) in that habitat. This accounts for the possibility that some habitat contexts may inherently support higher numbers of insects than other habitats. Chi-squared contingency table analyses were conducted to determine an overall effect and post-hoc pairwise comparisons were 168 calculated using the Marascuilo procedure (Marascuilo 1966) to determine significant differences between each habitat pair combination (Native-MONO, Native-GRASS, MONO-GRASS) for a given trap colour. For this procedure, the rejection of H 0 (that habitat has no influence on trap colour captures) occurs when 2 U' 0 > Χ 2,0.05 = 5.991.

RESULTS Trap colour and insect catches A total of 4060 Diptera and 436 Hymenoptera were captured in the study area. Yellow bowls caught the most insects, while blue and white attracted relatively equal numbers (Table A1.1). Results of Friedman tests revealed a statistically significant effect of trap colour on the total number of insects caught as well as on all individually-tested pollinator groups. Post-hoc pairwise comparisons showed that yellow traps caught significantly more insects than blue or white (Table A1.1). European honeybees were the only group that exhibited a significant preference for blue traps over white ( P = 0.035).

Habitat context and trap colour preference Total Insects Yellow traps caught the most pollinator insects in all habitats (Figure A1.2), although habitat context significantly influenced the proportion of insects caught in each trap colour (Table A1.2). Post-hoc comparisons showed that blue traps were significantly more attractive in Native habitat than in either almond orchard

(Marascuilo procedure, Native vs MONO U' 0 = 8.425, df = 2, P = 0.015; Native vs GRASS: U' 0 = 23.184, df = 2, P < 0.001). Conversely, white traps were signficantly less attractive in Native habitat than in either orchard type (Native vs

MONO: U' 0 = 6.517, df = 2, P = 0.038; Native vs GRASS: U' 0 = 7.631, df = 2, P = 0.022) (Table A1.2).

169 3588 White/Blue 238.5* 33 ~ 699.5 1073 3735 ) and ) and 2 χ 5050** Yellow/White 690** 268* ~ 1297* 2425** 5176** 5388** W Yellow/Blue 529* 337* ~ 1505* 2460* 4928** 2 24.609** χ 15.351** 19.043** ~ 7.112* 11.352** 16.112** = 134) and results from from ( results tests Friedman and 134) = colour 23 8 68 76 99 1158 1257 5 9.19 (1.26) 2, 9 White 88 57 64 121 209 1752 1961 7 14.63 (2.22) 2, 15 Yellow 64 128 1150 1278 4 9.54 (1.3) 2, 9 Trap Colour Blue 64 10 54 ) to test for differences between number of individuals caught in each trap colour. number) to test between for in each differences caught of individuals W Mean (SE) Median Mean : Pollinator insect abundance from pan trap collection in three habitat in three from contexts collection abundance pan (n trap : Pollinator insect Mean values indicate mean number of insects caught per trap of each trap of per colour.insectseach of caught indicate mean values number Mean * P 0.05 < 0.001** P < variable. on analysis not the associated the conducted that was ~ indicates Total Insects Insects Total Interquartiles Total Hymenoptera Total Diptera Total Other Native Hymenoptera Native Other Hymenoptera Native Total Honeybee Bee Native Pollinator Insect Insect Group Pollinator Table A1.1 Wilcoxon pairwise comparisons ( comparisons Wilcoxon pairwise 170 0.0133, 0.99 P = 0.27 1.1159, 0.57 P = White 0.33 0.6614, 0.72 P = 0.26 0.5578, 0.76 P = 0.56 14.7964, 0.0006 P = Yellow 0.31 2.856, 0.24 P = 0.48 NATIVE HYMENOPTERA NATIVE 0.004 15.609, P = Blue 0.36 1.0842, 0.58 P = 0.26 0.9812, 0.61 P = 0.17 9.3455, 0.01 P = White 0.24 6.5171, 0.04 P = 0.30 0.6932, 0.71 P = 0.28 7.6308, 0.02 P = pairwise comparisons (Marascuilo procedure) to determine whether habitat context whether (Marascuilo to determine pairwise comparisons procedure) post-hoc Yellow 0.41 0.1439, 0.93 P = 0.42 1.5233, 0.47 P = 0.45 4.6414, 0.01 P = 23.184, P<0.0001 0.35 8.4247, 0.02 P = 0.28 0.2629, 0.88 P = 0.27 TOTAL INSECTS 0.0001 27.491, P < Blue contingency table table analysis and contingency 2 Χ 2,0.05 2,0.05 2,0.05 2 2 2 Results of Native Χ Native GRASS Χ GRASS MONO Χ MONO 4,0.05 2 GRASS- MONO- GRASS MONO Native- Native Χ Table A1.2: influences insect captures in each coloured trap. Total Insect and Native Hymenoptera abundance in each trap is expressed colour abundance each in Hymenoptera and Native Total insect coloured trap. captures a proportion of the in each influences as Insect caught in each group habitat that individualstotal context. from 171 Figure A1.2 Total pollinator insects caught in each coloured pan trap expressed as a proportion of total individuals caught in each habitat context (Native, n = 53; MONO, n = 42; GRASS, n = 39). Native Hymenoptera Yellow traps caught more native hymenopteran insects in GRASS and MONO orchards than the other two colours, but native habitat had very little influence over the attractiveness of different trap colours (Figure A1.3). Post-hoc comparisons of each trap colour showed that blue traps were significantly more attractive in Native habitats than in GRASS orchards ( U' 0 = 9.346, df = 2, P = 0.009). Conversely, yellow traps were significantly more attractive in GRASS orchards than in Native habitats ( U' 0 = 14.79, df = 2, P = 0.0006).

Figure A1.3 Total Native Hymenoptera caught in each coloured pan trap expressed as a proportion of total individuals caught in each habitat context (Native, n = 53; MONO, n = 42; GRASS, n = 39).

172 DISCUSSION Overall, we caught the most insect individuals (for all groups) in yellow traps. Catches in blue and white traps were mostly similar across all insect groups, except for European honeybees, which were significantly more abundant in blue traps than in white. Habitat context significantly influenced pan trap catches. For all insect groups combined, blue traps caught more individuals in native habitats than in almond orchards, while white traps were more attractive within almond orchards. For native hymenopterans, yellow traps caught more individuals in GRASS orchards compared to native habitats, while blue traps were significantly more attractive in native habitats.

Trap Colour Pan trap surveys for pollinator insects highlight the wide variation in the attractiveness of particular colours to different species or groups (e.g. Laubertie et al. 2006; Cane et al. 2000; Gollan et al. 2011). Although most pan trap surveys have focussed on bees, the few studies that include other pollinator insects (e.g. flies, beetles, thrips etc.) have shown that one colour cannot be considered more attractive than others when targeting a wide range of taxa in different ecological contexts (Kirk 1984; Bowie et al. 1999; Vrdoljak & Samways 2012). UV blue is currently considered to be the most suitable colour to attract bees, perhaps because it has been noted previously that certain bee species in some habitats prefer to visit blue or violet flowers (Kevan 1983; Weiss 2001). Also, European honeybees have shown a preference for blue visual cues in some laboratory experiments (e.g. Horridge 2007) and some pan trap studies from North America have collected many more bee individuals in blue bowls than in other coloured bowls (Cane et al. 2000; Campbell & Hanula 2007). Blue vane traps (plastic jars with UV blue wing- like ‘vanes’ protruding from the mouth) have also had some success attracting wild bee species in North America (Stephen & Rao 2007; McKenney et al. 2009; Porter 2010) and have been recommended for bee collecting by Stephen and Rao (2005). Only one published Australian study has used blue vane traps to successfully sample native bees in an agricultural landscape in southern New South Wales (Lentini et al . 2012).However, this study did not use any other coloured traps or insect sampling methods, so it is still uncertain how effective blue vane traps are in Australian 173 ecosystems, relative to other trap types or colours. Other pan trapping surveys have collected more bees in white bowls than blue (Grundel et al. 2011) or equal numbers in blue and yellow bowls (Droege 2006) suggesting that bees do not show a consistent preference for trap colour in all habitat contexts. We collected a total of 682 more individuals in yellow traps than in blue traps and both European honeybees and native bees were caught more frequently in yellow traps, despite recommendations that the colour blue is more attractive to bees (e.g. Cane et al. 2000; Stephen & Rao 2005). Nevertheless, in our sampled habitats, honeybees were attracted to blue traps significantly more than native bee species – 37% of honeybees were caught in blue traps compared to only 13% of native bees. When compared with other Australian pan trap studies, our results support the hypothesis that trap colour preference among pollinator insects, including bees, is habitat-context specific. Gollan et al. ’s (2011) pan trap survey in New South Wales, which did not include blue bowls, caught more European honeybees in white traps compared to yellow traps. In an unpublished contemporaneous pan trap study of wet tropical rainforest and pasture systems in north Queensland, the majority of captured bees were caught in blue pan traps; however, the strength of colour preference varied among bee species, and across seasons (Smith T, 2012, pers. comm. 25 April). In another Australian study, which used five colours of sticky traps rather than bowls, Pickering and Stock (2003) sampled alpine pollinators in the Mt Kosciuszko area and collected the most insects in white traps. Hence, it is clear that variables other than trap colour per se affect which colour, or combination of colours, is most attractive to target insect groups in different habitat contexts.

Habitat context The published literature on pan trap studies covers a variety of habitat contexts, but a consistent preference for any one colour has not been determined. Results have shown either no difference in catch abundances across bowl colours (e.g. Toler et al. 2005; Wilson et al. 2008) or significant differences in catch abundances (e.g. Campbell & Hanula 2007; Abrahamczyk et al. 2010). These studies rarely correlate the pan trap data with ecological variables such as habitat context, floral resource, vegetation structure or canopy cover (but see Missa et al .

174 2009; Abrahamczyk et al. 2010), so it is still unclear if the wide variety of pan trap results is purely coincidental, or the result of an ecological interaction. The habitat contexts sampled in our study differ in a variety of ways. Almond plantations are managed as either bare-ground monocultures or as orchards with continuous weedy/grassy ground cover. Almond orchards also have a very different vegetation structure to the native mallee habitats. Although MONO and GRASS orchards differ in that the latter has extensive ground cover, they have essentially the same dimensional plant structure – all almond trees within a block grow at essentially the same height and there is no understorey vegetation. In contrast, native mallee habitats are more open than almond orchards and have multiple vegetation layers, including a sparsely vegetated ground layer and shrubs and trees ranging from two to twelve metres in height (Wood 1929). We found that native hymenopterans were caught in yellow traps more often than in blue or white traps within both MONO and GRASS orchards. However, at mallee vegetation sites, hymenopterans showed no significant preference for any colour. This could be because yellow traps were more visible to flying insects in the orchard habitats, as a result of the contrast between trap colour and the predominant colours of the surrounding habitat (i.e. green and white) (see Laubertie et al. 2006; Abrahamczyk et al . 2010). Almond trees are not native to Australia and produce white flowers, sometimes tinged with pale pink. Weedy species in the GRASS orchards produced mostly white and yellow flowers, but some other colours were observed, both within the orchard and in adjacent patches. Mallee sites were typical of Murray Mallee vegetation (Wood 1929), with a mixture of Eucalyptus , Callitris , Acacia , Senna , Casuarina or Allocasuarina species. From personal observation, the majority of flower-bearing plant species in these habitats produced yellow or white flowers at the time of trapping. Blue flowers were rarely observed at native mallee sites, yet blue traps caught significantly more insects in these locations than in either orchard type. The effect of local floral resources on the results of pan trap surveys has been discussed previously. There is evidence to support theories of co-evolution or adapted mutualisms between flowering plants and pollinators (Faegri & van der Pijl 1979; Kearns et al. 1998; Chittka & Thomson 2001; Pauw & Bond,2011), including between Australian Hymenoptera and native flowers (Dyer et al. 2012). 175 Although most pollinator insects are considered generalist foragers (Waser et al. 1996), it is also likely that the composition of local pollinator assemblages has evolved in response to ecosystem variables, such as habitat structure, plant diversity or predominant floral resource colour (Kevan et al. 2001; Pickering & Stock 2003; Wenninger & Inouye 2008; Bates et al. 2011). Therefore, such localised assemblages may respond to coloured traps differently depending on their habitat context. Very few studies have considered the interaction between pan trap colours and floral colour in the sampled habitat. Leong and Thorp (1999) sampled bee fauna in a flowering-vegetation patch dominated by white flowers and found that females of the host-specific bee species preferred white bowls matching the colour of their host plant, but other captured species preferred yellow bowls. Cane et al. (2000) collected more bees in blue rather than yellow bowls, even though yellow was the predominant flower colour at their study site, and Toler et al. (2005) found there was no effect of the ecosystem-dominant flower colour on pan trap catches of bees. An Australian study using six colours of pan traps found that local floral resource influenced pan trap catches of hoverflies - when traps were placed near yellow canola flowers, all hoverflies were caught in yellow traps, but when traps were placed near white nashi flowers, 20% of sampled hoverflies were caught in white bowls (Bowie et al. 1999). Although we did not statistically quantify floral resources in this study, we caught more total insects in white traps within both orchard types (where the dominant flower colour was white) compared to native sites. In contrast, hymenopterans showed a significant preference for yellow traps in GRASS orchards compared to native sites, indicating that the dominant flower colour in those orchards did not influence this taxonomic group. Previous work considers some wasp species to see and respond to colours differently to bees (Faegri & van der Pijl 1979; Peitsch et al. 1992; Lunau & Maier 1995; but see also Desouhant et al. 2010), and an insect’s functional role may also affect its response to colour, with different responses even appearing within the same taxonomic family (Kirk 1984). Most experimental research into insect colour vision has been conducted under controlled, laboratory conditions (e.g. Horridge 2007; Desouhant et al. 2010) using European honeybees or bumblebees (Lunau & Maier 1995; Kevan et al. 2001), both of which are native to 176 the northern hemisphere. Although these studies put much effort into designing a variety of response contexts, these conditions can never emulate actual environmental conditions across different habitats. Colour has multiple aspects, such as intensity, wavelength, contrast, or spectral purity, which will cause insects to respond differently depending on the behavioural context (e.g. foraging, initial flower detection etc.) (Lunau & Maier 1995) or the environmental characteristics of the habitat itself, and these values must not be considered as isolated variables (Menzel & Shmida 1993; Kevan et al. 2001). For example, light has been shown to affect plant pollination, as insects are less likely to visit shaded flowers compared to sunlit flowers (Kilkenny & Galloway 2008). Light itself is also affected by numerous atmospheric factors such as time of day, cloud cover, latitude or even hemisphere (Kevan 1983). Radiation at ground level was not measured in this study, but light levels varied between ‘interior’ sites (in the centre of almond orchard blocks) and more open sites (edges of orchards or native vegetation). We also conducted this study during late winter at latitude 34° S, both factors that will affect sunlight radiation and levels of ambient light. Hence, it is likely that these variables would have influenced the attractiveness of each pan trap colour in this study, and further comparison across other seasons would confirm how significant such an effect may be.

Conclusions and recommendations Our results highlight the importance of considering the habitat context of individual pan trapping surveys. Insects respond to colour differently across environmental contexts and within taxonomic groups. Previous research has shown that trap placement can interact with habitat context when attempting to attract target insects in pan trapping surveys (Missa et al. 2009; Abrahamczyk et al . 2010; Droege et al. 2010) and there is still much information to be gained on the influence of habitat context on trapping success. No one trap colour is likely to be suitable for trapping pollinator insects across all habitat contexts. Rather, the most efficient use of pan trapping should consider relevant environmental variables (e.g. vegetation structure), include clusters of multiple colours, and address the potential for complementary collection methods (e.g. sweep netting). Nevertheless, pan traps have immense 177 potential as a cheap, efficient collection method, especially for community- or school-based science programs, and more sampling of this kind will provide valuable ecological knowledge of insect diversity in different habitats, particularly in the southern hemisphere.

ACKNOWLEDGEMENTS

Thank you to Select Harvests and Manna Farms for allowing access to their almond orchards; James Abell and Katrina Lumb for field assistance; MM Mayfield, T Smith and two anonymous reviewers for valuable comments on the draft manuscript; and GM Gurr for helpful guidance and encouragement during planning of the project. The Institute for Land Water & Society at Charles Sturt University provided a postgraduate scholarship to undertake the research project.

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182 5 15 17 Native 43) (n = 8 MONO 30) (n = 4 2 individual on almond at flower Fly seen Wemenwith pollen plantation to leg. attached grains 2 1 other and smallHoneyeaters birds feeders on occasional almond also flowers. GRASS 27) (n = 38 3 random walks within 10 metres of each trap location, for a 10 minute period; European honeybees all random honeybees at observed trap 10 minutewere within each period; location, a walks of 10 metres European for – Observations of insects visiting open flowers at almond and mallee sites, August-September 2010. Sites are grouped by vegetation type type vegetation mallee sites, August-September almond and 2010. flowers Sites at grouped are visiting open by of insects Observations vegetation types, but types, not recorded vegetation N.B. Data collection Data collection N.B. Site notes Lepidoptera (unidentified) Lepidoptera Hymenoptera wasps) (all Hymenoptera Diptera: Syrphidae Diptera: Formicidae Hymenoptera: Diptera (unidentified) Diptera Table A2.1 vegetation). = monoculture Native mallee almond; = plant-diverse MONO almond; (GRASS = Appendix 2: Taxonomic lists of flower visitor observations and lists ofobservations Appendix samples visitor trap flower Taxonomic 2: 183 Representative photographs of Diptera, Lepidoptera and Hymenoptera individuals Hymenoptera and flowers in observed of Diptera, visiting RepresentativeLepidoptera almond photographs plantations (top row) and native vegetation (bottom row). See Figure 2.2 on page 13 for native 2.2 on page 13 for and vegetation of pollinators See additionalrow) (bottom vegetationFigure at native row). sites. photographs Figure A2.1 Figure 184 45 Liparoo Liparoo (n =15) 11 MONO Wemen (n =15) 61 3 1 NL (Liparoo) NL 15) (n = 10 15 2 2 NV (Wemen) NV (n =15) 2 Native NC (Colignan) (n =13) 3 10 4 13 2 Colignan C Colignan (n =10) 12 49 Colignan B Colignan (n =9) 23 7 2 2 Nangiloc (n =8) 10 5 GRASS Total abundance of European honeybees, native bees (species), wasps and flies (family groups) collected almond at and honeybees, collected (family European of groups) (GRASS, bees MONO) and flies abundance (species), native Total wasps L. (C.) brunnesetum L. (C.) Lasioglossum (Chilalictus) athrix Lasioglossum (Chilalictus) H. urbanus Total native bees nativeTotal bees sphecoides Homalictus Apis mellifera HYMENOPTERA Table A2.2 Native mallee vegetation mallee sites, August-SeptemberNative 3 4). 2010 (Chapters & 185 2 1 1 5 3 1 1 3 22 10 2 4 1 2 1 Lipotriches spp. L. (C.) spp. L. (C.) spp. Leioproctus L. (C.) victoriae L. (C.) L. (C.) helichrysi L. (C.) L. (C.) globosum L. (C.) L. (C.) callophyllae L. (C.) erythrurum L. (C.) 186 4 10 1 1 6 19 7 5 1 9 1 1 1 1 1 16 11 14 1 1 2 32 3 1 2 1 12 1 20 4 12 2 10 1 1 6 24 Ichneumonidae Diapriidae Evaniidae Crabronidae Chalcididae Ceraphronidae Bethylidae Braconidae Total waspsTotal 187 1 3 3 1 2 1 1 1 1 1 2 1 3 1 1 7 1 3 1 1 1 2 3 1 Trichogrammatidae Tiphiidae Sphecidae Scelionidae Proctotrupidae Pteromalidae Platygastridae Perilampidae Mymaridae Megaspilidae 188 6 4 4 21 4 19 207 15 14 11 3 4 7 1 11 139 44 15 1 1 3 18 2 1 139 3 12 3 9 3 11 4 10 138 10 2 10 7 2 1 274 11 1 17 4 12 5 8 6 10 201 1 14 8 7 19 20 27 16 24 968 79 26 35 29 231 1 64 2 17 238 57 6 29 1185 Ephydridae Heleomyzidae Drosophilidae Dolichopodidae Cryptochetidae Chironomidae Chloropidae Ceratopogonidae Calliphoridae Bombyliidae DIPTERA (total) DIPTERA Asilidae 189 1 104 3 10 1 27 1 12 2 8 59 6 1 11 6 3 39 7 1 11 1 10 1 10 5 3 2 190 3 27 33 5 21 3 85 2 1 1 3 429 5 689 1 3 1 1 1 75 Tephritidae Syrphidae Tachinidae Stratiomyidae Sepsidae Sarcophagidae Pipunculidae Platypezidae Phoridae Muscidae Milichiidae Hybotidae 190 1 1 1 2 2 Trichoceridae Therevidae 191 81 78 3 63 Mallee 18) (n = 86 79 1 6 128 8 September Almond 50) (n = 2 4 3 1 43 Mallee (n = 18) 44 1 August Almond 50) (n = 3 28 1 Mallee 18) (n = 5 30 July Almond 50) (n = 1 Total abundance of European honeybees, native bees (species), wasps and flies (family groups) collected at monoculture almond plantation almond monoculture at honeybees, collected (family European groups) of and flies bees (species), abundance native wasps Total Bethylidae Homalictus urbanus Homalictus WaspsTotal Symphyta) (inc. L (Ch.) erythrurum L (Ch.) Lasioglossum (chilalictus) spp. Lasioglossum (chilalictus) Total Native Bee Native Total HYMENOPTERA Apis mellifera Table A2.3 and native mallee vegetation 5 mallee 6). sites, native 2011 July-September (Chapters & and 192 1 4 1 1 4 2 4 24 13 2 2 9 8 2 2 4 2 3 3 2 5 3 1 1 1 3 2 4 6 1 Figitidae Evaniidae Eurytomidae Diapriidae Eulophidae Cynipidae Crabronidae Chalcididae Braconidae Ceraphronidae 193 10 4 1 8 2 1 17 3 14 15 10 2 7 1 6 5 3 3 19 4 4 10 8 1 3 4 1 5 8 1 2 1 11 Pteromalidae Scelionidae Proctotrupidae Pompilidae Platygastridae Pergidae Perilampidae Mymaridae Megaspilidae Ichneumonidae Gasteruptiidae 194 5 2 16 17 12 1 1 112 15 30 5 65 157 363 68 60 1 1 974 6 14 6 9 7 13 2 179 9 64 14 847 1 18 52 111 204 2 1 5 125 25 7 7 3 12 13 157 4 30 5 7 Drosophilidae Heleomyzidae Dolichopodidae Chloropidae Chironomidae Calliphoridae Ceratopogonidae Asilidae DIPTERA (total) DIPTERA Sphecidae Tiphiidae 195 3 38 1 1 2 114 1 11 2 11 69 4 8 1 2 80 14 18 6 55 54 2 10 105 5 5 145 2 2 3 6 1 68 4 33 3 45 1 Unidentified Acalyptrata Unidentified Brachycera Unidentified Therevidae Tephritidae Tachinidae Sepsidae Syrphidae Sarcophagidae Platypezidae Phoridae Milichiidae Muscidae 196 i w 0.37 0.17 0.16 0.13 0.07 0.06 0.02 0.02 c Δ AIC 0 1.5 1.6 2.1 3.4 3.8 5.6 5.7 c AIC 325.5 327.1 327.1 327.6 328.9 329.3 331.1 331.3 ) for each model indicate the probability the that the model indicate ) for each given i w pollinator relationships (Chapter 3)pollinator relationships (Chapter — Model -0.14(-0.11, to Constant only -0.09) 0 to 0) D (0, PR to 0.007) -0.02 (-0.005, to 0.001) GC (0, -0.001 0) 0 to D (0, PR to 0.008) + -0.02 (-0.005, to 0) 0 to 0.001) +GC (0, -0.001 D (0, 0) 0 to PR*D 0 to 0) D (0, + (0, PR to 0.02) + -0.02 (-0.003, GC*D to 0) + 0 to 0.001) +(0,GC (0, -0.001 D (0, 0 to 0) Abundance Parameter estimates with confidence intervals from negative binomial regression (abundance) and linear regression (richness) linear regressionand negative intervalsbinomial (abundance) from (richness) regression estimates with confidence analysis Parameter Fly Table A3.1 ( insect groups andcover weights variables. the relationship ground between Akaike testing response Insect Appendix 3: Full set of models for analyses ofmodels ground of cover Appendixvegetation set Full 3: model is the best fit for the data. fit for model is the best 197 0.43 0.34 0.12 0.04 0.25 0.23 0.20 0.10 0.10 0.07 0.03 0.03 0 0.4 2.6 4.6 0 0.2 0.5 1.8 1.9 2.4 4.1 4.6 201.9 202.3 204.5 206.5 107.9 108.1 108.4 109.7 109.8 110.3 111.9 112.5 GC (0.006, 0.003 to 0.01) D (0.001, + to 0.002) GC (0.007, 0.003 to 0.01) -0 D to 0.003) 0 to 0) + -0.001 GC (0.01, 0 to 0.01) (0.001, GC*D (0, + 0 to 0.002) PR D (0.001, 0.04 to 0.19) + (0.12, GC (-0.004, -0.01 to 0.004) + D (0, 0 to 0) to 0.004) GC*D -0.01 D (0, GC (-0.004, + 0 to 0) + (0, GC (-0.003, -0.007 to 0.001) -0.007 GC (-0.003, 1.18(1.33, to 1.49) Constant only to 0.05) PR -0.15 (-0.06, 0 to 0) D (0, to 0.002) -0.007 (0, D GC (-0.003, to 0.001) + -0.001 PR (-0.09, -0.22 to 0.07) + D (0, -0.002 to 0.001) -0.002 PR*D to 0) + D (0, (0, -0.001 PR to 0.07) + -0.22 (-0.09, PR (-0.06, -0.15 to 0.05) + D (0, -0.001 to 0) -0.001 D (0, PR to 0.05) + -0.15 (-0.06, Abundance Richness Wasp 198 0.60 0.04 0.03 0.31 0.26 0.13 0.11 0.09 0.05 0.05 0.03 0 4.9 5.5 0 0.4 1.8 2.0 2.6 3.8 3.9 4.8 88.0 206.8 207.5 127.9 128.3 129.7 129.9 130.5 131.7 131.8 132.7 PR 0.52 to 1.8) (1.18, GC (0.006, to 0.01) 0.001 0 to 0.002) D (0.001, + to 0.01) GC (0.006, 0.002 PR 0.008 to 0.22) (0.12, to 0.003) 0 D (0.001, PR 0.02 to 0.23) + (0.13, 0 to 0) GC*D (0, D (0.001, to 0.003) + to 0.01) + -0.002 GC (0.005, -0.003 PRto 0.17) 0.03 (0.10, to 0.002) -0.003 PR*D 0 to 0.001) + D (0, (0, PR to 0.19) + (0.06, -0.065 PR (0.08, -0.09 to 0.02) + D (0, -0.002 to -0.002 D (0, PR to 0.02) + (0.08, -0.09 0.003) PR*D to -0.001 + (0, 0.001) (0.87, 0.68 to 1.1) Int to 0.002) -0.001 D (0.001, Abundance Richness Native bee Native 199 0.20 0.08 0.07 0.04 0.01 2.2 4.0 4.4 5.6 7.9 90.2 92.0 92.4 93.6 95.9 GC (0.04, -0.01 to 0.09) + D (-0.004, 0 to 0) to 0.09) GC*D (0, GC (0.04, -0.01 D (-0.004, + to 0.01) + -0.02 PR (1.2, 0.49 to 1.8) + D (0, -0.006 to 0.005) -0.006 PR D (0, to 1.8) + 0.49 (1.2, 0.07) GC (0.05, 0.02 to to 0.01) -0.02 PR*D to -0.004 + (0.001, D (-0.004, PR to 2.0) + (0.98, -0.02 0.007) to 0.003) +-0.008 GC (0.04, 0.02 to 0.07) D (-0.003,

200 0.54 0.27 0.17 0.01 0.006 0.006 0 1.4 2.4 7.6 8.9 9.0 72.2 73.6 74.6 79.8 81.1 81.2 GC (0.007, 0.004 to 0.01) 0.004 to 0.01) GC (0.007, 0 to 0.001) D (0, + 0.005 to 0.01) GC (0.007, + 0.01) -0.001GC (0.005, 0 to D (0, 0 to 0) to 0.001) + GC*D (0, PRto 0.21) 0.07 (0.14, 0 to 0.001) D (0, PRto 0.21) + 0.07 (0.14, to 0.001) -0.002 o to 0.001) PR*D D (0, + (0, PR to 0.19) + (0.08, -0.02 Richness GC = percent ground cover; PR = plant richness; D = distance to natural or distance cover; semi-natural PR plant richness; parameter. constant vegetation.= D = as a All percent the models include GC = ground 201 Management History Management 2001, conventional established broadacre orchard only) (herbicide monoculture 1990 established fauna and reserve, flora State 1988, conventional established broadacre orchard only) (herbicide monoculture mallee history remnant, unknown unmanaged Patch Size (ha) Size Patch 1002 35283.5 165 210 Remnant mallee woodland mallee Remnant Commercial almond orchard almond Commercial woodland mallee Regenerated orchard almond Commercial Type Physical and historical attributes of the studied ecotone patches. studied ecotone of the and attributes historical Physical Collins Wemen Annuello Table A4.1 Name Liparoo Appendix 4: Location attributes and pollinator occurrence along gradients ecotone 5) and occurrence pollinator Appendix (Chapter 4: Location attributes 202 S J,S 300 A,S A J A,S 100 S Wemen ecotones. The Wemen ecotones. — S S 70 S Liparoo and (b) Collins (b) and Liparoo J,A 40 A — 10 S S J S -10 A A,S J,A,S Liparoo ecotone (m) Liparoo ecotone — -40 A S (a) Distance alongAnnuello Distance (a) -300 Monthly occurrence of Hymenoptera and Diptera Annuello the (a) along families Diptera occurrence of Hymenoptera and Monthly unique to the specified location (i.e. only collected at either Annuello/Liparoo or Collins/Wemen). Annuello/Liparoolocationor only collected either (i.e. at the specified unique to shaded portion of the table represents the mallee side of the boundary. J = July, A = August, S = September. Bold family names indicate the family was family the names family indicate the mallee Bold side A = S the table September. JAugust, boundary. the of represents portion = July, of shaded = Cynipidae Diapriidae Crabronidae Ceraphronidae Braconidae HYMENOPTERA Bethylidae Family Table A4.2 203 S J,S A,S S S S A,S A,S A S A J,S S S A S J,S S A,S J,A,S A S S A A,S S S S A S J A,S J,A,S S S S S J,A J,S S A S J A,S J S A,S S S Pteromalidae Scelionidae Proctotrupidae Pompilidae Platygastridae Mymaridae Perilampidae Megaspilidae Ichneumonidae Halictidae Eulophidae Figitidae 204 J,A,S J,A,S J,A S S J,A,S J,A,S A,S A A A A J,S A,S J,A,S A,S A,S A A J,S A S S S A,S A,S J,A,S J,A J,A,S A A,S A,S J,A,S A A A,S S A,S J,S J,A,S J,A,S S S A A,S A,S A S J J,A,S J A,S J,A,S A J J,S J,A S A S J,A S Muscidae Heleomyzidae Milichiidae Drosophilidae Dolichopodidae Chloropidae Calliphoridae Chironomidae Asilidae DIPTERA Tiphiidae Sphecidae 205 J,A,S S S J,A,S J,A,S J,A,S J J,A,S J,A,S J S J,A,S J,A J A A A J,A,S A S J,A,S J,A J,A A A,S J,A,S A A A J S J,A,S S A,S A un. Acalyp = unidentified Acalyptrata; un. LBrach = unidentified un. Brachycera unidentified = unidentifiedLBrach Lower = Acalyptrata; un. Acalyp un. LBrach un. Acalyp Tachinidae Tephritidae Syrphidae Sepsidae Sarcophagidae Phoridae Platypezidae 206 S S 300 A J,S J,S 100 J,S S S 70 S S J,A,S J A,S 40 S J S J,A S 10 Wemen ecotone (m) ecotone Wemen J S S J -10 — J A S A -40 S S S (b) Distance along Collins (b) Distance -300 S Diapriidae Eulophidae Cynipidae Crabronidae Chalcididae Braconidae Ceraphronidae Bethylidae HYMENOPTERA Family 207 S S J,S S S A,S A S S S S A S S A,S S S A,S S A,S S S S S S A A S A S J,S S A S J S A,S J A J A S S S A Pompilidae Proctotrupidae Platygastridae Pergidae Mymaridae Ichneumonidae Megaspilidae Halictidae Gasteruptiidae Figitidae Eurytomidae Evaniidae 208 A,S A,S S S S A,S J,A,S S J,A,S A,S A,S J,A,S S A,S A A,S S J,A,S A,S A,S J,A,S A,S J,A,S S J,A,S S J,A,S A,S J,A,S A,S J,A,S A,S A S A,S A,S J,A,S J,A,S J,A,S J,A,S S J,A A A J,S J,A,S J,A,S J J,S J,S J,S J,A J,A A,S S J,A,S S J,A J A A J,A,S J,S J,S S J,A,S Heleomyzidae Dolichopodidae Drosophilidae Chloropidae Chironomidae Ceratopogonidae Asilidae Calliphoridae DIPTERA Sphecidae Scelionidae Pteromalidae 209 A A A J,S J,A,S S J,A,S A A A A A S J J,A,S A,S J,A,S A A A J, S J,A,S J,A,S A A A J,A,S J,A A S J,A,S S J,A,S A A,S S S J,A,S A A S A A J,A J,A,S S A J,A,S A,S A A A A J,A,S S un. LBrach Therevidae un. Acalyp Tephritidae Tachinidae Syrphidae Sarcophagidae Sepsidae Platypezidae Phoridae Muscidae Milichiidae 210 unnamed patch unnamed – 6.6 m SE 0.13 m 5-6 m 5-6 5 m Wemen 142.679° -34.751° 1988 165 210 Annuello Flora and Fauna Reserve Fauna and Flora Annuello – 5.3 m SE 0.17 m -34.818° 142.576° -34.818° 2001 1,002 35,000 m 5-6 5 m Liparoo Location and physical almond of the plantations.attributes and monoculture sampled physical Location Row width spacing Tree Average height of almond height trees Average Area of adjacent mallee (ha) of mallee patch adjacent Area Area (ha) Area Location established Year Table A5.1 Appendix 5: Physical attributes and full candidate and relationships 6) ofsets full structure attributes for candidate Appendix analysis habitat models 5: Physical of (Chapter 211 i w 0.41 0.21 0.16 0.09 0.06 0.05 0.03 ) i (g L 1 0.51 0.39 0.21 0.14 0.11 0.08 c AIC i Δ 0 1.36 1.87 3.11 3.88 4.36 5.15 ≤ 10) explaining relationships between habitat attributes and monthly wild pollinator abundance (A) wild pollinator abundance attributes monthly habitat and relationships≤ 10) explaining between c AIC i Veg (0.47; 1.2) + 1.2) + (49.04; (0.47; (-15.65;GC 17.1) Veg*GC Veg 9.8) + (-19.6; 2.2) + Veg*LL 9.6) (1.64; (-13.81;GC (16.78; 14.0) Veg*GC (49.19; 18.9) + Veg + 9.7) + LL (11.23; 5.6) + GC (15.5; 8.4) LL 1.2) (3.72; Veg GC (26.49; 9.3) 2.4) + (-16.11; (16.68;Veg*LL (4.26; + 9.8) Veg 14.1) LL (16.21; 5.5) LL Model The candidate set of models (Δ models set of candidate The Month July Response (A) Fly Table A5.2 or richness (R) across all almond plantation and mallee woodlands sites (n sites (n 68). woodlands = (R) mallee and plantation almond all across richness or 212 0.28 0.24 0.22 0.11 0.05 0.04 0.04 0.03 0.35 0.29 0.18 0.14 0.03 1 0.86 0.80 0.41 0.17 0.16 0.13 0.11 1 0.83 0.51 0.40 0.10 0 0.31 0.46 1.81 3.61 3.73 4.03 4.39 0 0.38 1.34 1.84 4.71 Veg (-0.76; 0.2) + GC (5.56; 2.6) + Veg*GC (-7.37; 0.2) GC (5.56; (-0.76; Veg*GC + + 2.6) Veg 3.0) Veg (-6.78; 2.2) (-6.78; Veg (-28.99;8.2) LL + 4.3) (-94.53; (72.76; Veg*LL (0.63; 50.1) + Veg 51.8) LL 16.5) + (-25.65;LL GC (-17.36; 9.2) 1.2) (15.27; Constant only 12.9) GC (-26.07; (-38.03; 2.8) (30.15; GC (-5.72; Veg*GC + 61.0) + Veg 62.9) 5.2) + (2.67; (41.08;GC 47.8) (74.44; Veg Veg*GC 59.6) + (-54.77; (-97.67; Veg*LL + + LL 62.78) 49.8) Veg (-1.32; 0.3) + LL (3.07; 1.9) + Veg*LL 0.3) (-1.06; (3.07; Veg*LL (-1.32; 1.9) + + Veg 2.2) LL 0.2) (-0.99; Veg 0.9) (2.63; (1.39; 1.3) 0.3) GC CC (-1.54; 0.5) + + + (0.12; LL Veg (-1.52; 0.5) Veg*CC CC (-0.51; 0.5) + 1.4) + (0.29; Veg September August

213 0.30 0.19 0.16 0.13 0.10 0.06 0.03 0.03 1 0.62 0.53 0.42 0.33 0.20 0.10 0.09 0 0.96 1.26 1.72 2.22 3.21 4.52 4.86 Veg (0.02; 0.3) + LL (1.85; 2.2) + Veg*LL 0.3) + (-0.62; (1.85; (0.02; Veg*LL 2.2) + Veg LL 2.5) Veg (-0.46; 0.4) + GC (-4.45; 3.2) + LL (1.56; 2.2) + Veg*GC (7.08; 3.5) + Veg*LL 0.4) 2.2) (1.56; GC (-4.45; Veg*GC 2.5) (-0.46; (7.08; 3.5) (0.48; + + Veg*LL + Veg 3.2) + LL LL (1.27; 0.7) (1.27; LL 0.1) (1.50; Constant only (1.18; 0.7) + GC (0.69; 1.1) LL 0.2) (0.21; Veg GC (1.10; 1.1) 0.2) + (6.23; (0.01; GC (-4.67; Veg*GC 3.6) Veg + 3.2) July (R) 214 0.29 0.19 0.15 0.11 0.10 0.07 0.05 0.04 1 0.65 0.50 0.37 0.34 0.22 0.17 0.15 0 0.88 1.39 1.97 2.13 3.01 3.57 3.84 Constant only 0.1) (2.12; Constant only (7.96; 0.2) (-7.84; GC (-0.13; 3.5) Veg*GC + 3.2) + Veg 1.1) GC (-1.01; (-0.34; 0.7) LL 0.2) (-0.04; Veg (6.84; 3.5) (-3.88; 0.4) + 2.1) (0.36; 2.5) GC (-7.51; (2.37; + Veg*GC + Veg*LL Veg + 3.1) LL GC (-0.94; 1.2) + LL 1.2) + (-0.21;LL GC (-0.94; 0.7) 0.3) + (2.86; (0.35; Veg LL (-4.16; Veg*LL + 2.5) 2.2) August

215 0.26 0.12 0.12 0.07 0.07 0.07 0.06 0.06 0.05 0.05 0.03 0.03 0.01 1 0.46 0.45 0.29 0.29 0.28 0.24 0.23 0.19 0.18 0.11 0.11 0.04 0 1.54 1.58 2.49 2.49 2.56 2.86 2.96 3.33 3.43 4.36 4.46 6.40 Veg (-0.50; 0.4) CC (-0.50; (-1.12;+ (0.19;Veg Veg*CC 1.2) 0.5) + 1.0) GC (-0.91; 1.0) + (0.89;LL GC (-1.22; 0.7) 0.2) (-0.09; Veg 0.8) (0.47; CC (-0.25; + 0.4) LL 0.7) (0.49; GC (-1.61; CC 0.4) + + 1.1) (-0.45; LL (-3.45; 0.2) + (0.07; GC (1.99; Veg*GC Veg + 2.9) 3.2) Veg (-0.8; 0.3) 0.3) CC 0.9) (-0.8; (2.06; + (-1.05; 1.2) GC (0.19; Veg 0.5) + + LL 2.2) 0.3) (2.47; (0.12; Veg*LL (-0.63; 1.9) + + Veg LL 0.1) (1.92; only Constant CC 0.3) (-0.38; 0.6) (0.73; LL CC (-0.59; 1.1) + GC (-1.60; 0.4) September

216 0.30 0.29 0.15 0.10 0.08 0.04 0.04 0.01 1 0.98 0.49 0.34 0.27 0.14 0.12 0.02 0 0.04 1.43 2.18 2.65 3.89 4.23 8.30 LL (4.45; 2.0) (4.45; LL 0.4) (0.96; Veg (3.9; + 1.9) GC (2.57; 2.7) LL 0.2) (0.85; Constant only GC (4.61; 3.4) 0.7) + (2.39; (0.43; 5.5) (0.58; Veg*LL Veg LL + 4.2) (-3.65; 0.7) + (1.07; GC (2.81; Veg*GC Veg + 4.9) 6.9) (-1.25; 3.9) 1.0) + (2.36; 5.5) (0.15; Veg*GC GC (2.88; (1.36; + Veg + 4.9) 6.6) + LL Veg*LL July Wasp (A) 217 0.28 0.26 0.23 0.09 0.09 0.03 0.01 0.004 1 0.93 0.83 0.33 0.33 0.12 0.05 0.02 0 0.15 0.37 2.19 2.23 4.21 6.18 8.28 GC (1.95; 0.7) 0.3) + (0.26;(-1.89; Veg*LL (0.71; 2.9) + Veg LL 3.1) 0.2) (0.41; Veg (-0.01; + GC (1.95; 0.7) LL 0.7) (-0.71; 0.2) + (0.30; GC (1.91; Veg*GC Veg + 3.5) 3.5) 0.3) + (0.67; (-1.64;GC (2.09; 3.0) (-1.91; 3.2) (0.44; Veg + Veg*GC 3.6) 3.7) + LL + Veg*LL 0.1) (-0.47; Constant only 0.6) (0.11; LL August

218 0.19 0.14 0.13 0.12 0.09 0.08 0.06 0.05 0.04 0.03 0.03 0.02 0.02 1 0.73 0.69 0.62 0.49 0.40 0.31 0.28 0.23 0.15 0.14 0.12 0.09 0 0.63 0.74 0.94 1.44 1.81 2.36 2.54 2.94 3.79 3.92 4.17 4.93 Veg (-0.01; 0.3) CC (-0.01; + (-0.10; (0.39;Veg Veg*CC 0.7) 0.4) + Veg (0.29; 0.2) + CC (0.003; (-0.09; 0.2) + GC 0.7) (-1.13; (0.29; 0.4) + + 0.6) Veg LL Constant only 0.1) (-0.49; only Constant + 0.2) (-1.95; (0.54; Veg*LL (0.38; 1.5) + Veg LL 1.7) 0.1) (0.12; Veg GC (0.66; 0.6) CC 0.2) (-0.19; (-0.27; 0.5) LL CC (-0.59; (-0.34; 0.5) + 0.3) LL (-0.38; + GC (0.73; 0.5) LL 0.5) CC + (-0.11;GC (0.55; 0.6) 0.2) (3.05; (0.04; 0.1) + GC (-2.43; Veg*GC 3.1) Veg + 2.9) 0.5) (-0.61; GC (0.51; CC 0.3) + + 0.6) (-0.27; LL September

219 0.53 0.14 0.14 0.07 0.05 0.04 0.03 1 0.27 0.26 0.14 0.10 0.08 0.05 0 2.61 2.69 3.99 4.64 5.08 5.83 2.61; 2.3) - 3.67; 3.3) + Veg*LL ( 3.67; 3.3) + Veg*LL - 3.76; 3.3) - 2.72; 2.3) - GC (2.36; 1.0) + LL (1.93; 0.7) + GC (2.36; 1.0) LL 0.2) (0.54; Veg 0.7) (2.26; LL 0.3) + (3.45;( Veg*LL (0.52; 2.1) + Veg LL Veg (0.51; 0.2) + ( 0.2) + (0.51; GC (4.56; Veg*GC Veg + 3.0) GC (3.03; 1.1) Veg (0.36; 0.4) + ( 2.0) 0.4) + (3.78; (0.36; Veg*GC GC (5.08; + Veg + 2.9) LL July (R) 220 0.41 0.18 0.17 0.14 0.06 0.03 0.02 0.004 1 0.43 0.40 0.34 0.14 0.07 0.06 0.01 0 1.71 1.82 2.18 3.90 5.40 5.75 9.49 Veg (0.49; 0.4) + (-3.31; 2.3) 0.4) + (0.65; 2.6) (0.49; Veg*GC GC (2.77; (-1.96; + Veg + 3.3) 3.7) + LL Veg*LL Constant only 0.1) (0.64; Constant only GC (0.79; 1.1) 0.2) (0.10; Veg (-0.07; 0.7) LL (-0.19; + GC (0.85; 1.2) LL 0.7) 0.3) + (-1.62; (0.47; (0.36; Veg*LL 2.3) + Veg LL 2.5) 0.2) + (-2.54; (0.12; GC (2.68; Veg*GC Veg + 3.3) 3.6) August

221 0.27 0.14 0.10 0.10 0.10 0.08 0.05 0.05 0.04 0.03 0.02 0.02 0.01 1 0.53 0.37 0.36 0.34 0.31 0.19 0.17 0.14 0.12 0.07 0.06 0.04 0 1.27 1.98 2.07 2.19 2.36 3.30 3.51 3.92 4.24 5.44 5.62 6.39 GC (-1.44; 1.3) + CC (-0.21; 1.3) + GC (-1.44; 0.5) 1.3) + (-0.11;LL GC (-1.15; 0.8) (-1.90; 1.2) 0.4) + (0.76; GC (-3.14; CC 0.6) + Veg (0.24; + LL 1.6) Veg (0.28; 0.4) + LL (-1.10; 2.5) + Veg*LL 0.4) + (-0.02; (-1.10; Veg*LL (0.28; 2.5) + 2.8) Veg LL CC (0.28; 0.6) + Veg*CC (0.25; 0.7) + (-0.28; 1.5) Veg LL (-0.42; 0.9) + CC (-0.42; (-0.15; 0.9) + 0.5) LL 0.9) (-0.40; GC (-1.43; CC 0.5) + + 1.3) (-0.33; LL Constant only 0.1) (1.20; only Constant GC1.2) (-1.19; 0.2) (0.09; Veg (-0.27; 0.8) LL CC 0.4) (-0.03; 0.2) + (5.44; (0.14; (-6.74;GC 3.9) Veg*GC Veg 3.5) + September

222 0.37 0.23 0.23 0.13 0.04 0.008 1 0.63 0.62 0.34 0.12 0.02 0 0.94 0.97 2.16 4.27 7.60 Veg (0.78; 0.6) + CC(-3.89; 2.9) (-5.58; 0.6) + GC (-6.61; (0.78; 2.1) 1.1) + + Veg LL CC (-4.72; (-4.74; 1.8) + 0.9) LL CC (-4.11; 2.8) + GC (-6.43; 0.8) CC 0.9) (-3.94; CC (-4.44; 1.1) + (-1.82;Veg*CC (0.03; 3.1) + 1.4) Veg CC (-4.83; 0.9) + LL (-4.05; GC 1.8) + CC 0.9) + LL (-4.83; 2.7) (-5.45; September Bee (A) Bee 223 = Akaike weight for the model for weight Akaike = i w 0.21 0.15 0.14 0.10 0.10 0.06 0.06 0.05 0.05 0.03 0.03 0.02 0.007 1 0.73 0.66 0.50 0.48 0.31 0.29 0.24 0.22 0.16 0.13 0.10 0.04 0 0.62 0.84 1.39 1.45 2.34 2.51 2.86 3.02 3.61 4.03 4.59 6.53 ) = relative likelihood ) relative the model; = of i (g L values, relative to the highest-ranked model; relative to the values, highest-ranked c Constant only 0.1) (0.50; only Constant 0.8) GC (1.02; 0.3) CC (-0.32; 0.1) (0.11; Veg 0.5) (0.45; LL CC (-0.22; + GC (0.77; 0.9) 0.3) (0.32; 0.5) 0.8) + GC (0.91; LL (-3.93; 0.2) + (0.13; (4.14;GC Veg*GC Veg 2.3) + 2.6) 0.6) (0.18; CC (-0.27; + LL 0.3) 0.4) CC (-0.40; + (-0.55; (1.31; Veg*CC Veg 1.0) 0.4) + 0.2) (-1.74; (-0.12; + Veg (2.49;LL Veg*LL 1.6) + 1.8) GC (0.77;CC 0.3) + (-0.17; 0.6) (0.17; + 0.9) LL 0.8) 0.3) (0.51; GC (1.16; (-0.18; CC+ (-0.30; 0.4) + + 1.1) Veg LL = difference in AIC difference = c September AIC i Veg = vegetation type; CC = canopy cover; LL = leaf litter; GC = ground predictor leaf GC each parameter estimates= litter; cover. = Model: for ground cover; CC vegetationLL type; canopy = = Veg with SE in parentheses; Δ (R) 224