THE TANGLED WEB WE WEAVE
HUMAN-MEDIATED SPREAD OF INVASIVE SPECIES VIA
TRADE NETWORKS USING NEMATODES AS MODEL
ORGANISMS
Natalie Clare Banks B.A., B.Sc. Australian National University
This thesis is presented for the degree of Doctor of Philosophy
of
Murdoch University
October 2016 i
DECLARATION I declare that this thesis is my own account of my research (except where other sources are acknowledged) and contains as its main content work which has not previously been submitted for a degree at any tertiary education institution. This research was conducted under the following Human Ethics Permit numbers: 2013/005 and 2013/158. The original research for this thesis was conducted and authored by myself with the assistance of co-authors in regard to fieldwork (Dr Tangchitsomkid, Dr Hodda, Mr Chanmalee, Ms Sangsawang, Ms Songvilay, Ms Phannamvong, and Mr Thamakhot) and intellectual contributions to chapter drafts (Drs Bayliss, Paini and Hodda).
Natalie Banks
A NOTE ON CONTENT This PhD thesis comprises a published research paper (Chapter 1 published in Ecology Letters, February 2015) and chapters prepared as papers for publication (Chapter 4 submitted to Biological Invasions, October 2016). These are presented with chapter summaries in the introduction and a general discussion that links the chapters into a coherent and integrated body of research. References and in-text citations have been formatted for consistency.
ii
ABSTRACT
Human trade networks play a major role in the unintended introduction of invasive species to new environments. Network Science has shown that the structural properties of networks influence the movement of goods as well as their associated organisms. This thesis examines how the properties of one type of network, the plant produce trade network, aid the movement of one group of potentially invasive organisms, nematodes. The presence, diversity, abundance and dispersal of nematodes via these networks and points critical to the flow of goods as well as nematodes, were also examined. A survey of markets and farms was conducted in three countries (Australia, Thailand and Lao PDR) and nematodes were extracted from the roots of vegetable produce sampled at each location. Plant-parasitic nematodes were identified to genus and numbers of free-living nematodes recorded. Network analysis software was then used to generate and analyse maps of the trade, nematode and plant-parasitic nematode movement networks in each country. A large range and number of free- living and plant-parasitic nematodes were detected moving locally, nationally and internationally via plant produce trade networks. All networks were broadly similar, containing hubs and shortcuts and were directed, poorly clustered and disassortative networks. The movement of nematodes followed the same structural pattern as the larger trade network, meaning that goods and nematodes moved through these networks in a particular and predictable way. Certain critical points in each network were at greater risk of an incursion or of spreading nematodes and may, therefore, represent effective places to target intervention strategies. This thesis synthesises and applies insights and tools from Network Science to Invasion Science theory and practice. By untangling the role of networks in the invasion process, scientists and managers are in a better position to prepare, predict and prevent the spread of invasive species. iii
TABLE OF CONTENTS
DECLARATION.…………………………………………………………….……… i
A NOTE ON CONTENT.………………….….…………….……………….…….… i
ABSTRACT………………………………………………………………………… ii
TABLE OF CONTENTS...……………………………………….………………… iii
ACKNOWLEDGEMENTS……………………………………………………….… vii
LIST OF ABBREVIATIONS………………………………………………….…… viii
GENERAL INTRODUCTION……………………………………...…………………1
THE PROBLEM...... 2
THESIS STATEMENT……………………………………………………………… 3
THESIS OUTLINE…………………………………………………………………. 3 Chapter 1: The role of global trade and transport network topology in the human- mediated dispersal of alien species …………...... 3 Chapter 2: The Network Topology of Plant Produce Trade Networks and Implications for Pest Movement………………...... 3 Chapter 3: Nematodes Network Too: diversity, abundance and dispersal via Plant Produce Trade networks ………………...... 4 Chapter 4: Network Analysis and Plant Pest Infestation Risk in Plant Produce Trade Networks ………………...... 4 Chapter 5: General Discussion...... 4 References...... 5
CHAPTER 1: THE ROLE OF GLOBAL TRADE AND TRANSPORT NETWORK
TOPOLOGY IN THE HUMAN-MEDIATED DISPERSAL OF ALIEN
SPECIES………………………………………………………………………….... 8
ABSTRACT…...... 8
INTRODUCTION………………………………………….……………………………. 9
NETWORK TOPOLOGY………………………………………….…….……….……. 10 Scale-free network properties...... 11 Small-world network properties...... 11 Directed & undirected networks...... 15 Mixing patterns...... 17 iv
Connectance………………………...... 18
A SYSTEMATIC APPROACH TO PREVENTION & MANAGEMENT…….………...…. 20
ISSUES……………………………………………………………...…….…….…… 25 Heterogeneous, interacting components...... 26 Temporal dynamics………………………...... 26 Spatial issues………………………………...... 28 Integration………………….…………...... 29
CONCLUSION….………………………………………….…………….……….…... 30
ACKNOWLEDGEMENTS….………...…………………….…………….……………. 31
REFERENCES.…………………………………………...…………….………….…. 32
CHAPTER 2: THE NETWORK TOPOLOGY OF PLANT PRODUCE TRADE
NETWORKS AND IMPLICATIONS FOR PEST MOVEMENT...... 42
ABSTRACT...... 42
INTRODUCTION...... 43
METHODS...... 46 Data Collection…...... 46 General Structure………………...... 46 Movement of Nematodes...... 48 Data Analysis…...... 48
RESULTS...... 49 Degree Distributions...... 49 Other Network Parameters...... 49
DISCUSSION...... 52 Degree Distributions...... 52 Other Network Parameters...... 52 Average Path Length...... 53 Clustering Coefficient...…...... 53 Density.…...... 54 Assortativity Coefficient...... 55
CONCLUSION...... 56
REFERENCES...... 58 v
CHAPTER 3: NEMATODES NETWORK TOO: DIVERSITY, ABUNDANCE AND DISPERSAL VIA PLANT PRODUCE TRADE NETWORKS...... 62
ABSTRACT...... 62
INTRODUCTION...... 62
METHODS...... 64 Data Collection…...... 64 Nematode Extraction...... 64 Data Analysis…...... 66
RESULTS...... 67 Diversity and Abundance of Nematodes...... 67 Transportation Distances and Nematode Survival...... 70
DISCUSSION...... 74 Diversity and Abundance of Nematodes...... 74 Transportation Distances and Nematode Survival...... 75 Nematode Surveillance...... 76
CONCLUSION...... 77
REFERENCES.…………………………………………...………………………….... 79
CHAPTER 4: NETWORK ANALYSIS AND PLANT PEST INFESTATION RISK IN
PLANT PRODUCE TRADE NETWORKS …...... 84
ABSTRACT...... 84
INTRODUCTION...... 85
METHODS...... 87 Data Collection…...... 87 Data Analysis…...... 88
RESULTS...... 89 Nematode Presence and Movement…...... 89 Centrality…...... 89 Australia…...... 91 Thailand...... 93 Lao PDR...... 93 Source and Sink Nodes…...... 94
DISCUSSION...... 95
CONCLUSION...... 101
ACKNOWLEDGEMENTS….………...…………………….……………….….….…. 101 vi
REFERENCES.…………………………………………….…………………….…... 102
CHAPTER 5: GENERAL DISCUSSION...... 106
KEY POINTS...... 106
APPLICATIONS OF FINDINGS...... 107 Theoretical Applications for Invasion Science...... 107 Practical Applications for Global Biosecurity...... 108
STRENGTHS, WEAKNESSES, CHALLENGES & RECOMMENDATIONS...... 109 Methodological strengths and limitations...... 109 Time Constraints...... 110 Data Availability Constraints...... 111
FUTURE DIRECTIONS...... 111
CONCLUSION...... 112
REFERENCES…………………...……………………...………………………...… 113
APPENDICES………………………………………………….…………….… 114
APPENDIX A...... 114
APPENDIX B...... 131 APPENDIX C...... 143 vii
ACKNOWLEDGEMENTS
I would like to express my deep and sincere appreciation to my supervisors, Drs Kirsty Bayliss, Dean Paini and Mike Hodda. Thank you for taking a chance on me as a student, for all your intellectual input, as well as for your unfailing support and guidance over the past 4 years.
A big thank you to all the project collaborators who made this project possible in Thailand and Lao PDR: Dr Nuchanart Tangchitsomkid, Thanakorn Chanmalee, Tida Sangsawang, Phetsamone Songvilay, Nilandone Phannamvong, and Sayyan Thamakhot.
I am most grateful for support from Murdoch University, Plant Biosecurity Cooperative Research Centre (especially Naomi Thompson and Dr Jo Luck), Invasive Animals Cooperative Research Centre (especially Dr Tony Buckmaster), University of Canberra (especially Dr Joelle Vandermensbrugghe), Commonwealth Scientific and Industrial Research Organisation (especially Dr Michelle Stuckey and Bounnaliam Thammavongsa), and National Research Collections Australia.
I would also like to express my gratitude for the help, advice and support of the following people: Drs Sunil Singh, Tom Harwood, Bruce Halliday, Kevin Clayton- Greene, and Sarah Collins, Rachel Lancaster, Rohan Prince and Tony Napier, as well as market authorities, traders and growers in Thailand, Lao PDR and Australia.
Last but not least, I would like to thank my partner, Hermes, my mother, Anne, siblings Melissa and Rupert as well as my in-laws Ernesto, Gennady, Maggie-Jo and Tim. Words cannot express the extent of my gratitude to you all. I could not have done this without you. viii
LIST OF ABBREVIATIONS
BKK Bangkok EID emerging infectious disease EPP emerging plant pest FAO Food and Agriculture Organization of the United Nations FAOSTAT Food and Agriculture Organization Statistics Division FLN free-living nematodes FM Farmers’ Market FMD Foot-and-Mouth Disease GISP Global Invasive Species Programme IAS Invasive Alien Species ICAO International Civil Aviation Organization KK Khon Kaen NSW New South Wales NT Northern Territory PPN plant-parasitic nematodes QLD Queensland SA South Australia SARS Severe Acute Respiratory Syndrome SIPPC Secretariat of the International Plant Protection Convention SNA Social Network Analysis STDF Standards and Trade Development Facility TAS Tasmania UN United Nations UR Ubon Ratchathani VIC Victoria WA Western Australia WM Wholesale Market GENERAL INTRODUCTION
Biological invasions are a major threat to global biodiversity and food security, impacting the economy, environment, and society. Initial introduction is a fundamental stage in the invasion process. Yet, despite the considerable research attention that has been focussed on invasions, one crucial component has, until recently, been largely overlooked: the role of human trade and transport networks.
These networks move billions of tonnes of goods and people around the world each year (FAO 2015; UN 2015; ICAO 2014) and have been identified as major pathways for the introduction of invasive alien species (IAS) into new environments (Hulme 2009). Of particular interest are plant trade networks, where there is a major knowledge gap (Shaw and Pautasso 2014; Pautasso and Jeger 2009).
Invasive plant pests pose a serious risk to agricultural production as well as market access. Movement of plant pests from farms to markets is facilitated by networks, namely plant produce trade networks. These have the capacity to move potentially invasive plant pests, along with traded plant commodities, all over the world (Pautasso and Jeger 2014). However, plant produce trade networks have received very little research attention for their role in the dispersal of invasive plant pests, especially cryptic plant pests such as nematodes.
Nematodes are an important, yet underestimated group, of plant pests. Plant-parasitic nematodes pose a risk to plant health and food security by causing disease and yield loss and free-living nematodes can pose a risk to human health as some are capable of spreading foodborne diseases (Biosecurity Australia 2011; Anderson et al. 2006). Nematodes are a good model for IAS because they have many of the characteristics of IAS (Singh et al. 2013b) and similar cryptic plant pest groups, such as fungi, bacteria and viruses, that can also disperse via similar pathways to nematodes (Singh et al. 2013a). Nematodes evade detection during visual quarantine inspections due to their cryptic lifecycle and asymptomatic damage to plants (Singh et al. 2013b; Ravichandra 2014). Thus, qualitative and quantitative data on the movement of cryptic plant pests, such as nematodes, via plant produce trade networks is lacking in invasion science (theory) as well as biosecurity (practice). Network science, which is a relatively new discipline, can provide invasion science with new tools in which to study and combat invasions. Network science involves the study of networks, which 2 are a representation of the patterns of connections between different components of a system (Newman 2010). Movement patterns, for example, form trade networks comprised of nodes (components, such as cities, markets or farms) and edges (connections, such as roads, air and shipping routes) linking the nodes together. The pattern of connections in a network influences the direction and flow of matter over the network (Newman 2010).
The physical structure of trade and transport networks (i.e. topology) has been shown to play an important role in the spread of human and animal diseases via the networks (e.g. Severe Acute Respiratory Syndrome (SARS), Meyers et al. 2005; and Foot-and-Mouth Disease (FMD), Ortiz-Pelaez et al. 2006) and underpin patterns in disease outbreaks (e.g. Rasamoelina-Andriamanivo et al. 2014). However, the utility of network science theory and methodology have not been realised in invasion science as they have in the fields of human and animal epidemiology and thus the influence of trade networks on the direction, flow and pattern of IAS movement remains poorly studied.
Network analysis is a scientific method that can be used to identify the points in the system critical to flow and thus enable better management of incursions by influencing the pattern of IAS movement. Network analysis has shown that many networks move goods and organisms in predictable ways and there are often specific nodes or edges in a network that can enable (or disable) the flow of goods or organisms through the network. These points have been found to be critical in preventing the spread of human and animal pathogens via trade networks (Ortiz- Pelaez et al. 2006; Rasamoelina-Andriamanivo et al., 2014). Similarly, these areas may also represent critical points in preventing invasive plant pest movement via plant trade networks. If these points are known then intervention measures can be targeted at strategic places in a trade network, helping to reduce the potential for incursions, better manage the ones that do occur and make future incursions or invasions more predictable.
THE PROBLEM Plant trade networks play a major role in the unintended introduction of invasive plant pests to new locations. Yet the characteristics of these networks and how they 3 facilitate the introduction and spread of plant pests are largely unknown. In addition, qualitative and quantitative data on the movement of cryptic plant pests, such as nematodes, via plant trade networks is lacking. Without this information, it is difficult for decision-makers to know where to optimally target resources to manage incursions and invasions of plant pests over the system.
THESIS STATEMENT This thesis tests the hypothesis that nematodes can disperse via plant (produce) trade networks and that the topology of these networks facilitates the flow of pests. The aim was to demonstrate that network analysis can identify critical points in these networks at which dispersal can be best detected and prevented.
THESIS OUTLINE
CHAPTER 1: THE ROLE OF GLOBAL TRADE AND TRANSPORT NETWORK
TOPOLOGY IN THE HUMAN-MEDIATED DISPERSAL OF ALIEN SPECIES. ECOL
LETT 18:188–199
This chapter highlights an emerging field of research within invasion science: the role of networks in the spread of invasive species. It synthesises studies from several perspectives, approaches and disciplines to derive the fundamental characteristics of network topology determining the likelihood of spread of organisms via trade and transport networks. The review draws attention to a significant knowledge gap, the potential role of plant trade networks in facilitating invasions, and argues that the substantial knowledge generated within other disciplines can provide invasion biologists with new tools with which to study invasions.
CHAPTER 2: THE NETWORK TOPOLOGY OF PLANT PRODUCE TRADE
NETWORKS AND IMPLICATIONS FOR PEST MOVEMENT This chapter examines the network topology of one type of plant trade network, plant produce networks, and the flow of one group of plant pests, nematodes, through them in order to identify the general structural characteristics of this type of network that facilitate the movement of plant pests. It fills an important knowledge gap in 4 invasion science as well as network science and provides important theoretical and practical insights into how goods as well as plant pests flow through these networks, which can make it easier to predict plant pest movement patterns. These insights can be applied to other types of plant trade network as well as to other types of organisms. This chapter also demonstrates how a network perspective and approach can provide an overview of the transportation stage in the invasion process and potentially increase our understanding and management of our role in invasions.
CHAPTER 3: NEMATODES NETWORK TOO: DIVERSITY, ABUNDANCE AND
DISPERSAL VIA PLANT PRODUCE TRADE NETWORKS
This chapter examines the diversity, abundance and dispersal distances of nematodes via plant produce trade networks. It provides detailed baseline data to better inform biosecurity surveillance strategies for this group of organisms at the farm, market and entry port level. It highlights the need for more appropriate biosecurity surveillance and tracking of vegetables, in order to reduce the risk of moving agricultural plant pests as well as potential vectors of human pathogens within and between countries.
CHAPTER 4: NETWORK ANALYSIS AND PLANT PEST INFESTATION RISK IN
PLANT PRODUCE TRADE NETWORKS (SUBMITTED TO BIOLOGICAL INVASIONS
OCTOBER, 2016)
This chapter examines the extent of nematode pest presence and movement via plant produce trade networks in more detail and identifies the critical points in these networks at which to target surveillance and intervention efforts. It demonstrates empirically how network analysis can help identify the critical points in plant trade networks that correspond with nematode and PPN infestation risk, thus providing insights into the practical use of network theory for biosecurity.
CHAPTER 5: GENERAL DISCUSSION
This chapter highlights the theoretical and practical applications of the thesis results and discusses the strengths, weaknesses and challenges of the study as well as recommendations for future research in this field. 5
REFERENCES
Anderson GL, Kenney SJ, Millner PD, et al. (2006) Shedding of foodborne pathogens by Caenorhabditis elegans in compost-amended and unamended soil. Food Microbiol 23:146–153
Biosecurity Australia (2011) Review of import conditions for fresh taro corms. http://daff.gov.au/biosecurity. Accessed 11 July 2016
Eschen R, Britton K, Brockerhoff E, et al. (2015) International variation in phytosanitary legislation and regulations governing importation of plants for planting. Environ Sci Technol 51:228–237
Food and Agriculture Organization of the United Nations (2015) The State of Agricultural Commodity Markets. Food and Agriculture Organization of the United Nations, Rome
Grousset F, Petter F, Suffert M, Roy A (2012) EPPO study on the risk of imports of plants for planting: description and details of the first outcomes. EPPO bulletin 42:185–190
Hulme PE (2009) Trade, transport and trouble: managing invasive species pathways in an era of globalisation. J Appl Ecol 46:10–18
International Civil Aviation Organization (2014). Annual Report of the Council― 2014. http://www.icao.int/annual-report-2014/Pages/default.aspx. Accessed 11 July 2016
Meyers LA, Pourbohloul B, Newman MEJ, et al. (2005) Network theory and SARS: predicting outbreak diversity. J Theor Biol 232:71–81 6
Newman MEJ (2010) Networks: An Introduction. Oxford University Press, Inc. New York, USA
Ortiz-Pelaez A, Pfeiffer D, Soares-Magalhaes R, Guitian F (2006) Use of social network analysis to characterize the pattern of animal movements in the initial phases of the 2001 foot-and-mouth disease (FMD) epidemic in the UK. Prev Vet Med 76:40–55
Page-Weir N, Jamieson L, Bell N, et al. (2013) Interception and hot water treatment of mites and nematodes on root crops from the Pacific Islands. NZ Plant Prot 66:17– 28
Pautasso M, Jeger MJ (2014) Network epidemiology and plant trade networks. AoB Plants 6:1-14
Rasamoelina-Andriamanivo H, Duboz R, Lancelot R et al. (2014) Description and analysis of the poultry trading network in the Lake Alaotra region, Madagascar: implications for the surveillance and control of Newcastle disease. Acta Trop 135: 10–18
Ravichandra N (2014) Nematodes of Quarantine Importance. Horticultural Nematology. Springer, pp 369–385
Secretariat of the International Plant Protection Convention (2006) International Standards for Phytosanitary Measures. Food and Agriculture Organization of the United Nations, Rome
Shaw M, Pautasso M (2014) Networks and plant disease management: concepts and applications. Annu Rev Phytopathol 52:477–493
Singh S, Hodda M, Ash G (2013a) Plant-parasitic nematodes of potential phytosanitary importance, their main hosts and reported yield losses. EPPO Bulletin 43:334–374 7
Singh, S, Hodda, M, Ash, G and Banks, NC (2013b) Plant-parasitic nematodes as invasive species: characteristics, pathways, spread mechanisms, uncertainty and biosecurity implications. Annu Rev Phytopathol 163:323–350
United Nations (2015) Review of Maritime Transport 2015. United Nations, New York and Geneva 8
CHAPTER 1: THE ROLE OF GLOBAL TRADE AND
TRANSPORT NETWORK TOPOLOGY IN THE HUMAN-
MEDIATED DISPERSAL OF ALIEN SPECIES
Banks, Natalie Clare1, 2, 4, Paini, Dean Ronald2, 4, Bayliss, Kirsty Louise1, 2 and Hodda, Michael 2, 3, 4
1 School of Veterinary and Life Sciences, Murdoch University, 90 South Street, Murdoch, WA 6150 2 Plant Biosecurity Cooperative Research Centre, LPO Box 5012, Bruce, ACT 2617 3 National Research Collections Australia, Building 101, Clunies Ross Street, Black Mountain, ACT 2601 4 CSIRO, Clunies Ross Street, Black Mountain, ACT 2601
Keywords: invasive alien species, trade, transport networks, human-mediated spread, infectious diseases.
ABSTRACT
More people and goods are moving further and more frequently via many different trade and transport networks under current trends of globalisation. These networks can play a major role in the unintended introduction of exotic species to new locations. With the continuing rise in global trade, more research attention is being focussed on the role of networks in the spread of invasive species. This represents an emerging field of research in invasion science and the substantial knowledge being generated within other disciplines can provide ecologists with new tools with which to study invasions. For the first time, we synthesise studies from several perspectives, approaches and disciplines to derive the fundamental characteristics of network topology determining the likelihood of spread of organisms via trade and transport networks. These characteristics can be used to identify critical points of vulnerability within these networks and enable the development of more effective strategies to prevent invasions. 9
INTRODUCTION
Current world trends, such as globalisation, have dramatically increased the volume, frequency and range of movement of people and goods in the last few decades (Wilson 1995; Hulme 2009). These people and goods do not disperse by random or diffusion processes, but rather via trade and transport networks, which operate at many levels, from local, through national and regional levels, to global networks. These networks have been identified as key pathways for the unintended entry and spread of invasive alien species (IAS) (Hulme 2009), representing two of the three principal mechanisms for the introduction of organisms to new locations (Hulme et al. 2008). Trade and transport networks can be air, road, rail or shipping routes, and the organisms transported can range from weed plants, to large animals (e.g. mammals, reptiles, amphibians and fish), to small animals (e.g. arthropods, nematodes) and microbes (e.g. fungi, bacteria and viruses) (Pimentel et al. 2001; Hulme et al. 2008).
Not all the organisms introduced to a new area become invasive, and predicting which species could become invasive is the subject of considerable research (e.g. Paini et al. 2010; Venette et al. 2010). However, the species that do become invasive can cause significant damage. In the USA alone, the estimate of the direct economic cost from IAS is almost US$120 billion per year (Pimentel et al. 2005). Globally the figure is much greater, and it rises further when losses in biodiversity, ecosystem services and amenities are included (Pimentel et al. 2001).
Understanding the characteristics of networks that affect the likelihood of organisms moving can assist in devising strategies for preventing incursions of IAS and thus preventing or reducing the impact of these organisms on natural and managed ecosystems.
We present a synthesis of the role of human trade and transport network topology in the passive, unintended spread of organisms. Over the last 20 years, network science has amassed a substantial body of knowledge on the characteristics and nature of the spread of matter (e.g. people, pathogens, ships, ideas) through networks. The structural characteristics of real-world networks such as the World Airport Network (WAN; Guimerà and Amaral 2004), Global Cargo Shipping Network (GCSN; Kaluza et al. 10
2010) and the World Trade Network (WTN; De Benedictis and Tajoli 2011) have been studied and defined. Network theory and modelling have been successfully utilised in the field of epidemiology to understand and predict invasions by pathogens spreading through real-world trade, transport and contact networks (e.g. Severe Acute Respiratory Syndrome (SARS), Meyers et al. 2005; Foot-and-Mouth Disease (FMD), Ortiz-Pelaez et al. 2006; and avian influenza, Van Kerkhove et al. 2009). More recently, network theory has been applied to the movement of IAS by several authors (e.g. Kӧlzsch and Blasius 2011; Moslonka-Lefebvre et al. 2012; Paini and Yemshanov 2012). These individual studies have shown that network science can provide insights and immensely useful tools for ecologists studying invasions, yet the unintended movement of organisms through trade and transport networks has not been extensively studied in a systematic way. However, when the many independent, unrelated studies across several disciplines are assembled and synthesised, it is possible to identify some of the fundamental attributes of network topology that influence the spread of IAS. This paper synthesises the current research in this area, discusses the implications of this research for management of IAS, and suggests some future research directions. The ultimate aim of the paper therefore is to provide ecologists with an entry point into this new field, summarise and synthesise key results and insights from the full range of disciplines utilising network theory, and suggest how these insights may apply to invasion science.
The paper is presented in three sections. The first section briefly describes and summarises the features of network topology most relevant to the spread of IAS and outlines how these contributions from network science and epidemiology can provide insights into IAS spread on networks. The second section suggests how the insights from network science can provide new approaches to managing IAS spread in relation to the entire invasion system. The third section discusses some of the challenges in the application of network science to biological invasions, and how they may be addressed.
NETWORK TOPOLOGY World trade and transport networks—such as the WAN, the GCSN, and the WTN— share some characteristics that affect the movement of people and goods as well as pests and pathogens. Similarities include scale-free degree and small-world properties, while differences occur in the directionality of links and in the mixing patterns and clustering 11 of nodes. All these characteristics are important to the spread of IAS, so all need to be considered in applying network science to invasions.
SCALE-FREE NETWORK PROPERTIES
Scale-free networks are heterogeneous, where most nodes (ports, cities or countries) have few edges (links) and a few nodes have many edges; this “fat tailed” or “right skewed” distribution in degree (number of edges), follows a power law when logarithmically transformed, (Barabási and Albert 1999) (Figure 1.1; see Figure 1.2 for illustrated network terminology).
Invasive alien species may spread easily and quickly in scale-free networks via highly- connected “hub” nodes, which contain a large number of connections (Jeger et al. 2007). Invasive alien species may not need to be highly infectious or well adapted for transport to be spread via scale-free networks due to the high number of links from hub nodes to other susceptible nodes (Bigras-Poulin et al. 2007; Jeger et al. 2007). Severe acute respiratory syndrome (SARS), for example, is less infectious than influenza (WHO 2003), yet it spread to 37 countries via scale-free global transport networks within a matter of weeks, infecting approximately 10,000 individuals (Smith 2006).
SMALL-WORLD NETWORK PROPERTIES
Small-world networks are homogeneous, with each node having approximately the same number of edges (Figure 1.1). They follow a Poisson distribution curve where the distribution in the number of edges per node peaks at an average value and then decreases exponentially (Wang and Chen 2003). Networks with small-world properties have shortcuts, where any node in the network can be reached from any other node in a few steps. A small-world network has a small average path length (mean distance between any two nodes in a network) as well as a high clustering coefficient (the degree to which nodes are connected together in clusters) (Watts and Strogatz 1998). In practical terms, this means that most ports, cities and countries in such a network can be reached from any other port, city or country in a short number of trade or transport connections. Furthermore, nodes are grouped in densely-connected clusters with relatively fewer links between the clusters than within them. These properties have implications for the spread of IAS as with more shortcuts an IAS does not have to be well adapted for transport to spread through the network (Jeger et al. 2007) and can potentially spread to any receptive node in the network via these shortcuts. 12
Figure 1.1. The degree distribution in a small-world network, illustrated by the Australian road network (left), follows a Poisson curve while the degree distribution in the Australian airline network, a scale-free network, follows a power law distribution (right). 13
Figure 1.2. Illustrated network terminology (left) and real-world example of a trade network (right). Groups of nodes with symbols ranging in colour from black to light grey represent hierarchical clusters within the network.
Increased clustering in networks can produce largely separated communities (sometimes referred to as components). As opposed to network shortcuts, the presence of network communities is believed to slow down the spread of pests and pathogens through the network due to their confinement within these highly-connected clusters (Moslonka- Lefebvre et al. 2009), such as during the initial stages of the AIDS epidemic in the USA (Szendroi and Csanyi 2004). Thus, clustering may produce faster invasions within communities, even if there is relatively low transmission between nodes, but slower invasions over the whole network.
However, a high level of clustering in a network can produce one giant component, which can be either strongly or weakly-connected. Many real-world trade and human 14 contact networks have a giant strongly-connected component (GSCC) containing most of the nodes within the network linked together via multiple edges (e.g. Morris and Kretzschmar 1995; Aznar et al. 2005; Kao et al. 2006; and Rautureau et al. 2011). These networks are very densely-connected, which means that an infestation in any single node of the GSCC can lead to a widespread invasion in most of the network (Meyers et al. 2006).
Real-world trade networks can also be made up of hierarchical clusters of nodes, where a central ‘core’ node is linked to nodes in ‘lower’ levels of the hierarchy via the nodes in each successive level such that the network is structured like the trunk, branches and leaves of a tree (Newman 2010; Pautasso et al. 2010; Rasamoelina-Andriamanivo et al. 2011). Stratification of networks into hierarchical clusters based on the characteristics of nodes (such as movement and behavioural patterns or identity) has been found to have important implications for epidemics in networks (Doherty et al. 2005; Ortiz-Pelaez et al. 2006; Keeling et al. 2010). For instance, when hierarchical clusters of farms, markets and traders correspond to high risk animal movement in trade networks, the likelihood of disease transmission for the nodes within these particular clusters can be higher than for others (Bigras-Poulin et al. 2006; Ortiz-Pelaez et al. 2006). Key “spreaders” in the initial outbreak of Foot-and-Mouth Disease in the United Kingdom (farms, markets and dealers) were identified in part by identifying hierarchical clusters of nodes with links to each other as well as links to nodes in other clusters (Ortiz-Pelaez et al. 2006). In a hierarchical, scale-free network, once an organism infects or invades a hub, it can rapidly spread to other parts of the network through an invasion cascade from well- connected hub nodes at the national (or global) level to nodes in regional clusters with few links in the network (Kiss et al. 2006).
Many real-world trade and transport networks possess small-world AND scale-free characteristics; that is a small average path length, a high clustering coefficient and a power law degree distribution (Boccaletti et al. 2006). This may be related to the way in which trade and transport networks grow. New links are made in a network when a new node connects to an existing one or when previously unconnected existing nodes link together. These connections can be made in various ways: by preferential attachment to a well-connected node in the wider network; or by preferential attachment to nearby nodes (Li et al. 2012; Barthélemy 2011). This phenomenon is observed in the WTN in 15 relation to regional cooperative organisations (network communities), such as the EU, ASEAN and NAFTA. A country aiming to expand its international trade may choose to become a member of one of these organisations and preferentially link to one or several of the well-connected countries within these trade clusters (Li et al. 2012). However, trade barriers may reduce the impetus for this form of attachment and a country may choose instead to create local trade links with countries which are geographically close to it (Li et al. 2012). Preferential attachment produces power law distributions of degrees, which is a scale-free network characteristic (Newman 2010), while random or local preferential attachment produces a small average distance and a high clustering coefficient, which are small-world characteristics (Barthélemy 2011; Li et al. 2012).
The different forms of attachment are generated from decisions made at the local level, but are influenced by drivers operating at national, international and global levels (Barthélemy 2011; De Benedictis and Tajoli 2011). These underlying mechanisms are critical for determining the structure of networks (Newman 2010; Barthélemy 2011; Li et al. 2012) and hence the flow of pests and diseases through them.
While trade and transport networks, such as the WAN, the GCSN and the WTN, are similar in many of the general structural characteristics outlined above, they differ in other characteristics which are important to the spread of IAS, such as the directionality of links and in the patterns in which nodes link together.
DIRECTED & UNDIRECTED NETWORKS
In directed networks, such as the WTN and the GCSN, goods flow from source nodes (places of production, such as farms) in one direction to final destination nodes (such as individual households) often through other nodes (such as packing and distribution houses, wholesale markets and stores). The probability of invasions occurring across directed (and semi-directed) networks is influenced by the number of links going in specific directions, into and out of nodes (Meyers et al. 2006). The more outward links from an initial node (source), the greater the potential spread (Kiss et al. 2006) and the greater the potential final size of the invaded area (Pautasso et al. 2010). The greater the number of inward and outward links, the easier an organism will be able to spread throughout a network and the less important will be specific adaptations for long- distance travel (Pautasso and Jeger 2008). This is because in-degrees represent the number of nodes able to invade an individual node, and out-degrees represent the 16 number of nodes able to be invaded from that one node (Meyers 2007). The more outgoing links an invaded node has, the greater its spread potential. The number of outward links from invaded lakes, for example, has been correlated with the subsequent invasion of previously un-invaded lakes by Bythotrephes longimanus, the spiny waterflea (Muirhead and MacIsaac 2005). The horticultural trade network is another real-world example of a directed network where the movement of traded plants has been associated with the spread of the plant pathogens Phytophthora ramorum and P. kernoviae (Harwood et al. 2009), although how its structure may increase the probability of spread has not been investigated in detail. Notwithstanding this example, among real-world networks, plant trade has been relatively little studied from a network viewpoint. Considering the potential role of plant trade networks in facilitating invasions, this is a significant knowledge gap (Pautasso and Jeger 2014).
The number of outward links from an original source node is also important in the spread of IAS in theoretical modelling, and empirical studies of directed networks. In modelling of hierarchical, directed networks the probability of spread beyond the initial node and final extent of an invasion are both positively correlated with the number of outgoing links from the source node, regardless of higher level structural topology and cohesion (Pautasso et al. 2010). The key role played by source nodes in spread via networks is supported by research on avian influenza outbreaks in a real-world trade network (Martin et al. 2011). Potentially invasive pests or diseases originating at or near a well-connected source node will have a greater potential for spread throughout a network than species originating near less well-connected sources.
In undirected networks—where node connections run in both directions—spread of IAS is less predictable than in directed networks, particularly in the early stages of an invasion (Jeger et al. 2007). The symmetrical transportation links to and from airports and cities in the WAN and Global Road Network (GRN) (Barrat et al. 2004; Barthélemy 2011) are undirected networks in which IAS can potentially move in both directions. The poultry trading network in Madagascar is another real-world example of an undirected network, one in which exotic viruses, such as Newcastle disease and avian influenza, have been able to spread (Rasamoelina-Andriamanivo et al. 2011). 17
MIXING PATTERNS
Mixing patterns (measured by the assortativity coefficient and neighbour connectivity in network theory) refer to the tendency of nodes to link with other nodes in particular ways. Two main types of mixing are relevant to spread in real- world networks: assortative mixing and disassortative mixing (Danon et al. 2010; Newman 2010).
In networks with assortative mixing, nodes with similar numbers of edges preferentially link to each other, such that those with numerous edges (highly- connected nodes) link together and those with few edges (less well-connected nodes) preferentially link together. World transport networks, such as the GCSN and the WAN, are assortative networks: well-connected ports and airports tend to connect with other well-connected port and airport nodes (Barrat et al. 2004; Kӧlzsch and Blasius 2011). This kind of mixing within networks can create components that are well-connected internally but that are relatively disconnected from each other at the network level (Doherty et al. 2005).
In disassortative mixing, nodes with numerous edges link to nodes with few edges. This kind of mixing can create more links between different parts of the network and thus larger components (Morris and Kretzschmar 1995). Trade networks, such as the WTN, tend to be disassortative networks, where trade ties more commonly exist between well-connected countries and poorly-connected ones (Kiss et al. 2006; Fagiolo et al. 2008; De Benedictis and Tajoli 2011).
The type of mixing pattern affects the spread of an invasion in a network. Assortative mixing can enable less infectious or well adapted organisms to initially spread faster via the network (Gupta et al. 1989; Kiss et al. 2008). By contrast, disassortative mixing can produce larger scale invasions on networks (Gupta 1989; Morris and Kretzschmar 1995) and the maximum extent of an invasion is reached faster (Kiss 2008). However, initial spread is slower in disassortative networks as invasive agents move from higher degree nodes (such as well-connected markets) to lower degree nodes (such as farms with few connections) (Kiss 2006; Kiss et al. 2006).
When both types of mixing occur in a network, the degree of disassortative mixing may be most important because it determines the extent of spread throughout the 18 larger network and connects any isolated clusters created by assortative mixing (Newman 2003). Even in networks with largely assortative mixing, a small amount of disassortative mixing greatly increases the risk of infestation or infection for nodes in the rest of the network. This has been found in real-world sexual contact networks (Doherty et al. 2005).
Directedness and assortativity differ markedly in different real-world networks. The GCSN is directed and assortative (Kӧlzsch and Blasius 2011); the WTN is directed and disassortative (De Benedictis and Tajoli 2011); the WAN is undirected and assortative (Barrat et al. 2004); and the GRN is also undirected but its mixing patterns are undetermined because the whole network is yet to be characterised. Urban streets, representing most of the network, appear to be disassortative (De Montis et al. 2005; Porta et al. 2006), while highways representing a smaller portion of the network appear to be assortative (Mukherjee 2012; Mohmand and Wang 2013). Thus the network as a whole may be disassortative.
CONNECTANCE
Network connectance (measured by k-core in social network theory) refers to how well-connected the nodes are in the network, and influences the incidence and prevalence patterns of IAS (Ghani et al. 1997; Doherty et al. 2005; Martin et al. 2011). Network connectance is the fraction of realised links over the total number of possible links. The trend to globalisation is increasing connectance in networks such as the WTN, as each new trade connection (edge) between new trading partners (nodes) increases the density of linkages in the overall network (Foti et al. 2013). This improves the mobility of goods between any two nodes in the system but also means IAS can move around more easily, via multiple links (Jolly and Wylie 2002; Bigras-Poulin et al. 2007).
Centrality is another aspect of network connectance that influences the potential spread of organisms. Centrality measures the importance of a particular node to connectivity within a network, and has been shown to be influential in IAS spread in human and animal contact networks through three different measures: degree centrality, betweenness centrality and closeness centrality.
Degree centrality determines the importance of a node to connectivity within a network by the large number of edges the node has, linking it to other nodes in the 19 network (Piraveenan et al. 2013). Degree centrality determines the likelihood of a node becoming invaded and spreading the invader through the network. The degree centrality of the source node of an invasion or epidemic may have a significant influence on the spread of the pest or pathogen (Pautasso et al. 2010). Degree centrality may also be important in the general spread of IAS over the network, as well-connected areas are more likely to be invaded than less well-connected areas. Nodes with high degree centrality have been correlated with the occurrence of exotic viral outbreaks in animal trade networks (Rasamoelina-Andriamanivo et al. 2011).
Betweenness centrality refers to the extent to which a particular node lies on the shortest paths between other nodes in a network, acting as a channel or funnel. It is a measure of the importance of a particular node in connecting other nodes in a network: nodes with greater betweenness centrality spread IAS faster and with higher probability. Nodes with high betweenness centrality have been identified as “key players” in the spread of viruses in real-world networks (Ortiz-Pelaez et al. 2006; Rasamoelina-Andriamanivo et al. 2011). A market with the highest betweenness of all the nodes in an animal movement network was pivotal in transmitting Foot-and-Mouth Disease in the United Kingdom during the 2001 epidemic (Ortiz-Pelaez et al. 2006). This may also be the case for plant pests. For example, the Netherlands is the most important importer and distributor of ornamental plants in the world (European Commission 2006, Dehnen-Schmutz et al. 2010). Because it is on the paths between most producers and importers, it could act as a gateway to Europe and the rest of the world for the introduction of pests such as the western flower thrips (Kirk and Terry 2003).
Betweenness centrality can refer to edges as well as nodes. Edges with high betweenness centrality act as bridges between otherwise disconnected components of a network. In terms of invasions, bridges enable IAS to jump geographic or social barriers (long-distance jumps) to establish new populations (Jolly and Wylie 2002). For example, in a human contact network one link between two people in Manitoba, Canada connected the population in the capital, Winnipeg, with the rural population in Manitoba, and formed a bridge for disease transmission between the two populations (Jolly and Wylie 2002).
Closeness centrality is slightly different to the previous two centrality measures in that a node’s importance is determined by the short average distance from that node 20 to all other nodes in the network, rather than the number of connections it has (degree centrality), or how it acts as a channel to other nodes (betweenness centrality). Nodes with a shorter total distance (closeness) to all other nodes may be more important in spreading diseases, because pathogens can move through these nodes to the other nodes in a network in only a few steps (Natale et al. 2009).
In simulations of the spread of cattle diseases through the Italian cattle network, a short number of steps between an initial contaminated farm (source node) and all the other nodes in the network strongly correlated with the final size of epidemics (Natale et al. 2009). Other modelling studies based on real-world networks have found that the distance between nodes in highly-connected cores of networks can be the most influential determinant of the extent of spread (Kitsak et al. 2010).
This section has summarised theoretical modelling and empirical studies of the topology of networks in the areas of human and animal health, aquatic invasion biology and, more recently, plant pathology. Many different aspects of networks have been investigated, not just those mentioned above. However, only some features of networks have been found important to the spread of IAS. These include small-world and scale-free properties, directedness of links, mixing patterns and aspects of network connectance. These characteristics may help pinpoint those nodes within a network (e.g. lakes, ports or national parks) that are the most vulnerable to IAS introductions and those nodes and edges that are likely to be the most influential in the dispersal of IAS. Some suggestions for IAS prevention and management are presented below.
A SYSTEMIC APPROACH TO PREVENTION & MANAGEMENT Currently, IAS management strategies are applied at the state, national and, occasionally, international level (STDF 2013). These strategies typically target particular species or vectors. With increased globalisation, national and regional networks are becoming increasingly interconnected and incorporated into global networks. Invasive alien species, vectors, networks and the relationships between them represent parts of a system for the spread of IAS around the world. Therefore, we suggest that the prevention and management of IAS spread within this globalised system requires a systemic perspective and a systemic approach, rather than one 21 focussed on particular species or vectors. The characteristics of the network, not just those of the pest, vector or location, are important.
The key components of the system and their interrelationships contribute to the likelihood of transportation of IAS to new environments (see Figure 1.3 for conceptual diagram). These components may also represent the critical points in the system at which to apply intervention efforts to prevent invasions. A systemic approach involves nested intervention strategies targeting, specifically, those points within the system that generate the highest likelihood of IAS movement. For example, focussing on bridges (edges with high betweenness centrality), the specific vectors that link regional clusters (network communities) together via those bridges, and finally, targeting the means and sites of species attachment for these vectors (how IAS enter the network). By targeting prevention and management measures at strategic, critical points at the global or regional levels in worldwide trade and transport networks, rather than within individual states or countries, limited resources can be focussed on the specific areas where they will be most effective, thus saving time and resources.
Globalisation has increased the interconnectedness of world nations, thus making problems of IAS potentially global rather than local or national. A systemic approach therefore, recognising the whole global system, has particular relevance. However, it may require changes to management of IAS such as: increased commitment to international cooperation and collaboration in IAS management; coordination of different levels of trade from global through regional to local levels; implementation of quarantine measures at levels other than the national level; consideration of IAS in decisions on making trade and transport links; and allocation of resources to the countries with limited resources to deal with global IAS problems because they represent the “weakest links” in the network (Perrings et al. 2002). A systemic approach and perspective recognises that everyone is part of global, interconnected networks and the IAS problems of one part influence the IAS problems of many other parts of the network.
A network approach can also improve the accuracy of Import Risk Analyses (IRAs). By incorporating the bigger picture of global and regional networks and analysing all pathways—not just particular species or pathways—IRAs can better capture the full 22 range of risks from IAS and become more reliable tools to prevent unintended introductions.
There are other, more specific insights for the management of IAS which come from recognising the importance of network topology in the human-mediated dispersal of alien species through global trade and transport networks. The insights from network science can apply to management at all stages of invasion, from before invasions (prevention and preparedness) to during invasions (decision making for issues such as eradication or areas of monitoring and allocation of resources) to after invasions (implications and on-going management). To demonstrate how these can enhance invasion science, some implications are discussed briefly below.
One important insight is, because species that are less infectious or well adapted for transport, may spread in scale-free and small-world networks, more species may have the potential to become IAS. Their characteristics may be different to those spreading by natural means and those organisms that have been invasive in the past. 23
Figure 1.3. Likelihood of IAS transportation is a function of three factors: characteristics of species, behaviour of vectors, and the topology of the network. Their influence on the likelihood of movement (represented by the surface) is driven by the varying ability of these variables to transport IAS.
Another insight is that, because hubs have particular potential to spread IAS, monitoring them may be a very effective way of detecting IAS, particularly in hierarchical networks. Targeting hubs can also be an effective strategy for containment of the extent of invasions. These insights can also apply to hubs between different networks, for example transhipment or transfer points (hubs connecting the GCSN and GRN). Hubs may be increasing in their potential to spread IAS due to the increased mixing of produce from multiple sources and origins at these nodes (Barham and Sylvander 2011). 24
Because high degrees of clustering may slow the spread of invasions and lead to their natural confinement within clusters, knowing the level of clustering in network communities may be an important part of preparedness for, and management during, invasions. In addition to providing insight into the time scale for dealing with an incursion most efficiently, knowing the extent of clusters can help greatly in defining the area for monitoring, eradication and management: the focus can be limited to the cluster and does not have to extend to the entire network. By contrast, because invasions can spread very rapidly and extensively in a network with a GSCC, then speed of response is more important.
As the potential for spread is great via the outward links from a source node in directed networks, invasions and invasive species originating at or near a well- connected source node have higher potential to spread. This may mean that more resources are justified for dealing with invasions from such nodes than those from less well-connected sources. It may also mean that species with relatively low invasion potential may be threats if they are near a well-connected node in a directed network than if they are elsewhere, or part of a network with different characteristics.
The spread of IAS is less predictable in undirected networks, especially in the early stages of an invasion, so more uncertainty must be allowed for in responses, possibly involving greater margins of error. More strategic action may be required for invasions in such networks.
Mixing patterns can have different effects on the movement of IAS through networks and there can be several responses when using this information. In networks with assortative mixing, organisms can spread initially very quickly via the network but in the early stage of an invasion the spread of these organisms is likely to be isolated within a network cluster. The responses to IAS may therefore need to be more rapid in networks with assortative mixing, but these responses may only need to target smaller areas, if the IAS is detected before it becomes widespread. The clustering associated with assortative mixing means that expansions of range may occur in quite sudden jumps into new clusters rather than gradual expansion. This sort of range expansion means that data on the spread of invasive organisms must be interpreted rather differently. For example, an apparent long stasis in range 25 expansion may precede another rapid increase in range, rather than indicate that the maximum extent of an invasion has been reached.
As disassortative mixing is associated with slower initial rates of spread, rapid action may be less important than for networks with assortative mixing. Range expansion may be at a steady rate, which can be used to inform monitoring and management strategies better than in assortative networks. This may be important because IAS may move farther via a network with disassortative mixing, meaning that response actions for an IAS will need to cover a wider area.
The increasing connectance of many networks means that IAS can move around more easily and via multiple links. This has two implications. Firstly, identifying and targeting the weakest links in biosecurity systems and whole networks are required for effective management. Secondly, co-ordinated action at several or many nodes may be required to manage an invasion, and that the costs of management, therefore, may be high. However, high connectance also means that the implications of an IAS entering a network may be substantial and widespread, so that high costs may be justified.
Centrality measures may be useful in minimising costs of IAS through targeting ports, cities or countries with the greatest potential to cause large-scale invasions or epidemics. For example, source nodes with high degree centrality can be targeted and their key connections broken in order to contain invasions (Natale et al. 2009). Additionally, knowing the number of steps between an initial source node or farm and all the other nodes in the network is essential information for management, as sources that are closer to all other nodes in the network (closeness central nodes) may be key to determining the speed and final size of an invasion as well as the likely costs and benefits of different responses. Traceability is, therefore, vital in the prevention and management of invasions.
ISSUES A systemic approach to IAS has many potential benefits, but it also requires a deep understanding of the invasion system, how it operates, and the relationships among components of the system. All of these are complex, dynamic, heterogeneous, highly sensitive to variation and are constrained by space. These factors present several 26 issues which need to be considered when applying network science to the spread of IAS.
HETEROGENEOUS, INTERACTING COMPONENTS
Invasion systems contain invasive species, vectors, habitats and communities of organisms as well as environmental variables, all of which act and interact within various spatial and temporal scales. These components also have heterogeneous characteristics which can influence spread. For instance, habitats can vary in their susceptibility to invasion (García-Robledo and Murcia 2005), countries can vary in their phytosanitary policies and procedures (FAO 2000), environmental factors can favour or prevent establishment (Holway et al. 2002) and vector classes can have heterogeneous links to different habitats (Kinloch et al. 2003; Seebens et. al. 2013). In addition to these aspects, invasion systems also contain networks of roads, air and shipping routes, connecting different environments together in different ways, which can passively transport IAS to new localities. The role of species and vector characteristics in the transport component of invasions has been well documented (e.g. Ruiz and Carlton 2003; Leung et al. 2012) but the role of network topology on the spread of invasive organisms and how it interacts with other components of invasion systems has only recently gained attention in invasion science (e.g. Pautasso and Jeger 2014). For example, a species’ characteristics may determine whether it survives in sufficient numbers during transport to reach a new habitat, while vector mobility patterns may determine when and where an organism is moved in the landscape, and network topology determines how far the potential IAS can spread through the network. The different components, their heterogeneity and interactions may increase or decrease the likelihood of IAS movement. All the relevant components of invasion systems need to be included in network models in order to increase their utility and predictability for invasion networks.
TEMPORAL DYNAMICS
Temporal dynamics are important to include in consideration of networks because networks change over time. Over large time scales, real-world systems develop and evolve (Holme and Saramäki 2012). Connectance and the number of hub nodes increase, clusters form and average path lengths change (Barthélemy 2011; De Benedictis and Tajoli 2011). Over smaller time scales, new links are created while 27 old ones are lost (Cocks et al. 2009), there are changes in volumes and flows (Martínez-López et al. 2009; Tatem 2009), and there are directional changes in the links according to the season (Aznar et al. 2005; Cocks et al. 2009).
The changes in networks have implications for IAS movement. Increased connectance over time produces potentially larger and more rapid invasions: previously localised pests, such as western flower thrips and Colorado Potato Beetle, have quickly become cosmopolitan in a more connected network (Kirk and Terry 2003; Grapputo et al. 2005). The evolution of nodes into hubs increases both their vulnerability and potential to spread IAS, together with increasing the probability and speed of dispersal in the entire network. The growth of regional clusters as networks evolve also means that IAS may be more easily and rapidly spread but also that they may be more easily isolated within geographical regions. As more trade (or transport) links are made, the average distance between nodes in the network decreases, but as the network increases in size, so does the average distance between nodes (De Benedictis and Tajoli 2011).
Trade flow dynamics represent fluctuations in flow and volume in time and space. They are particularly important in affecting the spread, establishment and prevalence of IAS. Fluctuations in flow through a network can produce periods of high and low IAS spread through the system. The flow of livestock trade, for example, is affected by market forces (supply and demand) and these forces can change the flow of trade during the course of a year as well as over larger time scales (Cocks et al. 2009). Additionally, periods of high and low demand during the year often correspond to different festival times such as Christmas and Lent (Bigras-Poulin et al. 2007; Leslie 2010). The peaks in commodity movement at these times can represent critical points in time when the likelihood of spread is far higher than usual (Martínez-López et al. 2009).
Fluctuations in the volume of goods moving can also affect the introduction of IAS into new geographic areas. Changes in traffic volume, such as those modelled over the WAN over large time scales, may change the number of propagules arriving in new areas and thus the probability of successful introduction of IAS into those new localities (Tatem 2009). Similarly, simulations of the spread of invasive marine species over a regional water transport network found that locations receiving higher 28 volumes of traffic were 75% more likely to become infected by an invasive marine species than quieter ports (Floerl et al. 2009). They were also more likely to accelerate the spread of the IAS to other locations (Floerl et al. 2009).
Although networks change over time, most empirical studies on real-world networks examine no more than one year’s movement data, presumably due to the large amount of data involved. This is effectively looking at a dynamic network as a snapshot in time, and it cannot reveal whether the network remains constant over a longer time scale. Unfortunately, collecting temporal data can be difficult, time consuming and expensive.
There are several strategies to combat this problem. For a general picture of how real-world networks operate and their temporal dynamics, snapshots of several networks could be taken and network sizes and factors influencing movement compared. However, if a specific network is under examination, it can be monitored periodically (Bata et al. 2005) once other characteristics of the system have been established. These are just a few examples of how network dynamics, a major gap in network analysis, could be addressed.
SPATIAL ISSUES
Real-world systems are constrained by space, unlike mathematical representations. These spatial constraints can shape some features of small-world and scale-free networks (Barthélemy 2011). In the real world, moving longer distances costs more, and this spatial constraint on human vectors can create clustering in a network (Barthélemy 2011). Habitats close to each other link preferentially together and thus spread IAS within these communities more readily than over longer distances. This same local preferential linking tends to produce short average path lengths in spatially constrained networks as well as more regional hubs and fewer global hubs (Barrat et al. 2005; Barthélemy,2011). The spatial constraints and costs associated with new links that apply in real-world trade and transport networks can create a maximum in the number of links a port, city, airport or country is able to have (Barthélemy 2011).
Other features of real-world networks can also be shaped by space. For example, spatial constraints can influence the centrality of nodes in networks such as the 29
WAN, where airports with the greatest number of shortest paths passing through them to other airports (high betweenness), are not those with the highest number of connections (high degree). They are instead the airports that are the most geo- politically central in the network (Guimerà and Amaral 2004; Barthélemy 2011).
Spatial scales also influence how IAS may move through networks. Different networks and movement patterns are important at different scales. Long-range human travel is dominated by airlines, which have been shown to spread viruses to new geographical areas, thus facilitating IAS spread on an international scale (Meyers et al. 2005; Balcan et al. 2009). At a regional or country level, however, commuter transport networks have a greater effect, spreading viruses to different subpopulations within small areas (Balcan et al. 2009). A similar phenomenon has been found for marine IAS introductions. Commercial ships have been implicated in the primary, long-distance spread of a now cosmopolitan tunicate, Botryllus schlosseri, whereas recreational boats are implicated in secondary, localised spread (Lacoursière-Roussel et al. 2012).
The movement patterns of the vectors of IAS may only be revealed by data at particular spatial scales. For malaria, the high spatial resolution of mobility data on a regional scale can pinpoint those human settlements expected to receive or transmit more parasites than others in surrounding regions (Wesolowski et al. 2012). At a larger scale, however, movement data are more general and has lower spatial resolution. Thus some network features influencing spread may not be visible at the larger scale.
Larger scale movement data (such as the WAN and the GCSN) may be more valuable in visualising the bigger picture of global trends. Trade networks are ultimately open systems, forming part of larger, increasingly-globalized and increasingly-integrated networks. Considering large-scale movement data may put regional networks into the context of these larger, more-encompassing networks and systems.
INTEGRATION
Component interactions, heterogeneity, temporal and spatial characteristics of real systems are all issues, which need to be addressed in network modelling because, as 30 discussed above, all are characteristics of networks and are important for understanding invasions. Some of these characteristics have been incorporated into network models using constructs such as differential node status, weighted and directed edges (Newman 2010), degree-block variables (Colizza and Vespignani 2007, 2008), time-scale separation (Keeling and Rohani 2002), and most recently with the design and use of dynamic network models (Ferrari et al. 2014). However, some features have yet to be incorporated and a single model incorporating all features remains to be developed. Thus, the utility of network models is limited by the number of these characteristics that can be incorporated.
Despite their limitations, network models have been useful in understanding and predicting the behaviour of some systems. SARS, FMD and avian influenza are examples of diseases whose spread on some real-world trade and transport networks have been successfully modelled. In these cases, the network approach still captures more of the important details of real-world networks than other approaches, such as gravity models, which only capture trends (Kaluza et al. 2010; De Benedictis and Tajoli 2011). Network models can, therefore, make more realistic predictions of spread dynamics and are thus are more informative for international policy decisions and management of IAS (Kaluza et al. 2010; Ferrari et al. 2014).
An interdisciplinary collaboration between the fields of network science and invasion biology is necessary to further develop an approach to understanding, modelling, and managing IAS that includes the whole invasion system. The network approach can help invasion biologists visualise, analyse and potentially predict the introduction and spread of IAS, key steps in the invasion process. Highly complex information can be visualised as network maps and the structure of the network can be analysed. Conversely, invasion science can make a significant contribution to the development of network theory by identifying the ecological variables important in real systems, and thus guiding discovery of the universal principles governing network structure. 31
CONCLUSION
This synthesis outlines some of the main factors implicated in the passive, unintentional spread of exotic species via human trade and transport networks and these fundamental factors cut across taxa as well as disciplinary and sector boundaries. Network science has contributed a large body of knowledge on spread via networks and provides new and useful tools for ecologists to study invasion systems. Utilising these new insights and tools in a systemic approach to the prevention of IAS movement can help decision-makers in managing threats to national and regional biosecurity and, ultimately, in safeguarding the world’s natural and managed ecosystems.
ACKNOWLEDGEMENTS
The authors would like to acknowledge the support of the Australian Government’s Cooperative Research Centres Programme, Murdoch University and the Commonwealth Scientific and Industrial Research Organisation. We would like to thank Tom Harwood and, especially, Bruce Halliday for their comments on the manuscript. This manuscript was significantly improved by suggestions from two anonymous reviewers.
32
REFERENCES
Aznar M, Stevenson MA, Zarich L, León EA (2011) Analysis of cattle movements in Argentina, 2005. Prev Vet Med 98:119–127
Balcan D, Colizza V, Gonçalves B et al. (2009) Multiscale mobility networks and the spatial spreading of infectious diseases. P Natl Acad Sci 106:21484–21489.
Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286: 509-512
Barham E, Sylvander B (eds.) (2011) Labels of origin for food: local development, global recognition. CABI, Wallingford, UK.
Barrat A, Barthélemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. P Natl Acad Sci 101:3747–3752
Barthélemy M (2011) Spatial networks. Phys Rep 499:1-101
Bata MO, Dradri S, Chapasuka E et al. (2005) A Report on a Joint Rapid Assessment of Informal Cross Border Trade on the Mozambique-Malawi Border Regions Conducted between 27 June-1 July 2005. Maputo, Sistema De Informação De Mercados Agricolas De Mozambique (SIMA)
Bigras-Poulin M, Thompson R, Chriél M et al. (2006) Network analysis of Danish cattle industry trade patterns as an evaluation of risk potential for disease spread. Prev Vet Med 76:11–39
Bigras-Poulin M, Barfod K, Mortensen S, Greiner M (2007) Relationship of trade patterns of the Danish swine industry animal movements network to potential disease spread. Prev Vet Med 80:143–165 33
Boccaletti S, Latora V, Moreno Y et al. (2006) Complex networks: Structure and dynamics. Phys Rep 424:175–308
Caton BP, Dobbs TT, Brodel CF (2006) Arrivals of hitchhiking insect pests on international cargo aircraft at Miami International Airport. Biol Invasions 8:765–785
Cocks P, Abila R, Bouchot A, Benigno C et al. (2009) FAO ADB and OIE SEAFMD Study on Cross-Border Movement in the Market Chains of Large Ruminants and Pigs in the Greater Mekong Sub-Region. Bangkok, Thailand
Colizza V, Vespignani A (2007) Invasion threshold in heterogeneous metapopulation networks. Phys Rev Lett 99:148701
Colizza V, Vespignani A (2008) Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. J Theor Biol 251, 450–467
Danon L, Ford AP, House T et al. (2010) Networks and the epidemiology of infectious disease. Interdiscip Perspect Infect Dis 1:1–52
De Benedictis L, Tajoli L (2011) The world trade network. World Econ 34:1417– 1454.
Dehnen-Schmutz K, Holdenrieder O, Jeger MJ, Pautasso M (2010) Structural change in the international horticultural industry: some implications for plant health. Sci Hortic-Amsterdam 125:1–15
Doherty IA, Padian NS, Marlow C, Aral SO (2005) Determinants and consequences of sexual networks as they affect the spread of sexually transmitted infections. J Infect Dis 191: S42–S54
European Commission (2006). Working document of The Commission staff on the situation of the flowers and ornamental plants sector 34 http://www.coag.org/rep_ficheros_web/a52d1e6b51191d5e91200925d642c108.pdf. Accessed 26 September 2014
Fagiolo G, Reyes J, Schiavo S (2008) On the topological properties of the world trade web: A weighted network analysis. Physica A: Statistical Mechanics and its Applications 387:3868–3873
Ferrari JR, Preisser EL, Fitzpatrick MC (2014) Modeling the spread of invasive species using dynamic network models. Biol Invasions 16:949–960
Floerl O, Inglis G, Dey K, Smith A (2009) The importance of transport hubs in stepping-stone invasions. J Appl Ecol 46:37–45
Food and Agriculture Organization of the United Nations (2000) Multilateral trade negotiations on agriculture: a resource manual. http://www.fao.org/docrep/003/x7354e/X7354e00. Accessed 26 August 2014
Foti NJ, Pauls S, Rockmore DN (2013) Stability of the World Trade Web over time– an extinction analysis. J Econ Dyn Control 37:1889–1910
García-Robledo CA, Murcia C (2005) Comparative habitat susceptibility to invasion by Chinese ash (Fraxinus chinensis: Oleaceae) in a tropical Andean landscape. Biol. Invasions 7:405–415
Ghani AC, Swinton J, Garnett GP (1997) The role of sexual partnership networks in the epidemiology of gonorrhoea. Sex Transm Dis 24:45–56
Grapputo A, Boman S, Lindstroem L et al. (2005) The voyage of an invasive species across continents: genetic diversity of North American and European Colorado potato beetle populations. Mol Ecol 14:4207–4219
Guimerà R, Amaral LAN (2004) Modeling the worldwide airport network. Eur Phys J B 38:381–385
35
Guimerà R, Mossa S, Turtschi A, Amaral LAN (2005) The worldwide air transportation network: Anomalous centrality, community structure, and cities’ global roles. P Natl Acad Sci 102:7794–7799
Gupta S, Anderson RM, May RM (1989) Networks of sexual contacts: implications for the pattern of spread of HIV. Aids 3:807–817
Harwood TD, Xu X, Pautasso M et al. (2009) Epidemiological risk assessment using linked network and grid based modelling: Phytophthora ramorum and Phytophthora kernoviae in the UK. Ecol Model 220: 3353–3361
Holme P, Saramäki J (2012) Temporal networks. Phys Rep 519:97–125
Holway DA, Lach L, Suarez AV et al. (2002) The causes and consequences of ant invasions. Annu Rev Ecol Evol Syst 33:181–233
Hulme PE, Bacher S, Kenis M et al. (2008) Grasping at the routes of biological invasions: a framework for integrating pathways into policy. J Appl Ecol 45:403– 414
Hulme PE (2009) Trade, transport and trouble: managing invasive species pathways in an era of globalisation. J Appl Ecol 46: 10–18
Jeger MJ, Pautasso M, Holdenrieder O, Shaw MW (2007) Modelling disease spread and control in networks: implications for plant sciences. New Phytol 174:279–297
Jolly A, Wylie J (2002) Gonorrhoea and chlamydia core groups and sexual networks in Manitoba. Sex Transm Infect 78:145–151
Kaluza P, Kӧlzsch A, Gastner MT, Blasius B (2010) The complex network of global cargo ship movements. J R Soc Interface 7:1093–1103 36
Kao R, Danon L, Green D, Kiss I (2006) Demographic structure and pathogen dynamics on the network of livestock movements in Great Britain. Proc R Soc B 273:1999–2007
Keeling MJ, Rohani P (2002) Estimating spatial coupling in epidemiological systems: a mechanistic approach. Ecol Lett 5:20–29
Keeling MJ, Danon L, Vernon MC, House TA (2010) Individual identity and movement networks for disease metapopulations. P Natl Acad Sci 107:8866
Keller RP, Drake JM, Drew MB, Lodge DM (2011) Linking environmental conditions and ship movements to estimate invasive species transport across the global shipping network. Divers Distrib 17:93–102
Kirk WD, Terry LI (2003) The spread of the western flower thrips Frankliniella occidentalis (Pergande). Agr Forest Entomol 5:301–310
Kinloch M, Summerson R, Curran D (2003) Domestic vessel movements and the spread of marine pests: Risk and management approaches. Department of Agriculture, Fisheries and Forestry, Bureau of Rural Sciences, National Oceans Office Canberra
Kiss IZ, Green DM, Kao RR (2006) The network of sheep movements within Great Britain: network properties and their implications for infectious disease spread. J R Soc Interface 3:669–677
Kiss IZ, Green DM, Kao RR (2008) The effect of network mixing patterns on epidemic dynamics and the efficacy of disease contact tracing. J R Soc Interface 5:791–799
Kitsak M, Gallos LK, Havlin S et al. (2010) Identification of influential spreaders in complex networks. Lett Nat Phys 6:888–893 37
Kӧlzsch A, Blasius B (2011) Indications of marine bioinvasion from network theory. Eur Phys J B 84: 601–612
Krapivsky PLP, Redner LS (2001) Organization of growing random networks. Phys Rev E 63:066123-066123.
Lacoursière-Roussel A, Bock D, Cristescu M et al. (2012) Disentangling invasion processes in a dynamic shipping-boating network. Mol Ecol 21:4227–4241
Leslie EE (2010) Formal pig movements across Eastern Indonesia – Risk for Classical Swine Fever transmission. Society of Risk Analysis Conference 28th-29th September 2010 Sydney, Australia
Leung B, Roura-Pascual N, Bacher S et al. (2012) TEASIng apart alien species risk assessments: a framework for best practices. Ecol Lett 15:1475–93. doi: 10.1111/ele.12003
Li C, Liu S, Fang Z, Chen Y (2012) Emergence of structural properties in economic networks: a multi-local-world evolving model. Simul Model Pract Th 21:91–102
Martin V, Zhou X, Marshall E et al. (2011) Risk-based surveillance for avian influenza control along poultry market chains in South China: The value of social network analysis. Prev Vet Med 102:196–205
Martínez-López B, Perez A, Sánchez-Vizcaíno J (2009) Combined application of social network and cluster detection analyses for temporal-spatial characterization of animal movements in Salamanca, Spain. Prev Vet Med 91:29–38
Meyers LA (2007) Contact network epidemiology: bond percolation applied to infectious disease prediction and control. B Am Math Soc 44:63
Meyers LA, Pourbohloul B, Newman MEJ et al. (2005) Network theory and SARS: predicting outbreak diversity. J Theor Biol 232:71–81 38
Meyers LA, Newman MEJ, Pourbohloul B et al. (2006) Predicting epidemics on directed contact networks. J Theor Biol 240:400–418
Mohmand YT, Wang A (2013) Weighted complex network analysis of Pakistan highways. Discrete Dyn Nat Soc 2013:1–5
Morris M, Kretzschmar M (1995) Concurrent partnerships and transmission dynamics in networks. Soc Networks 17:299–318
Moslonka-Lefebvre M, Pautasso M, Jeger MJ (2009) Disease spread in small-size directed networks: Epidemic threshold, correlation between links to and from nodes, and clustering. J Theor Biol 260:402–411
Moslonka-Lefebvre M, Harwood T, Jeger MJ, Pautasso M (2012) SIS along a continuum (SISc) epidemiological modelling and control of diseases on directed trade networks. Math Biosci 236:44–52
Muirhead JR, MacIsaac HJ (2005) Development of inland lakes as hubs in an invasion network. J Appl Ecol 42:80–90
Mukherjee S (2012) Statistical analysis of the road network of India. Pramana-J Phys 79: 483-491
Natale F, Giovannini A, Savini L et al. (2009) Network analysis of Italian cattle trade patterns and evaluation of risks for potential disease spread. Prev Vet Med 92:341–350
Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256
Newman MEJ (2010) Networks: An Introduction. Oxford University Press, Inc. New York, pp 107-701 39
Ortiz-Pelaez A, Pfeiffer D, Soares-Magalhaes R, Guitian F (2006) Use of social network analysis to characterize the pattern of animal movements in the initial phases of the 2001 foot-and-mouth disease (FMD) epidemic in the UK. Prev Vet Med 76:40–55
Paini DR, Worner SP, Cook DC et al. (2010) Threat of invasive pests from within national borders. Nat Commun 1:115
Paini DR, Yemshanov D (2012) Modelling the Arrival of Invasive Organisms via the International Marine Shipping Network: A Khapra Beetle Study. PLOS ONE 7. doi: e44589
Pautasso M, Jeger MJ (2008) Epidemic threshold and network structure: The interplay of probability of transmission and of persistence in small-size directed networks. Ecol Complex 5:1–8
Pautasso M, Jeger MJ (2014) Network epidemiology and plant trade networks. AoB Plants 6:1-14
Perrings C, Williamson M, Barbier EB et al. (2002) Biological invasion risks and the public good: an economic perspective. Conserv Ecol 6:1
Perrings C, Dehnen-Schmutz K, Touza J, Williamson M (2005) How to manage biological invasions under globalisation. Trends Ecol Evol 20:212–215
Pimentel D, McNair S, Janecka J et al. (2001) Economic and environmental threats of alien plant, animal, and microbe invasions. Agric Ecosyst Environ 84:1–20
Pimentel D, Zuniga R, Morrison D (2005) Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol Econ 52:273–288
Piraveenan M, Prokopenko M, Hossain L (2013) Percolation centrality: quantifying 40 graph-theoretic impact of nodes during percolation in networks. PLOS ONE 8. doi: e53095
Porta S, Crucitti P, Latora V (2006) The network analysis of urban streets: a dual approach. Physica A: Statistical Mechanics and its Applications 369:853–866
Rasamoelina-Andriamanivo H, Duboz R, Lancelot R et al. (2011) Analysis of poultry trading network and its implication for surveillance of avian influenza and Newcastle disease, Alaotra Lake Madagascar. In: Society for Veterinary Epidemiology and Preventive Medicine (ed. Fourichon C., Pfeiffer D.U. and the SVEPM Executive Committee). Leipzig, Germany, pp 193–204
Rautureau S, Dufour B, Durand B (2011) Vulnerability of animal trade networks to the spread of infectious diseases: a methodological approach applied to evaluation and emergency control strategies in cattle, France, 2005. Transbound Emerg Dis 58(2): 110–120
Ruiz GM, Carlton JT (2003) Invasion vectors: a conceptual framework for management. In: Invasive species: vectors and management strategies. (ed. Carlton, J.T.), Island Press, Washington D.C., pp 459-504
Seebens H, Gastner M, Blasius B (2013) The risk of marine bioinvasion caused by global shipping. Ecol. Lett 16:782-790
Smith RD (2006) Responding to global infectious disease outbreaks: Lessons from SARS on the role of risk perception, communication and management. Soc Sci Med 63:3113–3123
Standards and Trade Development Facility (2013) International Trade and Invasive Alien Species. World Trade Organization, Geneva
Szendroi B, Csányi G (2004) Polynomial epidemics and clustering in contact networks. Proc R Soc Lond B: Biol Sci 271: S364–S366 41
Tatem AJ (2009) The worldwide airline network and the dispersal of exotic species: 2007-2010. Ecography 32:94–102
Van Kerkhove MD, Vong S, Guitian J et al. (2009) Poultry movement networks in Cambodia: implications for surveillance and control of highly pathogenic avian influenza (HPAI/H5N1). Vaccine 27:6345–6352.
Venette RC, Kriticos DJ, Magarey RD, et al. (2010) Pest risk maps for invasive alien species: a roadmap for improvement. BioScience 60:349–362
Wang XF, Chen G (2003) Complex networks: Small-world, scale-free and beyond. IEEE Circuits Syst Mag 3:6-21
Watts D, Strogatz S (1998) Collective Dynamics. Nature 393:440–442.
Wesolowski A, Eagle N, Tatem AJ et al. (2012) Quantifying the Impact of Human Mobility on Malaria. Science 338:267–270
Wilson ME (1995) Travel and the emergence of infectious diseases. Emerg Infect Dis1:39–46
World Health Organization (2003) Frequently Asked Questions on Severe Acute Respiratory Syndrome (SARS). http://www.who.int/csr/sars/sarsfaq/en/. Accessed 3 July 2013
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CHAPTER 2: THE NETWORK TOPOLOGY OF PLANT
PRODUCE TRADE NETWORKS AND IMPLICATIONS FOR
PEST MOVEMENT
ABSTRACT
Despite considerable gains in knowledge over the past five decades, the invasion process still remains challenging to predict. Network theory has the potential to increase our understanding of the human role in the invasion process by examining the movement of goods and associated organisms through trade networks. In this paper we demonstrate the use of network analysis to identify patterns in plant pest movement. We examined the general structural properties of one type of trade network, the plant produce trade network, and the movement of one group of plant pests, nematodes, through it in order to identify characteristics of this type of network that facilitate the movement of plant pests. A survey was conducted of 23 farms and 27 markets in three countries: Thailand, Lao PDR and Australia. Fresh vegetable produce samples were obtained from random market stalls and from each farm. Nematodes were extracted from the roots of these samples and plant-parasitic nematodes identified to genus level. Gephi network analysis software was used to generate network maps of the trade, nematode and plant-parasitic nematode movement networks in each country and to analyse them to determine the properties of each network. Structurally, plant produce trade networks resembled scale-free networks in their degree distribution. Nematode and plant-parasitic nematode networks followed this same structural pattern. The trade, nematode and PPN movement networks were all directed, with short average path lengths and low clustering coefficients. All were weakly-connected and the majority of networks were disassortative. These results suggest that plant produce networks move goods and pests in the same way, which can make it easier to predict plant pest movement patterns via these networks. This is the first study to examine the network properties of one type of real-world plant trade network, the plant produce trade network, and how produce as well as nematodes (particularly PPN) move through it. An understanding of how these characteristics facilitate the movement of goods and 43 organisms can help target intervention strategies more effectively. This knowledge can be applied to other types of networks as well as to other types of organisms. By understanding the human-assisted movement of potential invaders via networks, we are in a better position to prepare, predict and prevent the spread of invasive organisms.
INTRODUCTION
Biological invasions are a major threat to global biodiversity and food security. Initial introduction or transportation is the first and most important stage in the invasion process. Despite the considerable research attention that has been focussed on invasions, a crucial component has been largely overlooked: how human trade and transport networks contribute to invasions.
Trade and transport networks have been identified as major pathways for the introduction of IAS into new environments (Hulme 2009) yet they are not considered in major models of invasion biology (e.g. Henderson et al. 2006; Davis 2009; and Leung et al. 2004), nor has the same amount of research attention been paid to them as to components of other stages, such as species characteristics and ecosystem invasibility (reviews by Hayes and Barry 2008; and Levine and D'Antonio 1999).
The physical structure of networks (i.e. topology), has been shown to play an important role in the spread of pests and diseases (Banks et al. 2015) and can underpin patterns in disease outbreaks (e.g. Rasamoelina-Andriamanivo et al. 2014; Ortiz-Pelaez et al. 2006). Movement patterns form networks that are comprised of nodes (points) and edges (connections) that link the nodes together. Nodes represent locations or entities such as cities, markets or farms and edges represent connections, such as roads, air and shipping routes. Specific aspects of network topology such as hub nodes, shortcuts, clusters, mixing patterns, connectance and directedness (see Definitions Table 2.1) can all influence the spread of invasive organisms via networks (Banks et al. 2015). Network analysis of invasions can give invasion biologists and biosecurity agencies a broader view, helping to identify and predict patterns of invasive species outbreaks spreading via real-world networks, thus providing more strategic options regarding containment and management. 44
The study of networks has enabled a better understanding of how pests and diseases move via real-world shipping, airline and livestock trade networks (see Banks et al. 2015 for a review), yet there is very little known about plant trade networks. This has been identified as a significant gap (Pautasso and Jeger 2014), and only a few modelling studies have been published (Harwood et al. 2009; Nelson and Bone 2015), most likely because detailed plant movement data are limited and, where they do exist, access is limited. In addition, constructing an entire network usually requires a significant amount of detailed data, collected over time, which is often beyond the capacity of most researchers due to time and budget restrictions. One solution however, is to construct a general picture of the structure of plant trade networks.
A general picture of how networks move goods and organisms can be constructed by taking ‘snapshots’ of different networks, analysing them using Social Network Analysis (SNA) and then comparing them. SNA is the standard method used in network theory to measure and map network structures (Wasserman and Faust, 1994). A network snapshot is a map outlining the layout of a network, constructed from a proportion of the nodes (and links) and taken at a given point in time. These may be used to capture general trends in how plant pest networks function. Where detailed data are not available, snapshots, when combined and compared, may provide a general picture of how plant trade networks move goods and pests.
In this paper, we examine three such snapshots, assembled from data on plant and pest movement through plant produce trade networks in three countries. Individually, each of these datasets represents a snapshot of the plant produce trade network in each of these countries. Collectively they can form a picture of how plant produce trade networks may function in a general sense. These data provide rare empirical data on plant produce networks, one of the many trade networks that make up the introduction or transport stage of the invasion process. By understanding structure and flow on plant produce trade networks we can better understand how these networks facilitate as well as hinder the movement of plant pests. 45
Table 2.1. Network Parameter Definitions
Nodes are the points in the network representing locations or entities. Edges (also called degrees) are the links connecting the nodes in the network together. Diameter is the greatest distance between the two points furthest away from each other in a network connected by a shortest path. It is the “longest shortest path” though the network. Density refers to the links that are present between the nodes in the network as a proportion of the number of links possible between nodes in the network. Mixing Patterns refer to the ways in which nodes in a network tend to connect to each other: with nodes that are similar to themselves or different from themselves. The two types of mixing are called: Assortative- nodes with similar numbers of edges preferentially link to each other such that markets or farms with many trade connections (highly-connected nodes) link together and those with few trade connections (less well-connected nodes) preferentially link together. Disassortative- nodes with numerous edges link to nodes with few edges such that markets or farms with many links preferentially link with markets or farms with few trade links. Connectance refers to how well nodes in a network are connected together. There are certain nodes in a network that are more important to overall network connectance than others. These are described in terms of their “centrality”: Degree centrality determines the importance of a node to connectivity within a network by the large number of edges the node has, linking it to other nodes in the network. Betweenness centrality refers to the extent to which a particular node lies on the shortest paths between other nodes in a network, acting as a channel or funnel, moving goods from one part of the network to other parts. Closeness centrality determines a node’s importance by the short average distance from that node to all other nodes in the network such that all other nodes can be reached from that node in a short number of steps. Directedness indicates that the links between nodes in the network go in one direction rather than both ways (undirected) and indicates that goods and organisms also move in one direction. Scale-free networks are a type of network where the distribution in the number of degrees (links) all the nodes have follows a particular pattern; in this case a power law. In scale-free networks, many nodes have few degrees and a few nodes have many degrees. The nodes with many degrees are called hub nodes and are important spreaders of goods and organisms in networks. Small-world networks are another type of network where the distribution in the number of degrees (links) all the nodes have follows a different pattern; in this case a Poisson distribution curve. In small-world networks, most nodes have the same number of degrees (links). These networks are characterised as having shortcuts (short average path lengths) between nodes as well as a high clustering coefficient where nodes in these networks tend to link together to form clusters.
46
One group of organisms, nematodes, was chosen to be used as a case study. This is an economically important plant pest group containing many plant-parasitic genera. Nematodes are a good model for other plant pest groups because of their size, cryptic lifestyle, reproductive potential and pesticide resistance. This group also contains both free-living as well as plant-parasitic nematodes (PPN). Thus, inferences from this case study may be applicable to other plant pest groups.
The aim of this paper is to test the hypothesis that network analysis can be used to characterise invasive species movement. This study also aims to demonstrate the power and utility of SNA in a biosecurity context and to identify characteristics of plant-produce networks to help target intervention strategies strategically and thus to reduce potential, manage present and predict the prevalence of future invasions spreading via this type of network.
METHODS Data Collection
General Structure
To determine the general structure of plant produce networks and how produce moves on them, a survey of growers was conducted on 23 farms and traders at 27 markets in three countries (Thailand, Lao PDR and Australia) between 2013 and 2015 (Table 2.2; Appendix C). Markets and farms were chosen with the advice of local experts to represent a range of crops as well as growing areas in each country. Information was obtained on the origins and destinations of produce from interpreter-assisted interviews with vegetable traders and growers at each market and farm (Human ethics permit number: 2013/158), and also obtained produce samples from each of the growers or traders surveyed (see below). At each market sampled, additional information on the origins and destinations of produce being traded was obtained from a larger sample by questioning all traders across 2 transects along the length and width of each market (no produce samples were taken from these traders). 47
Table 2.2. Markets and farms sampled in Lao PDR, Thailand and Australia. Exact locations have not been included due to conditions in Ethics Permit 2013/158. Australia Thailand Lao PDR Dates Visited 11/2013, 9/2014, 3/2014, 5/2014 and 8/2015 3/2014-4/2014 and 10/2014 2/2014-4/2014 New South Wales Bangkok Vientiane Sydney Wholesale Market Talad Thai Salakham Cowra Farmers' Market Bangkhen Market Early Market Gunning Farmers' Market Nonthaburi Organic Market Wagga Wagga Farmers' Market Ban Yai Market Vang Vieng Victoria Ubon Ratchathani Morning Market Melbourne Wholesale Market Sri Cha Rean Market Pakse Markets Mansfield Farmers' Market Nakhon Ratchasima Dao Heuang Market Lancefield Farmers' Market Su Ra Market Savannakhet Wodonga Farmers' Market Kohn Kaen Savanxai Market Western Australia Srimeungthong Market Canning Vale Wholesale Market Tedsaban Neung Market Mandurah Farmers' Market Nakhon Pathom Margaret River Farmers' Market Pathom Mongkon Market South Fremantle Farmers' Market New South Wales Nakhon Pathom Vientiane Farm 1 Farm 1 Farm 1 Kohn Kaen Farm 2 Western Australia Farm 1 Farm 3 Farm 1 Farm 2 Pakse Farm 2 Ubon Ratchathani Farm 1 Farm 1 Farm 2 Farms Farm 2 Farm 3 Farm 3 Farm 4 Ratchaburi Farm 5 Farm 1 Farm 6 Farm 2 Savannakhet Farm 1 Farm 2 48
Movement of Nematodes
To determine the nematodes moving with fresh produce through these networks, produce samples were obtained from a subset of surveyed market stalls and from each of the farms in the three countries. Depending on the market size, 4-7 vegetable traders were chosen at random positions in each market; and 1-6 vegetable growers per state or province were chosen independently of market data. Up to 1 kg of produce was collected from each, comprising green leafy vegetables and root and tuber vegetables. Live, vermiform nematodes were extracted from the roots of each sample using two techniques: the whitehead tray technique (Hodda and Davies 2011) and a new technique called ultrasonication (Tangchitsomkid et al. 2015), whereby nematodes are extracted from roots using ultrasonic waves. PPN were visually identified to genus level in accordance with Mai et al. (1996) and by experts in Australia, Thailand and Lao PDR (Dr Nuchanart Tangchitsomkid, Tida Sangsawang and Dr Mike Hodda). The presence or absence of free-living nematodes was recorded.
Data Analysis
Three network maps were generated for each country (produce network, nematode network and PPN network) using the origin and destination points of sampled produce as well as the additional market data. These points represented nodes linked together by the movement of produce between them (from farm to market or market to market). The nematode networks consisted of the data where nematodes of any type were found, represented a subset of the trade network in each country. The PPN networks consisted of the data where PPN were found, represented a further subset. These were plotted into Gephi network analysis software (Bastian et al. 2009). Using this software, we generated network maps for each of the datasets and analysed them using SNA techniques (Wasserman and Faust 1994) to determine the properties of each network. One additional parameter, the assortativity coefficient, was calculated in Excel using the Pearson correlation coefficient function. This allowed us to establish the general structure and flow of produce, nematodes and PPN via each trade network and to make comparisons between networks. Degree distributions were calculated in Excel, logged, and a power line added to each distribution graph. 49
RESULTS Degree Distributions Structurally, plant produce trade networks had long tailed degree distributions (a scale-free network property), where 46-64% nodes had few connections and 2-4% nodes had many; they approximately followed a power law with a sharp cut-off when logarithmically transformed (Figure 2.1). The nematode and PPN networks in each country had the same structural pattern as the trade network: a long tailed degree distribution that approximately followed a power law. However, the nematode and PPN networks in Lao PDR did not follow a power law as closely as in the other two countries.
Other Network Parameters
The trade, nematode and PPN movement networks all had short average path lengths (a small-world network property) but were not highly clustered (not a small-world property) (Table 2.3). Each of the networks were also directed, with links going in one direction only and most were disassortative, where nodes with many links connected to nodes with few. Australia was the largest trade network in size, followed by Thailand and then Lao. Thailand had the largest in diameter and the longest average path length. Australia also had the largest nematode and PPN movement network of the three countries. Thailand had the smallest PPN movement network.
The trade, nematode and PPN networks in all countries had low density values, indicating that the nodes in all the networks were weakly connected. However, the smallest network, Lao, was marginally denser (more connected) than Thailand. Australia was the least dense, compared with the other countries. The Thai trade network had the largest diameter (i.e. it had the longest shortest path between the two most distant nodes in the network). However, this distinction was only apparent in the trade network as the diameter of the Thai nematode and PPN networks were similar to the other two countries. Thailand also had the longest average path length of all the trade networks, meaning produce travelled further. This distinction was carried over into the nematode network of this country, meaning nematodes also travelled further (in terms of steps) in this country. The Thai trade and nematode networks also had slightly higher clustering coefficients, indicating that nodes had a slightly greater tendency to cluster together in these networks. 50 51
Figure 2.1 (on previous page). The logged degree distributions of plant produce trade, nematode and plant-parasitic nematode movement networks in Australia, Thailand and Lao PDR each approximately follows a power law with a sharp cut-off. Blue dots represent nodes with fewer than 5 links, yellow dots nodes with 5-15 links and red dots nodes with greater than 15 links.
While the country rankings in size and density remained the same across countries, across networks, differences in diameter, clustering coefficient and average path length between countries were minimal at the PPN movement network level. All networks in all countries were disassortative, except the Thai PPN network which was weakly assortative. Meaning, in these networks, well-connected nodes generally linked to poorly-connected nodes in these networks, rather than to other well connected nodes.
Table 2.3. Network parameters of trade, nematode and plant-parasitic nematode networks in Australia, Thailand and Lao PDR.
Trade Network Parameters Australia Thailand Lao PDR Number of Nodes 126 66 53 Number of Edges 166 114 87 Density 0.01 0.03 0.03 Diameter 4 5 3 Clustering Coefficient 0.04 0.07 0.04 Average Path Length 1.92 2.27 1.16 Assortativity Coefficient -0.33 -0.28 -0.42 Nematode Network Parameters Australia Thailand Lao PDR Number of Nodes 75 46 35 Number of Edges 79 63 41 Density 0.01 0.03 0.03 Diameter 4 4 3 Clustering Coefficient 0 0.019 0.017 Average Path Length 1.25 1.76 1.24 Assortativity Coefficient -0.3 -0.02 -0.27 PPN Network Parameters Australia Thailand Lao PDR Number of Nodes 55 26 33 Number of Edges 57 23 28 Density 0.02 0.03 0.04 Diameter 2 2 2 Clustering Coefficient 0 0 0 Average Path Length 1.13 1.09 1.13 Assortativity Coefficient -0.31 0.05 -0.27 . 52
DISCUSSION Degree Distributions
The degree distributions of plant produce trade networks show that they have hub nodes. These distribution hubs not only improve connectivity and enhance the flow of goods to different parts of the network but also the flow of plant pests, such as nematodes. The similarity in the degree distribution of the nematode and PPN networks in each country indicates that plant pests flow through the network in the same pattern as goods in the larger trade network: moving from source nodes, through hub nodes, to other parts of the network. The plant produce networks in Australia, Thailand and Lao are similar in this respect to other regional trade networks such as the Madagascan poultry network (Rasamoelina-Andriamanivo et al. 2014). In this trade network also, hub nodes were identified as integral in the spread of disease where the classes of nodes with the most hubs also had the highest proportion of outbreaks. Nodes in the least connected classes had the least number of outbreaks (Rasamoelina-Andriamanivo et al. 2014).
This study is the first to demonstrate the scale-free properties of real-world agricultural produce trade networks. Horticultural plant trade networks (for the nursery trade) have also been reported to have scale-free properties (Harwood et al. 2009; Nelson and Bone 2015). This suggests that in the event of an outbreak in plant trade networks, hub nodes could act as super spreaders of pests and pathogens to different parts of the network. For example, if a small outbreak in one region of the network reached a hub node, the outbreak could more easily spread to other parts of network from that one highly-connected node. Thus, the presence of hubs in plant produce networks and other networks moving plants, could increase the potential for much larger, and more rapidly spreading, outbreaks than those moving on networks without hub nodes. Conversely, these hubs may also represent critical points in the networks for the tracing and containment of outbreaks.
Other Network Parameters
There are several other characteristics of the plant produce trade networks examined here that have important implications for the dispersal of plant pests. 53
Average Path Length
All plant produce trade networks had short average path lengths. This trend was also seen in the nematode and PPN movement networks. A short average path length indicates that a network has shortcuts through it and this is generally a property of small-world networks. However, modelled scale-free networks also have a short average path length, thought to be due to hub nodes with many connections decreasing the path length between nodes (Albert and Barabási 2002). A small average path length makes it easier for pests to be transported to the other nodes in plant produce trade networks which may allow incursions on these networks to spread more easily because pests do not have far to go in terms of links. The small average path length of the Australian trade network, for example, means that nematodes and PPN can move between nodes in this network relatively quickly in terms of steps; and the similar short average path lengths in the movement networks of nematodes and PPN indicates that they do.
The presence of this property in plant produce networks may be due to the perishability of fresh produce when compared with livestock, typically transported live. Livestock trade networks, such as the swine trade network in the UK (Smith et al. 2013) and the Italian cattle trade network (Natale et al. 2009) have slightly longer path lengths. With a fewer number of intermediate nodes (steps) along the transportation route, plant produce is moved from source to final destination faster, so produce is fresher. This may also increase the likelihood of nematodes being found on produce. If produce travels via a longer path, passing through several markets, then fewer nematodes may survive the journey. Short cuts are a property of small-world networks. However, small-world networks also have a high clustering coefficient, not present in these plant produce networks.
Clustering Coefficient
The plant produce trade networks examined here all had low clustering coefficients. This property was also seen in the nematode and PPN movement networks in each country. The clustering coefficient measures the tendency of nodes in a network to cluster together. While clusters can spread pests extensively within them, they may also temporarily confine pests within them as the nodes in the cluster have more connections to each other than to nodes outside in the rest of the network (Banks et al. 2015). This could potentially slow the spread of an invasion over the whole 54 network. The lack of clustering in plant produce networks means that a pest may not be confined within a cluster of nodes, but could move more widely (but in a more scattered pattern) in the network. The low clustering coefficient in the Australian trade network, for example, indicates that nematodes can disperse further across the network though scattered as they are not confined within clusters of nodes; and the nematode and PPN movement networks in Australia indeed follow this pattern. This means that eradication responses to incursions of plant pests spreading via this pathway need to be more rapid or cover a larger area of the network.
Other regional trade networks also have low clustering coefficients, such as the cattle, pig and poultry networks in Italy (Natale et al. 2009), UK (Smith et al. 2013) and Madagascar (Rasamoelina-Andriamanivo et al. 2014). Interestingly, regional transport networks have much higher clustering coefficients (e.g. the highway and airline networks in India (Mukherjee 2012), China (Li and Cai 2004), and USA (Li- Ping et al. 2003). A low clustering coefficient may produce patterns of isolated outbreaks arising in quick succession at different nodes in the network rather than an epidemic spreading extensively within a cluster of a network. Thus, while pests can less easily be contained in a network with low clustering, such a network is potentially more easily fragmented. When specific, influential nodes or links are targeted, spread could be prevented in a network with low clustering. This offers a distinct advantage to national biosecurity agencies. While an outbreak may spread faster, it is more easily controlled if the critical points essential to spread are known.
Lack of clustering in plant produce networks may be due to the design of the production system, where farms and growing regions are linked to more than one market. However, this may also be due to the small size of the sampled networks here. Larger numbers of farms may have revealed more regional clustering in the data. While the Barabási model predicts that the clustering coefficient of scale-free networks will decrease with network size (Albert and Barabási 2002), the plant produce trade networks presented here are quite small. Thus, the size of the plant produce trade networks would not explain the low clustering.
Density
None of the plant produce trade networks in the three countries were well-connected, as indicated by the low density values for these networks. The nematode and PPN movement networks of each country were also poorly-connected. In poorly- 55 connected networks, there are fewer link options by which goods and pests can move throughout the network. For example, the Australian trade network was the largest but least well-connected and the Lao trade network the most well-connected, despite being the smallest network. The density values of the nematode and PPN networks in these two countries reflected those of each trade network, indicating that the flow of nematodes may be determined by the number of link options present in each trade network. When nodes are well-connected, produce can move through the network more efficiently; and so do nematodes and PPN.
In this respect, they are similar to other regional trade networks, such as pig and poultry trade networks in northern Germany, East Timor and Madagascar (Büttner et al. 2013; Leslie et al. 2015; and Rasamoelina-Andriamanivo et al. 2014 respectively). Regional transport networks, by comparison, are much more well- connected (such as the airport networks in India, Bagler 2008; USA, Xu and Harriss 2008; and Italy, Guida and Maria 2007). This indicates that the flow of goods and organisms may be more easily controlled in regional trade networks, such as plant produce trade networks, by cutting specific trade links and thus fragmenting the network. When networks are more densely-connected, movement may be more difficult to control as goods and organisms can move via multiple links.
Assortativity Coefficient
The plant produce trade networks, as well as nematode and PPN movement networks, were all disassortative, with the exception of the Thai PPN network, which was weakly assortative. These terms refer to the mixing patterns of nodes within a network (see Definitions Table 2.1) and mean that, in all the plant produce trade networks, nodes were linked to others with dissimilar numbers of connections to them; and that the movement of nematodes and PPN generally follow the same pattern. Other trade networks, such as the French cattle trade network and the Madagascan poultry trade network are also disassortative (Rautureau et al. 2011; Rasamoelina-Andriamanivo et al. 2014). Disassortative mixing can produce slower moving invasions (initially) but ones that spread over a larger extent of the network than by assortative mixing (Banks et al. 2015). Thus, biosecurity responses may need to cover a wider area in plant produce trade networks, should an incursion be detected. 56
However, interpretations and predictions of the movement of pests and pathogens using network analyses of mixing patterns must be made with caution. When the movement of goods is measured (such as trade goods, cattle or poultry) networks can appear disassortative (e.g. Benedictis and Tajoli 2011; Rautureau et al. 2011; and Rasamoelina-Andriamanivo et al. 2014). But, when measuring the movement of the carrier, or vector (such as cargo ships or aeroplanes), networks can appear assortative (e.g. Kӧlzsch and Blasius 2011; Barrat et al. 2004). This does not mean that the movement of pests and pathogens via these types of mixing will follow the same patterns. To determine the movement patterns of plant pests via a trade network, one must accurately match pest associations (i.e. with the goods or with the carrier) with the specific movement the network is measuring: goods or carriers.
In summary, these snapshots of plant produce trade networks have similar network properties and this suggests that these types of networks move goods and pests in the same way: facilitating movement via hub nodes and short cuts and hindering that movement via lack of clustering. These results demonstrate the power of SNA, as well as a network approach, to provide an overview of the movement of invasive organisms during the transportation stage in the invasion process, as well as identify the characteristics of these networks influencing the dispersal of organisms through them. National plant protection agencies can use these characteristics to target intervention strategies at appropriate places in their plant produce trade network, which may help prevent the dispersal of exotic species via these networks.
CONCLUSION Despite the considerable gains in knowledge over the past five decades, the invasion process remains challenging to predict. By understanding the human contribution to the movement of potential invaders via networks, we are in a better position to predict, prepare and prevent the spread of invasive species. This paper uncovers the general network properties of plant produce trade networks and the movement of a certain group of organisms (plant pests) through them. It demonstrates how a network approach to the movement of invasive organisms can provide an overview of the transportation stage in the invasion process and identify characteristics influencing spread via a certain type of network. An understanding of these characteristics can help target interventions more effectively. This can be applied to 57 other types of plant trade networks as well as to other types of organisms. With human networks moving goods, and potential invasives, further and faster than ever before, a network perspective has the potential to increase our understanding and management of our role in the invasion process.
58
REFERENCES
Albert R, Barabási AL (2002) Statistical Mechanics of Complex Networks. Rev Mod Phys 74:47–97. doi: 10.1103/RevMod Phys.74.47
Bagler G (2008) Analysis of the airport network of India as a complex weighted network. Physica A: Statistical Mechanics and its Applications 387:2972–2980
Banks NC, Paini DR, Bayliss KL, Hodda M (2015) The role of global trade and transport network topology in the human-mediated dispersal of alien species. Ecol Lett 18:188–199
Barrat A, Barthélemy M, Vespignani A (2008) Dynamical processes on complex networks. Cambridge University Press Cambridge
Bastian M, Heymann S, Jacomy M et al. (2009) Gephi: an open source software for exploring and manipulating networks. ICWSM 8:361–362
De Benedictis L, Tajoli, L (2011) The world trade network. The World Economy 34: 1417–1454
Büttner K, Krieter J, Traulsen A, Traulsen I (2013) Static network analysis of a pork supply chain in Northern Germany—Characterization of the potential spread of infectious diseases via animal movements. Prev Vet Med 110: 418– 428.
Davis MA (2009) Invasion biology. Oxford University Press. Oxford, UK
Guida M, Maria F (2007) Topology of the Italian airport network: A scale-free small-world network with a fractal structure? Chaos, Solutions and Fractals, 31: 527–536
Harwood TD, Xu X, Pautasso M et al. (2009) Epidemiological risk assessment using linked network and grid based modelling: Phytophthora ramorum and Phytophthora 59 kernoviae in the UK. Ecol Model 220: 3353–3361
Hayes KR, Barry SC (2008) Are there any consistent predictors of invasion success? Biol Invasions 10: 483–506
Henderson S, Dawson TP, Whittaker RJ (2006) Progress in invasive plants research. Prog Phys Geog 30:25–46
Hodda M, Davies K (2011) Nematodes in Cropping Systems: Identification and Techniques, The University of Adelaide and Commonwealth Scientific and Industrial Research Organization, Canberra
Hulme PE (2009) Trade, transport and trouble: managing invasive species pathways in an era of globalisation. J Appl Ecol 46: 10–18
Kӧlzsch A, Blasius B (2011) Indications of marine bioinvasion from network theory. Eur Phys J B 84: 601–612
Leslie EE, Christley RM, Geong M et al. (2015). Analysis of pig movements across eastern Indonesia, 2009-2010. Prev Vet Med 118: 293–305
Leung B, Drake JM, Lodge DM (2004) Predicting invasions: propagule pressure and the gravity of Allee effects. Ecology 85: 1651–1660
Li W, Cai X (2004) Statistical analysis of airport network of China. Phys Rev E 69: 46106-46111
Li-Ping C, Ru W, Hang S et al. (2003) Structural properties of US flight network. Chinese Phys Lett 20:1393.
Mai WF, Mullin PG et al. (1996) Plant-parasitic nematodes: a pictorial key to genera. Comstock Publishing Associates
60
Mukherjee S (2012) Statistical analysis of the road network of India. Pramana-J Phys 79: 483–491
Natale F, Giovannini A, Savini L, et al. (2009) Network analysis of Italian cattle trade patterns and evaluation of risks for potential disease spread. Prev Vet Med 92:341–350
Nelson MF, Bone CE (2015) Effectiveness of dynamic quarantines against pathogen spread in models of the horticultural trade network. Ecol Complex 24:14–28
Ortiz-Pelaez A, Pfeiffer D, Soares-Magalhaes R, Guitian F (2006) Use of social network analysis to characterize the pattern of animal movements in the initial phases of the 2001 foot-and-mouth disease (FMD) epidemic in the UK. Prev Vet Med 76:40–55
Pautasso M, Jeger MJ (2014) Network epidemiology and plant trade networks. AoB Plants 6:1-14
Rasamoelina-Andriamanivo H, Duboz R, Lancelot R et al (2014) Description and analysis of the poultry trading network in the Lake Alaotra region, Madagascar: implications for the surveillance and control of Newcastle disease. Acta Trop 135: 10–18
Rautureau S, Dufour B, Durand B (2011) Vulnerability of animal trade networks to the spread of infectious diseases: a methodological approach applied to evaluation and emergency control strategies in cattle, France, 2005. Transbound Emerg Dis 58: 110–120
Simberloff D (2009) The role of propagule pressure in biological invasions. Annu Rev Ecol Evol Syst 40: 81–102
Smith R, Cook A, Christley R (2013) Descriptive and social network analysis of pig transport data recorded by quality assured pig farms in the UK. Prev Vet Med 108: 61
167–177
Tangchitsomkid N, Chanmalee T, Hodda M (2015) Ultrasonic extraction of plant- parasitic nematodes from plant roots. Australas Plant Pathol 44:87–96
Wasserman S, Faust K (1994) Social network analysis: Methods and applications. Vol. 8. Cambridge University Press, London
Xu Z, Harriss, R (2008) Exploring the structure of the US intercity passenger air transportation network: a weighted complex network approach. GeoJournal 73: 87– 102 62
CHAPTER 3: NEMATODES NETWORK TOO: DIVERSITY,
ABUNDANCE AND DISPERSAL VIA PLANT PRODUCE
TRADE NETWORKS
ABSTRACT The dispersal of invasive species, such as plant pests, can have major economic, environmental and social impacts worldwide. Movement of plant pests from farms to both foreign and domestic markets is facilitated by trade networks, such as plant produce trade networks. While many potential pathways of invasive plant pest entry are regulated, very few studies have examined the diversity, abundance and dispersal of soil microorganisms, such as nematodes, on plant produce while en route between origin and destination to quantify the risk of invasive plant pest introductions via these pathways. Here we show that a large range and number of live nematodes are dispersing locally, nationally and internationally via plant produce trade networks. Most (60-98%) of these nematodes are free-living forms and a small proportion (2- 40%) are plant-parasitic, and survival numbers and diversity of nematodes decrease with distance. These findings may have implications for plant biosecurity surveillance as well as human health. Moreover, nematodes provide a model for other potentially invasive species dispersing via plant produce trade networks.
INTRODUCTION Invasive species dispersal is a problem of global importance affecting trade and food production systems (McNeely et al. 2001). Invasive plant pests, in particular, pose a serious risk to agricultural production as well as market access (Merriman and McKirdy 2005). One of the principle mechanisms for invasive species dispersal is human-assisted movement via trade networks (Hulme et al. 2008), such as plant trade networks. These networks move millions of tonnes of plants and plant produce around the world (FAOSTAT 2015) and have the capacity to move plant pests as well (Pautasso and Jeger 2014). 63
Plant trade networks form a major knowledge gap in network epidemiology (Shaw and Pautasso 2014) as well as plant pathology (Moslonka-Lefebvre et al. 2009). Plant produce (fruit and vegetable) trade networks in particular, have received little research attention for the role they play in moving plant pests. While considerable information exists on plant pests in production systems as well as published interception records from ports of entry (e.g. Chen et al. 2005), qualitative and quantitative data on plant pests moving between farms of origin and port or market destinations are lacking.
This information is important because biosecurity surveillance activities, aimed at safeguarding natural ecosystems and food security, are more difficult when the identity, numbers and distances plant pests are capable of moving is unknown. Of particular interest are cryptic plant pest groups, which evade visual detection and often require specialised extraction methods and identification.
One such cryptic group are the plant-parasitic nematodes (PPN), which represent a significant risk to plant health and food security. These nematodes affect a wide range of agricultural food crops, impacting yield by as much as 100% in some situations (Brodie and Mai 1989), as well as facilitating the invasion of plant roots by other pathogens (Siddiqui et al. 2012). In addition, free-living nematodes (FLN) may represent a risk to human health as several species are capable of spreading foodborne diseases, such as Escherichia coli ( Anderson et al. 2006; Biosecurity Australia 2011). Nematodes are also a good model for other cryptic plant pest groups because they have many of the characteristics of other plant pests (Singh et al. 2013).
The incidence of nematodes in interception records is generally very low (e.g. McCullough et al. 2006), but can be high when inspection strategies are designed to detect and extract them (e.g. McNeill et al. 2011; Page-Weir et al. 2013). Seeds and plants for planting are known to transport nematodes long distances, but agricultural produce also has this potential (Singh et al. 2013). The potential for vegetable commodities to transport invasive plant pests is well recognised (STDF 2013), however, qualitative and quantitative data on the movement of nematodes via this pathway are very limited.
In this paper we examine the presence, abundance and movement of nematodes (FLN and PPN) via plant produce trade networks, to provide detailed baseline data 64 for this group of organisms. This information can be used to better inform biosecurity surveillance strategies at the farm, market and entry port level.
METHODS Data Collection
We surveyed the nematodes moving through plant produce trade networks in three countries, Lao PDR, Thailand and Australia between 2013 and 2015 (Table 2.2; Appendix C). In total, 22 farms and 26 markets were sampled from 3-6 states or provinces in each country (Table 2.2). These were chosen with the advice of local experts to represent the range of crops and growing areas in each country. The origins and destinations of produce were obtained by interviewing vegetable traders at each market and growers at each farm via an interpreter (Human ethics permit number: 2013/158). Depending on the market size and number of vegetable traders, 1-7 traders were chosen at an approximately even distribution spatially across each market; and 1-6 vegetable growers per state or province were chosen based on the advice of local experts.
A small quantity (500g- 1kg) of 2-3 types of produce was purchased from each trader and 3-6 produce samples (500g- 1.5kg including soil) were taken from each farm (3 for small household farms and up to 6 for large commercial farms) (Table 3.1 details the range of crops sampled). The produce chosen had roots attached and represented the crops available at each market and farm; these included green leafy vegetables and root and tuber vegetables.
Nematode Extraction
Nematodes were extracted from produce samples within 12 hours or, where this was not possible, refrigerated at 4°C for up to 72 hours before extraction. Live, vermiform nematodes were extracted from whole roots or the outer layers of tubers for each produce sample using two techniques: whitehead trays (Hodda and Davies 2011) and ultrasonication (Tangchitsomkid et al. 2015). Half of each sample was sonicated and then added to the other half in the whitehead tray. PPN were visually identified to genus level in accordance with Mai et al. (1996) as well as by experts in Australia, Thailand and Lao PDR (Dr Nuchanart Tangchitsomkid, Tida Sangsawang and Dr Mike Hodda). The presence of free-living nematodes was recorded. 65
Table 3.1. Range of crops sampled from markets and farms in Thailand, Lao PDR and Australia. Green Leafy Crops Root and Tuber Crops Other Farm Crops Scientific Name Common Name Scientific Name Common Name Scientific Name Common Name Allium ampeloprasum leek leek Allium cepa onion group Arachis hypogaea ground nut Allium schoenoprasum chives Allium cepa var. aggregatum eschalot Arctotheca calendula capeweed Anethum graveolens dill Allium fistulosum spring onion Benincasa hispida wax gourd Apium graveolens celery Allium sativum garlic Brassica oleracea var. botrytis cauliflower Brassica juncea chinese mustard greens Alpinia galanga galangal Brassica oleracea var. capitata cabbage Brassica rapa subsp. narinosa tatsoi Beta vulgaris beetroot Brassica oleracea var. italica broccoli Brassica oleracea alboglabra Capsicum annuum capsicum group chinese kale Boesenbergia rotunda krachai Brassica oleracea var. sabellica kale Colocasia esculenta taro Capsicum frutescens chilli Brassica rapa subsp. pekinensis chinese cabbage Curcuma longa turmeric Cucumis sativus cucumber Coriandrum sativum coriander Daucus carota subsp. sativus carrot Cucurbita pepo pumpkin Cymbopogon lemon grass Ipomoea batatas sweet potato Cucurbita pepo var. cylindrica zucchini Eruca sativa rocket Pachyrhizus erosus jicama Phaseolus vulgaris green bean Eutrema japonicum wasabi leaf Pastinaca sativa parsnip Pisum sativum var. saccharatum snow pea Foeniculum vulgare fennel Raphanus sativus radish Sisymbrium officinale mustard Ipomoea aquatica chinese spinach Solanum tuberosum potato Solanum nightshade Ipomoea aquatica morning glory Zingiber officinale ginger Solanum lycopersicum tomato
Lactuca sativa lettuce Solanum melongena eggplant Mentha mint Vigna unguiculata cowpea Vigna unguiculata subsp. Pakwan (no translation) pakwan sesquipedalis snake bean Ocimum basilicum basil Zea mays corn Petroselinum crispum parsley Spinacia oleracea spinach 66
Data Analysis
The data were collated into two datasets: the first on nematode diversity and abundance and the second on crop and PPN movement.
Differences in the nematode communities between countries, and between regions within countries were assessed using community ecology statistics, in which each country/region was considered a community. Both the number of different genera in each country/region and the frequency with which they occurred were assessed using the freeware Paleontological statistics software package (PAST) V 3.01 (Hammer et al. 2001). The multiple farms/markets in each country/region constituted replicates for the analysis. The Non-Metric Multidimensional scaling (nMDS) statistical technique, with the Bray-Curtis distance measure, was used to visualise patterns of nematode diversity across countries/regions, with stress values of less than 0.2 being interpreted as providing a useful figure (Clarke and Warwick 2001). One-way non- parametric multivariate analysis of variance (PERMANOVA), based on the Bray- Curtis measure, was used for statistical comparisons of the nematode communities from each country/region. Following significant results in PERMANOVA, similarity percentages (SIMPER) were used to determine which nematodes made the greatest contribution to differences between countries/regions (Hammer and Harper 2006).
The crop movement dataset, representing how far root crops were moved in each country, included market and farm data. The PPN movement dataset, representing how far FLN and PPN were transported in each country, included the origin and destination points of market samples on which nematodes were found. Data points were discarded if the identity of PPN, origin or destination of produce was unknown. Crops were divided into storable and perishable categories and the distances crops (and nematodes) were moved were calculated using Google Earth, taking the shortest route by road (or where appropriate, air or sea) between origin and destination points. Average distances crops (and nematodes) were moved, as well as comparative statistics were calculated in Excel. 67
RESULTS Diversity and Abundance of Nematodes
Almost all farm samples (97-100%) in all countries surveyed contained nematodes. Similarly, 75-88% of market samples also contained nematodes. The number of nematodes surviving from farms to markets varied between countries surveyed, with the highest number of nematodes surviving transport in Thailand (80% of all samples), compared to Lao (over 50%) and Australia (30%). There was, however, great variation in the number of nematodes found on market and farm samples (Table 3.2).
The majority of nematodes present on farms and moving from farms to markets in all countries surveyed were FLN, although the number varied significantly between as well as within countries (Table 3.3).
In each country, there was a dominant genus of PPN present on farms and in markets. In Thailand and Lao, Meloidogyne was the dominant genus and in Australia, Aphelenchus was the dominant PPN genus (Table 3.4). There were 6 PPN genera in Lao farm samples that were not found in farm or market samples in the other countries surveyed (Table 3.4). Two genera (Rotylenchus and Scutellonema) were only found Thailand farm samples and not in farm or market samples in the other countries surveyed.
Table 3.2. The total number of nematodes found on market and farm produce samples in Thailand, Lao PDR and Australia, the average per sample as well as standard deviation.
Thailand Lao Australia Location # Samples # Nematodes # Samples # Nematodes # Samples # Nematodes Market 87 6416 59 1812 76 4113 Farm 38 4920 54 4139 16 3560 Av. Markets 174.6 30.7 54.8 STDV Markets 73.7 72.8 107.2 Av. Farms 129.5 76.6 251.1 STDV Farms 195.9 227.4 344.1 68
Table 3.3. The percentage of free-living nematodes compared to plant-parasitic nematodes on farm and market produce samples in Thailand, Lao PDR and Australia as well as the average and standard deviation.
Thailand Lao Australia Location FLN PPN FLN PPN FLN PPN Market 95% 5% 66% 34% 82% 18% Farm 98% 2% 63% 37% 60% 40% Average Markets 72.1 3.5 21.8 11.1 33.7 8 Average Farms 127.4 2.1 51.7 14.6 61.6 65.5 STDV Markets 175.9 13.7 56.7 47.5 53.8 14.9 STDV Farms 193.8 4.4 216 25.9 31.6 55.3
Table 3.4. Similar numbers of plant-parasitic nematode genera were moved from farms to markets in Thailand and Australia but not in Lao PDR. Green cells indicate those genera that were present on farms but were absent in markets as well as farms and markets in the other countries.
The diversity of nematodes between the countries was confirmed following community analysis. When market samples were analysed, there was significant 69 overlap between nematode communities meaning several nematode genera were common to all markets (Fig. 3.1). For farm samples, the nematode communities were significantly different between Lao and Thailand (Fig 3.2) which was supported by PERMANOVA (p (same) = 0.0227). SIMPER analysis of the farm samples showed an overall average dissimilarity of 77.5% between the three countries, with FLN making the primary contribution to this variation (64%) followed by Meloidogyne (13%) and Helicotylenchus (6%).
Figure 3.1. MDS plot of all markets in Thailand (blue), Lao PDR (red) and Australia (yellow). Stress 0.02282. Minimum convex hulls (the smallest polygon embracing all points for a country) are shown. 70
Figure 3.2. MDS plot of all farms in Thailand (blue), Lao PDR (red) and Australia (yellow). Stress 0.1016. Minimum convex hulls (the smallest polygon embracing all points for a country) are shown.
Transportation Distances and Nematode Survival
Each country had their own pattern of produce and nematode movement. In Thailand, produce and nematodes were moved just as far on storable as perishable produce. In Lao, produce and nematodes were moved short average distances (<1- 100 km) on perishable produce and mid average distances (100-1000 km) on storable produce. In Australia, produce and nematodes were moved short average distances on perishable produce and long average distances (over 1000 km) on storable produce (Figure 3.3). There were no differences in the genera moved on storable and perishable produce or on produce moved short, mid or long distances. The number of nematodes on different crop samples varied greatly and therefore it was not possible to make generalisations about which crops contained the most or least nematodes. 71
Figure 3.3. The average distance produce and nematodes were transported on storable and perishable produce in Thailand, Lao PDR and Australia. Short (<1-100 km), Mid (100-1000 km), and Long (over 1000 km).
The total number of nematodes (both FL and PPN) decreased with distance (Figure 3.4) although individual numbers were variable. For example, the number of PPN moved short distances in Thailand ranged from 1-36 individuals, while the number of PPN moved mid distances ranged from 1-94 individuals. The ratio of FL to PPN generally declined with distance indicating that the number of FL nematodes declined more rapidly with distance than the number of PPN.
The range of PPN genera moved generally declined with distance (Table 3.5) although this was variable. Smaller ranges of PPN genera were moved over long distances in Thailand and Lao. In Australia, however, a similar range of genera were moved short and mid distances and fewer over long distances. Specific genera, such as Meloidogyne, were moved short, mid and long distances while others, such as Tylenchorhynchus were only moved short distances.
72
Figure 3.4. Distances total number of nematodes (plant-parasitic and free-living) were transported in Thailand, Lao PDR and Australia. Short (<1-100 km), Mid (100- 1000 km), and Long (over 1000 km). Plant-parasitic nematodes (dark blue shading) and free-living nematodes (light blue shading). 73
Table 3.5. The average number, standard deviation and range of plant-parasitic nematodes moving short, mid and long distances in Thailand, Lao PDR and Australia.
Thailand Lao PDR Australia Distance short mid long short mid long short mid long Average # 6.7 25 0 19 2.8 0 14.5 7.7 19.2 STDV 10.7 33.7 64.4 2.3 11.6 3.9 13.2 Meloidogyne Helicotylenchus Aphelenchoides Aphelenchoides Aphelenchoides Aphelenchoides Aphelenchoides Aphelenchus Pratylenchus Aphelenchus Aphelenchus Aphelenchus Aphelenchus Aphelenchus Helicotylenchus Meloidogyne Ditylenchus Helicotylenchus Ditylenchus Ditylenchus Meloidogyne Range of Tylenchorhynchus Helicotylenchus Meloidogyne Meloidogyne Helicotylenchus PPN Pratylenchus Meloidogyne Pratylenchus Tylenchorhynchus Meloidogyne Pratylenchus Tylenchus Tylenchus Pratylenchus Tylenchorhynchus Tylenchus Tylenchus 74
DISCUSSION Diversity and Abundance of Nematodes
We have demonstrated that a large range and number of live FLN and PPN disperse locally, nationally and internationally via plant produce trade networks. To our knowledge, this is the first study to report quantitative data on nematodes moving on vegetable produce via plant trade networks.
Many of the PPN genera moving via traded produce in this study contain species of quarantine importance in many countries around the world (Ravichandra 2014). Meloidogyne, the dominant PPN genus moving in Thailand and Lao, for example, contains several species that can reduce crop yields by up to 90% (Bhatti and Jain 1979; Lamberti et al. 1988). PPN cause additional, underestimated damage by facilitating the invasion of plant roots by bacteria, fungi and viruses such as Pseudomonas, Phytophthora, Fusarium and the Maize Lethal Necrosis virus complex (Whitehead 1998; Siddiqui et al. 2012; Miano 2014).
Interestingly, some PPN genera were found to disperse from farms to markets while there was no evidence of this for other genera. All of the PPN genera dispersing in the three countries of this study have previously been intercepted on root crops, rootstock or soil moving internationally (Lal et al. 2005; McNeill et al. 2011; Page- Weir et al. 2013), which suggests these genera may have adaptations for survival during transport. Previous studies concluded that the PPN found were those that could survive desiccation, disturbance or transportation within root tissue (Lal et al. 2005; McNeill et al. 2011; Page-Weir et al. 2013), though in our data, both sets of genera (moving and sedentary) varied in size, susceptibility to desiccation and mode of feeding (i.e., inside or outside root tissue) (e.g. Yeates, et al. 1993; Nickle 1991). A combination of factors may contribute to transportation success or failure in these genera and further investigation into other adaptations, such as robustness or fragility (ability to survive disturbance), would be beneficial to the general search for specialised adaptations believed to characterise invasive species.
The finding that most of these nematodes were FLN and a small number were PPN is consistent with other studies looking at soil fauna moving on footwear (McNeill et al. 2011) and the proportion of PPN in soil nematode fauna generally (Hodda and Traunspurger 2009). The presence of FLN is significant as these nematodes can have 75 a considerable impact on human health. For example, several bacterivorous and omnivorous soil nematodes ingest and shed viable E. coli, Salmonella and Listeria pathogens (Gibbs, et al. 2005; Anderson et al. 2004; Kroupitski et al. 2015). The presence of FLN on plant produce for consumption may, therefore, have significant implications for human health, especially in countries where food hygiene standards are low.
This study found a significant dissimilarity between nematode communities in Thailand, Lao PDR and Australia, primarily at the farm level and mainly in FLN numbers. These differences may be due to farm management practices in each of the countries. In Australia, the farms sampled were monocultures; in Thailand and Lao PDR, the farms sampled were polyculture. Most farms in Lao PDR were organic, whereas, most of the farms in Thailand used chemical fertilizers or pesticides. This may account for the differences recorded between farm nematode communities in these countries and would benefit from further investigation in future studies.
Furthermore, sampling issues encountered during fieldwork in Lao PDR and Australia indicates that the true numbers of FLN and PPN found in these countries are uncertain. For example, equipment failure in Lao suggests that the number of nematodes on farm samples is likely to be higher. Likewise, lack of expertise as well as access to farms in Australia means that the dataset for this country contained fewer farm samples overall and that only a proportion of PPN in each sample (20%) were identified to genus. Therefore, the number of PPN present as well as the proportion of FLN to PPN in Australia may be over or underestimates of the total number present.
Transportation Distances and Nematode Survival
This study found great variability in the dispersal distance of nematodes via plant produce trade networks; anywhere from less than 1 km to greater than 1000 km from the point of origin. However, the number and diversity of nematodes moving decreased with distance. There are few published interception records of nematodes moving long distances on plants (e.g. Lal and Lal 2005; Page-Weir et al. 2013) and we now provide detailed evidence from three countries that a range of PPN genera (n=8) are able to move locally, nationally as well as internationally. Aphelenchus, Aphelenchoides and Meloidogyne, which are all cosmopolitan, are the only genera moving long distances while a much greater range of PPN genera move short and 76 medium distances. The distance ranges are arbitrary, in that mid as well as long distance definitions can represent national as well as international transportation of crops and nematodes. For example, crops and nematodes moving mid distances (100-1000 km) as well as long distances can represent interstate movements within Australia but international destinations when exported from Thailand and Lao.
Distance has also been found to be important in the survival of other microorganisms such as those transported in ships’ ballast water. For example, the number and range of viable phytoplankton and zooplankton in ballast tanks decrease with voyage duration (Gollasch et al. 2000; Burkholder et al. 2007). This similarity may be a function of transport time and may mean there is a lower risk of transportation of nematodes (and PPN in particular) for long distances over 1000 km and a greater risk for distances between 100-1000 km, which include interstate and international locations. This highlights the need for more appropriate quarantine, surveillance and treatments for the control of live nematodes, as genera of economic importance were found moving long distances across state and national borders.
Nematode Surveillance
That nematodes are abundant on plant produce moving through trade networks may seem surprising due to the low number of nematodes and frequency of nematode interceptions in published records generally (e.g. McCullough et al. 2006, Page-Weir et al. 2013). However, routine quarantine surveillance measures may not be suited for the detection of all nematodes, as quarantine inspections typically focus on visual inspection of commodities for the presence or diagnostic symptoms of pests. Unfortunately, the symptoms of nematode damage are usually non-specific (Ravichandra 2014) and nematodes also require specialised extraction techniques and diagnosis to identify plant pests, meaning nematodes are likely to be missed during inspections.
Vegetable commodities for consumption are a regulated pathway, yet this paper is one of the first to collect quantitative scientific data on the diversity and abundance of nematodes moving via this pathway. There are numerous records of nematodes on plants for planting (e.g. Mani 1998; Latha et al. 1999) and nematodes have been recorded on traded seeds (Lal and Lal 2006), but published records of nematodes on rooted vegetable crops for consumption are rare (though see Page-Weir et al. 2013). 77
These data are important as they improve the scientific basis for quarantine decisions and guide the use of appropriate and more cost-effective disinfestation strategies.
The diversity, abundance and dispersal distances of nematodes of human and plant health importance found in this study highlights the importance of tracking shipments of plant produce yet, to our knowledge, this is not yet in place for any plant trade network, including nursery stock. In contrast, livestock tracking was introduced in many countries after animal disease outbreaks such as bovine spongiform encephalopathy and foot-and-mouth disease (Kao et al. 2007; Dubé et al. 2009), and the subsequent movement databases developed have been used to forecast, as well as retrospectively determine, the spread of other livestock diseases via trade networks (e.g. Ortiz-Pelaez et al. 2006; and Kao et al. 2007). The tracking of plant produce could also be useful to investigate destinations of produce after outbreaks of foodborne illness. For example, tracking of the contaminated sprouts associated with the E. coli outbreaks in Europe in 2011, the cost of which is estimated at over several hundred million euros, could have been useful to limit the sale of contaminated punnets (Grad et al. 2012; Karch et al. 2012). Vegetable tracking has advantages for tracing both foodborne illness outbreaks as well as pest outbreaks.
This paper provides a case study of the movement of one group of plant pests and thus provides an illustration of a broader problem: cryptic plant pest movement. Fungi, bacteria and viruses are believed to disperse via similar pathways to nematodes (Grousset et al. 2012). Thus, improved biosecurity surveillance appropriate for the detection of cryptic plant pests, such as nematodes, on asymptomatic plant produce would reduce the risk of moving agricultural plant pests as well as potential vectors of human pathogens within and between countries.
CONCLUSION
The results of this study change the way we look at nematode biosecurity. The large number and range of nematodes, especially PPN, found to be dispersing via the plant produce pathway highlights the need for appropriate biosecurity surveillance and tracking of vegetables for cryptic plant pests in order to reduce the risk of moving 78 agricultural plant pests as well as potential vectors of human pathogens within and between countries. 79
REFERENCES
Anderson GL, Kenney SJ, Millner PD, et al. (2006) Shedding of foodborne pathogens by Caenorhabditis elegans in compost-amended and unamended soil. Food Microbiol 23:146–153
Anderson PK, Cunningham AA, Patel NG, et al. (2004) Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends Ecol Evol 19:535–544
Bhatti D, Jain R (1977) Estimation of loss in okra, tomato and brinjal yield due to Meloidogyne incognita. Indian J Nematol 7:37–41
Biosecurity Australia (2011) Review of import conditions for fresh taro corms. http://daff.gov.au/biosecurity. Accessed 11 July 2016
Brodie B, Mai W (1989) Control of the Golden Nematode in the United States. Annu Rev Phytopathol 27:443–461
Burkholder JM, Hallegraeff GM, Melia G et al. (2007) Phytoplankton and bacterial assemblages in ballast water of US military ships as a function of port of origin, voyage time, and ocean exchange practices. Harmful Algae 6:486–518
Chen H, Zhang L, Chen K (2005) Review of pests intercepted by entry plant quarantine. Proceedings of the Fifth International Symposium on Biodiversity Conservation and High Technology [in Chinese]. Beijing (China): China Society of Plant Protection. pp 39–42
Clarke K, Warwick R (1994) An approach to statistical analysis and interpretation. Change in Marine Communities 2
80
Dubé C, Ribble C, Kelton D, McNab B (2009) A review of network analysis terminology and its application to foot-and-mouth disease modelling and policy development. Transbound Emerg Dis 56:73–85. doi: 10.1111/j.1865-1682.2008. 01064.x
Food and Agriculture Organization of the United Nations Statistics Division (2015) http://faostat3.fao.org/browse/T/TP/E. Accessed 8 April 2016
Gibbs DS, Anderson GL, Beuchat LR et al. (2005) Potential role of Diploscapter sp. strain LKC25, a bacterivorous nematode from soil, as a vector of food-borne pathogenic bacteria to preharvest fruits and vegetables. Appl Environ Microbiol 71:2433–2437
Gollasch S, Rosenthal H, Botnen H et al. (2000) Fluctuations of Zooplankton Taxa in Ballast Water during Short-Term and Long-Term Ocean-Going Voyages. Int Rev Hydrobiol 85:597–608
Grousset F, Petter F, Suffert M, Roy A (2012) EPPO study on the risk of imports of plants for planting: description and details of the first outcomes. EPPO bulletin 42:185–190
Hammer Ø, Harper DAT, Ryan PD (2001) PAST: Paleontological statistics software package for education and data analysis. Palaeontol Electron 4:9
Hammer, Ø, Harper. D (2006) Paleontological Data Analysis. Oxford, Blackwell Publishing, Oxford, UK
Hodda M, Davies K (2011) Nematodes in Cropping Systems: Identification and Techniques. The University of Adelaide and Commonwealth Scientific and Industrial Research Organization, Canberra
Hodda M, Traunspurger W (2009) Nematodes as environmental indicators. In: Wilson MJ and Thomae K-D (ed.) CABI Publishing, Wallingford, UK, pp 45–94 81
Hulme P, Bacher S, Kenis M et al. (2008) Grasping at the routes of biological invasions: a framework for integrating pathways into policy. J Appl Ecol 45:403– 414
Kao RR, Green DM, Johnson J, Kiss IZ (2007) Disease dynamics over very different time-scales: foot-and-mouth disease and scrapie on the network of livestock movements in the UK. J R Soc Interface 4:907–916
Kroupitski Y, Pinto R, Bucki P, et al. (2015) Acrobeloides buetschlii as a potential vector for enteric pathogens. Nematology 17:447–457
Lal A (2005) Nematodes intercepted in introduced germplasm of horticultural crops. Indian J Plant Prot 33:282–285
Lal R, Lal A (2006) Plant-parasitic nematodes intercepted from seeds, soil clods and packing material under import quarantine. J New Seeds 8:49–60
Lamberti F, Boiboi J, Ciancio A (1988) Losses due to Meloidogyne incognita in okra in Liberia. Nematol Mediterr 16:5–6
Latha S, Babu D, Reddy NSO, Sharma R (1999) Plant-parasitic nematodes intercepted from imported ornamental plants. Indian Phytopathol 52:283–284
Mai WF, Mullin PG et al. (1996) Plant-parasitic nematodes: a pictorial key to genera. Comstock Publishing Associates
Mani A (1998) Plant-parasitic nematodes intercepted from imported plants and organic materials. Nematol Mediterr 26:93–96
McCullough DG, Work TT, Cavey JF et al. (2006) Interceptions of nonindigenous plant pests at US ports of entry and border crossings over a 17-year period. Biol Invasions 8:611–630
82
McNeely JA, Mooney HA, Neville LE et al. (2001) A Global Strategy on Invasive Alien Species. IUCN Gland, Switzerland, and Cambridge, UK
McNeill M, Phillips C, Young S et al. (2011) Transportation of nonindigenous species via soil on international aircraft passengers’ footwear. Biol Invasions 13:2799–2815
Merriman P, McKirdy S (2005) Technical guidelines for the development of pest specific response plans. Plant Health Australia
Miano DW (2014) Maize Lethal Necrosis Disease: A real threat to food security in the eastern and central Africa region. Faculty of Agriculture, University of Nairobi
Moslonka-Lefebvre M, Pautasso M, Jeger MJ (2009) Disease spread in small-size directed networks: Epidemic threshold, correlation between links to and from nodes, and clustering. J Theor Biol 260:402–411
Nickle WR (1991) Manual of Agricultural Nematology. Marcel Dekker, Inc., NY, USA
Ortiz-Pelaez A, Pfeiffer D, Soares-Magalhaes R, Guitian F (2006) Use of social network analysis to characterize the pattern of animal movements in the initial phases of the 2001 foot-and-mouth disease (FMD) epidemic in the UK. Prev Vet Med 76:40–55
Page-Weir N, Jamieson L, Bell N et al. (2013) Interception and hot water treatment of mites and nematodes on root crops from the Pacific Islands. NZ Plant Prot 66:17– 28
Pautasso M, Jeger MJ (2014) Network epidemiology and plant trade networks. AoB Plants 6:1-14 83
Ravichandra N (2014) Nematodes of Quarantine Importance. Horticultural Nematology. Springer, pp 369–385
Shaw M, Pautasso M (2014) Networks and plant disease management: concepts and applications. Annu Rev Phytopathol 52:477–493
Siddiqui ZA, Nesha R, Singh N, Alam S (2012) Interactions of plant-parasitic nematodes and plant-pathogenic bacteria. Bacteria in agrobiology: Plant probiotics. Springer, pp 251–267
Standards and Trade Development Facility (2013). International Trade and Invasive Alien Species. World Trade Organization, Geneva
Tangchitsomkid N, Chanmalee T, Hodda M (2015) Ultrasonic extraction of plant- parasitic nematodes from plant roots. Australas Plant Pathol 44:87–96
Whitehead A (1998) Plant Nematode Control, CAB International. New York
Yeates GW, Bongers T, De Goede RG et al. (1993) Feeding habits in soil nematode families and genera-an outline for soil ecologists. J Nematol 25:315–31 84
CHAPTER 4: NETWORK ANALYSIS AND PLANT PEST
INFESTATION RISK IN PLANT PRODUCE TRADE
NETWORKS
N.C. Banks 1, 2, 4, K.L. Bayliss 1, 2, D. R. Paini 2, 4, N. Tangchitsomkid 5, T. Chanmalee 6, T. Sangsawang 5, P. Songvilay 7, N. Phannamvong 7, S. Thamakhot 7, and M. Hodda 2,3,4
1 School of Veterinary and Life Sciences, Murdoch University, 90 South Street, Murdoch, WA 6150 2 Plant Biosecurity Cooperative Research Centre, LPO Box 5012, Bruce, ACT 2617 3 National Research Collections Australia, Building 101, Clunies Ross Street, Black Mountain, ACT 2601 4 Commonwealth Scientific and Industrial Research Organisation, Clunies Ross Street, Black Mountain, ACT 2601 5 Department of Agriculture, Phaholyothin Road, Chatuchak, Bangkok 10900 Thailand 6 Centre for Agricultural Biotechnology, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom 73140, Thailand 7 Department of Agriculture, Ministry of Agriculture and Forestry Lao PDR, PO Box 811, Vientiane Capital, Lao, PDR
Keywords: network analysis, invasive alien species, nematodes, plant-parasitic, centrality
ABSTRACT Network analysis is a promising approach to managing invasive alien species (IAS) spread which has the potential to reduce the costs associated with incursions by helping target biosecurity measures at points where they are most effective. This 85 study examines the presence and movement of one group of IAS, nematodes, via plant produce trade networks. It utilises network snapshots to identify critical points at which to target surveillance and intervention efforts. A survey of 22 farms and 26 markets was conducted between 2013 and 2015 in three countries (Australia, Thailand and Lao PDR). Vegetable produce was sampled from each market and farm, and nematodes were extracted from the roots of each sample. Plant-parasitic nematodes were identified to genus level and numbers of free-living nematodes recorded. Origin and destination points of produce were used to generate three network maps for each country: the plant produce trade, nematode and PPN movement networks. Network properties were analysed using Social Network Analysis to identify degree, betweenness and closeness central nodes critical to flow through each network. Results indicate that nematodes were present and moving on a high proportion of nodes and links in each trade network (97-100% of the nodes; 93- 94% of the links), while plant-parasitic nematodes were present and moving over a smaller proportion of each network (47-84% nodes; 30-78% links). Betweenness and degree (particularly in-degree) centrality measures of these nodes and links corresponded with nematode and plant-parasitic nematode infestation risk. These nodes and links are likely to be the most effective biosecurity intervention points to prevent the movement of nematodes and other IAS through these networks. The closeness centrality measure was not effective in identifying particular nodes as central to connectedness in any of the networks. This study shows that network snapshots can be a useful predictive tool and that a network perspective to invasions can provide decision-makers with a broader view of the ‘field of play’, thereby making it easier to determine where intervention and monitoring strategies would be most effectively targeted.
INTRODUCTION Effective biosecurity measures exist for many invasive alien species (IAS), including surveillance, detection, eradication and containment strategies (SIPPC 2006). However, the cost or difficulty in adopting certain strategies can make them prohibitive, especially for developing nations, who may not have the resources to maintain the plant protection capability required to meet international trade standards (Van De Graff and Khoury 2009). If effective biosecurity measures could be targeted at strategic, critical points, the potential costs of national plant protection could be
86 reduced and these measures could become more feasible to a greater number of countries.
The human-mediated movement of IAS occurs along trade and transport infrastructure (Hulme 2008, 2009): road, rail, air and shipping routes connecting agricultural production sites, ports and markets as well as wilderness areas together in a network. As a result, network analysis is one promising approach to managing IAS spread (Banks et al. 2015). It has the potential to reduce the costs associated with incursions by helping target resources to sites where they are most effective. Network science has shown that many networks move goods and organisms in predictable ways and that there are often specific nodes (sites) or links (connections) in a network that can enable (or disable) the flow of goods or organisms through the network. These points have been found critical in preventing the spread of human and animal pathogens via human trade and transport networks (Ortiz-Pelaez et al. 2006; Rasamoelina-Andriamanivo et al. 2014). The most critical nodes are those with three particular properties: degree, betweenness and closeness centrality (Banks et al. 2015).
The movement of IAS (which include pest species as well as infectious agents) via human networks is an emerging discipline (Banks et al. 2015). There are numerous studies that have looked at the spread of human viruses via transport networks (e.g. Salathé et al. 2010; Bozick and Real 2015), as well as animal virus spread via livestock trade networks (e.g. Kiss et al. 2006; Ortiz-Pelaez et al. 2006). Unfortunately, there are few empirical studies examining the movement of plant pests via plant trade networks, which represents a major gap in this field of study considering how important plant trade and agriculture are to the global economy and food security (Van De Graff and Khoury 2009; Pautasso and Jeger 2014).
One reason for the lack of studies in plant trade networks is that data on these networks are not accessible to most scientists and collecting data for an entire network is a time and labour intensive task. However, it is possible to take snapshots of parts of networks at a given point in time to create a general picture of how networks move goods and organisms. This method has been used successfully to uncover general trends in how certain types of networks operate (See Chapter 2).
While there has been some work on horticultural networks (e.g. Pautasso et al. 2010; Nelson and Bone 2015), plant produce trade networks have received very little 87 attention in network science despite the fact that these networks move millions of tonnes of agricultural produce within and between countries each year (FAOSTAT 2015). The potential for unintentional movement of IAS via plant produce trade networks is well recognised (STDF 2013) and there are mitigation strategies in effect to reduce the likelihood of movement (e.g. SIPPC 2006); however there is little information available to target these measures effectively.
Here we examine the unintended movement of one group of organisms, nematodes, via plant produce (vegetable) trade networks. Nematodes are an ideal model for many other plant pest groups because they have many of the characteristics of other plant pests. They occur in large numbers in association with plants. They have a range of life cycles, are often cryptic in habit, and vary in dispersal abilities and mechanisms. Finally, this group contains both free-living and plant-parasitic forms (PPN); and hence is a good model for other plant pest groups, such as saprophytic and biotrophic plant pathogens. By themselves, nematodes are an economically important group containing many plant-parasitic genera with direct damage estimates amounting, per annum, to $125 billion dollars worldwide (Chitwood 2003). Indirectly, the estimates are likely to be considerably higher as nematodes can facilitate the invasion of other organisms, such as Maize Lethal Necrosis virus (Zacheo 1993; Miano 2014).
METHODS Data Collection
We surveyed the plant produce trade network and nematode movement in three countries, Australia, Thailand and Lao PDR between 2013 and 2015 (Table 2.2; Appendix C). In total, 22 farms and 26 markets were sampled from 3-6 states or provinces in each country (Table 2.2, Figure 4.1). These were chosen with the advice of local experts to represent the range of crops and growing areas in each country. The origins and destinations of produce were obtained from interpreter-assisted interviews with vegetable traders at each market and growers at each farm (Murdoch University Human ethics permit number: 2013/158). Depending on the market size and number of vegetable traders, 1-7 traders were chosen at an approximately even distribution spatially across each market; and 1-6 vegetable growers per state or province were chosen independently of market data.
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A small quantity (500g- 1kg) each of 2-3 types of produce (Table 3.1) was purchased from each trader and three to six produce samples (500g- 1.5kg including soil) were sampled from each farm (3 for small household farms and up to 6 for large commercial farms). The produce had roots attached and represented the crops available at each market and farm including green leafy vegetables and root and tuber vegetables. At each market sampled, additional information on the origins and destinations of produce being traded was obtained from a larger sample by questioning all traders across two transects along the length and width of each market (no additional produce samples were taken from these traders).
Produce samples were subjected to nematode extraction within 12 hours or, where this was not possible, refrigerated at 4°C for up to 72 hours before extraction. Live, vermiform nematodes were extracted from whole roots or the outer layers of tubers for each produce sample using two techniques: whitehead trays (Hodda and Davies 2011) and ultrasonication (Tangchitsomkid et al. 2015). Half of each sample was sonicated and then added to the other half in the whitehead tray. Plant-parasitic nematodes (PPN) were visually identified to genus level in accordance with Mai et al. (1996) as well as by experts in Australia, Thailand and Lao PDR (Dr Nuchanart Tangchitsomkid, Tida Sangsawang and Dr Mike Hodda). The presence or absence of free-living nematodes was recorded.
Data Analysis
The origin and destination data for all produce samples, as well as the additional market data, were used to generate three network maps for each country using Gephi network analysis software (Bastian et al. 2009). The first was the overall trade network in plant produce which used all data for all samples. The nematode network (a subset of the trade network) consisted of the data where nematodes of any type were found. The PPN network (a subset of the nematode network) consisted of the data where PPN were found. Social Network Analysis (Wasserman and Faust 1994) was used to analyse the overall properties of each network as well as identify the individual nodes critical to flows through the network (i.e. degree, betweenness and closeness central nodes). These are defined as follows: 89
Degree central nodes connect a network together through the large number of connections they have to other nodes in the network (Newman 2010). Degree centrality can be separated into out-degree central nodes and in-degree central nodes; the greatest number of outgoing links and the greatest number of ingoing links respectively. Out-degree central nodes will hereafter be referred to as sources and in- degree central nodes as sinks.
Betweenness central nodes lie on the shortest paths between all other nodes in the network. These nodes are important because goods and organisms moving around in one part of the network will most likely pass through them to reach other areas of the network (Banks et al. 2015).
Closeness central nodes are those that are a short total distance to all other nodes in a network. These points can more easily be infected as well as spread IAS due to their close proximity to all other nodes in a network (Büttner et al. 2013).
RESULTS Nematode Presence and Movement
Nematodes were present and moving on a large percentage of the trade network (97- 100% of the nodes; 93-94% of the links) with PPN moving over a smaller percentage of each network (47-84% nodes; 30-78% links) (Figure 4.2, Table 4.1). This proportion varied from country to country: there was least movement of PPN in the Thai network (30% of infected links had PPN) and greatest movement in the Lao network (78% of infected links had PPN) with 55% infected links in Australia (Table 4.1).
Centrality
In each country, market centres and growing regions connected the nodes in each network together in terms of the following centrality measures: degree, betweenness, and closeness. Large markets were the most central nodes in the overall trade networks. Large producers and growing regions, as well as large markets, were central in the nematode and PPN networks.
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Figure 4.1. Farm and market survey sites in Australia, Thailand and Lao PDR. 91
Table 4.1. Proportion of nodes and links infected by nematodes and plant-parasitic nematodes in the trade networks of Australia, Thailand and Lao PDR.
Australia Thailand Lao PDR
% Nodes % Links % Nodes % Links % Nodes % Links Nematodes Present 98 94 100 93 97 93 PPN Present 72 55 47 30 84 78
Australia
The most critical points in the Australian trade network were: Sydney (New South Wales (NSW)), Footscray (Victoria (VIC)) and Canning Vale (Western Australia (WA)) Wholesale Markets (WM) almost equally (degree), Canning Vale WM (betweenness), and one small market (Carnarvon, WA) by a small percentage point (0.3, closeness) (Figure 4.2a). The most critical points in the Australian nematode network were: Sydney WM and one large commercial farm, WA Farm 2 (degree), Canning Vale WM (betweenness), and three small growing regions in WA (Baldivis, North Perth and Oakford) (closeness) (Figure 4.2b). The Australian PPN network (Figure 4.2c) had the same centrality points as the nematode network except that closeness centrality measures were so similar between large numbers of nodes, that the most central nodes could not be delineated. Additional data are given in Appendix A (Tables A.1–A.3).
Canning Vale WM was an influential node in the Australian trade network, in terms of degree and betweenness, with the highest percentage share of links of any node in the network (18%). It was also the most betweenness central node in the nematode movement network, meaning that it lay on the shortest paths between other nodes in the network, despite only having 9% of the infected links. Sydney WM and Footscray WM ranked similarly in terms of degree centrality holding 12% and 16% of the links in the trade network. However, in the nematode movement network, Sydney WM and one large commercial farm were more influential in terms of degree (Sydney WM 14% all in-degree and WA Farm 2 30% all out-degree links). Most nodes in the Australian trade and nematode movement networks ranked as equally close to each other, with the difference between them being less than one degree (link). Effectively, no particular node stood out as having a shorter average distance from it to all other nodes in the network compared with the other network nodes.
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Figure 4.2. Plant produce trade network, nematode and plant-parasitic nematode movement networks for Australia (a-c), Thailand (d-f) and Lao PDR (g-i). The white dot or ring represents the most betweenness central node, black dots, the most degree central nodes and dark grey, the most central nodes in terms of closeness. 93
Thailand
The critical points in the Thai networks were: Talad Thai market (degree), Talad Thai market (betweenness), and two growing regions in Ubon Ratchathani and Kohn Kaen equally (closeness) (Figure 4.2d). The most critical points in the Thai nematode network were: Talad Thai market (degree), Talad Thai market (betweenness), one growing region in Ratchaburi (closeness) (Figure 4.2e). For the Thai PPN network, the most degree central node was Talad Thai, only one small market in Bangkok stood out for betweenness (Bangkhen), two other markets (Simum Muang and Sapanmai) in Bangkok were barely separated from the others in terms of closeness and the most degree central node matched those of the larger networks: Talad Thai (Figure 4.2f). Additional data are given in Appendix A (Tables A.4–A.6).
Overall, Talad Thai market was the most influential node in both the trade and nematode movement networks in Thailand (in terms of degree and betweenness). As with the Australian networks, most of the nodes in the Thai trade and nematode movement networks were equally close to each other.
Lao PDR
The most critical points in the Lao trade network were: Early Market, Vientiane (degree), Morning Market, Vang Vieng (betweenness), and Vang Tao Quarantine Station (closeness) (Figure 4.2g). The most critical points in the Lao PDR nematode network were: Paksong, Champasak (degree), Morning Market, Vang Vieng (betweenness), but many nodes possessed similar closeness values (Figure 4.2h). In the Lao PPN network, only Paksong, Champasak stood out as a major degree central node. There was little difference between nodes for the other centrality measures in this network (Figure 4.2i). Additional data are given in Appendix A (Tables A.7–A.9).
Vang Vieng market in Vientiane Province was the most critical point in the Lao trade and nematode network in terms of betweenness, despite only having shares of 8% and 4% of the links in the Lao trade and nematode movement networks. The most degree central node in the Lao trade network was the Early Market in Vientiane Capital which had 34% share of the links (all in-degrees). However, in the nematode movement network, this node possessed only 8% of the infected links. The most degree central node in the Lao nematode movement network was Paksong, Champasak which had 36% of the infected links in the network. As with the networks 94 in Australia and Thailand, most nodes in the Lao trade and nematode networks were only marginally separated in terms of closeness ranking.
Source and Sink Nodes
For Australia, the two main source nodes in the overall trade network were also the top source nodes in the nematode and PPN networks (Table 4.2). Three main wholesale markets (Sydney, Footscray and Canning Vale) were consistently ranked as the main sinks in the trade, nematode and PPN networks for Australia (Table 4.3).
Table 4.2. Main source nodes (nodes with highest number of outgoing links) in Australian networks. The number of links for each node are in brackets. Australia Trade Network Nematode Network PPN Network Rank Sources 1 WA Farm 1 (15) WA Farm 2 (15) WA Farm 2 (14) 2 WA Farm 2 (9) WA Farm 1 (9) WA Farm 1 (9) 3 Cobram, VIC (7) NSW Farm 1 (4) NSW Farm 1 (4)
Table 4.3. Main sink nodes (nodes with highest number of ingoing links) in Australian networks. The number of links for each node are in brackets.
Australia Trade Network Nematode Network PPN Network Rank Sinks 1 Sydney WM, NSW (28) Sydney WM, NSW (15) Sydney WM, NSW (12) 2 Footscray WM, VIC (26) Canning Vale WM, WA (8) Footscray WM, VIC (7) 3 Canning Vale WM, WA (21) Footscray WM, VIC (6) Canning Vale WM, WA (5) 95
In Thailand, the main source nodes were not consistent across trade, nematode and PPN networks (Table 4.4). However, one major market, Talad Thai, ranked consistently as the main sink node across the Thai trade, nematode and PPN networks (Table 4.5).
For Lao PDR, two major source nodes ranked consistently across the trade, nematode and PPN networks (Table 4.6). The main sink nodes in the overall trade network were among the main sinks in the nematode and PPN networks (Table 4.7) with one market, Early Market, Vientiane Capital, ranking the highest consistently across the trade, nematode and PPN networks in Lao PDR.
DISCUSSION
This study has demonstrated that nematodes are moving through plant trade network nodes and links in Australia, Thailand and Lao PDR. The betweenness and degree (particularly in-degree) centrality measures of these nodes and links also corresponded with nematode and plant-parasitic nematode infestation risk. Therefore, these are likely the most effective biosecurity intervention points in these networks to stop nematode movement, as they are the most critical to the flow of goods as well as organisms.
Plant-parasitic nematodes were found to comprise only a small proportion of the nematodes present in the network. This is not surprising as PPN generally make up a smaller proportion of soil nematodes (20-40%; Neher 2010) as well as recorded soil nematode intercepts (4.5%, McNeill et al. 2011; and 34%, Page-Weir et al. 2013). What is surprising, however, is that nematodes were found on so many nodes and links. This is despite the fact that produce was generally clean with only minute quantities of soil, yet there were still live nematodes present. Furthermore, the prevalence of nematodes in these networks was significantly higher than the prevalence of diseases reported in livestock trade networks ( Kao et al. 2007; Ciccolini et al. 2012; Tinsley et al. 2012). It is worth investigating whether this trend also applies to other cryptic plant pest groups such as viruses, bacteria and fungi.
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Table 4.4. Main source nodes (nodes with highest number of outgoing links) in Thailand networks. The number of links for each node are in brackets. Thailand Trade Network Nematode Network PPN Network Rank Sources 1 Srimeungthong Market, KK (10) Bangkok (4) Nonthaburi (3) China (4) Sri Cha Rean Market, UR (8) Talad Thai, BKK (3) Mueung district, KK (2) 2 Nonthaburi (3) Sisakhet (3) Talad Thai, BKK (6) Mueung district, KK (2) Simum Muang Market, BKK (2) Nahkon Chai Si district, Nakhon Pathom (2) Kohn Kaen (2) 3 Chiang Mai (2) Phetchabun (2) Ratchaburi (2) India (2) Pak Chong district, Nakhon Ratchasima (2) Po Tharam district, Ratchaburi (2) 97
Table 4.5. Main sink nodes (nodes with highest number of ingoing links) in Thailand networks. The number of links for each node are in brackets. Thailand Trade Network Nematode Network PPN Network Rank Sinks 1 Talad Thai, BKK (23) Talad Thai, BKK (14) Talad Thai, BKK (8) 2 Sri Cha Rean Market, UR (14) Pathom Mongkong Market, Nakhon Pathom (8) Bangkok (3) Su Ra Market, NR (12) Tedsaban Neung Market, KK (7) Sri Cha Rean Market, UR (2) 3 Kohn Kaen (2) Bangkhen Market, BKK (2)
Table 4.6. Main source nodes (nodes with highest number of outgoing links) in Lao PDR networks. The number of links for each node are in brackets. Lao PDR Trade Network Nematode Network PPN Network Rank Sources 1 Paksong district, Champasak (11) Paksong district, Champasak (9) Paksong district, Champasak (8) Vientiane Capital (5) Vientiane Capital (3) Vientiane Capital (2) Vietnam (5) Vietnam (3) Kaysone Phomvihane district, Savannakhet (2) 2 Thailand (5) Morning Market, Vientiane Capital (2) Vientiane (2) Champasak (2) Kaysone Phomvihane district, Kaysone Phomvihane district, 3 Savannakhet (4) Savannakhet (2) Morning Market, Vientiane Capital (2) Vientiane (2) 98
Table 4.7. Main sink nodes (nodes with highest number of ingoing links) in Lao PDR networks. The number of links for each node are in brackets. Lao PDR Trade Network Nematode Network PPN Network Rank Sinks Early Market, Vientiane Capital (22) Early Market, Vientiane Capital (7) Early Market, Vientiane Capital (4) 1 Dao Heuang Market, Pakse, Champasak (4) Organic Market, Vientiane Capital (4) 2 Savanxai Market, Savannakhet (11) Savanxai Market, Savannakhet (5) Savanxai Market, Savannakhet (3) Dao Heuang Market, Pakse, Champasak (5) Morning Market, Vang Vieng, Vientiane (3) 3 Organic Market, Vientiane Capital (10) Organic Market, Vientiane Capital (3) Morning Market, Vang Vieng, Vientiane (3) 99
The results suggest that the strategic, critical points at which to target interventions for nematodes (and potentially other IAS) moving through plant produce trade networks are the most degree and betweenness central nodes as these nodes represent the most central nodes in the flow of nematodes as well as goods. This indicates that the nodes with the highest degree centrality in the trade network are the most likely to spread nematodes, due to the high number of ingoing and outgoing links to and from these nodes to other nodes in the network. The nodes with the highest betweenness centrality are those most likely to act as funnels, channelling the movement of nematodes from one region of the network to another, because they more often lie on the shortest paths between all the nodes in the network.
The importance of both degree and betweenness centrality in the spread of organisms is supported by other studies of trade and transport networks. Global shipping ports at high risk of becoming infested with the invasive Khapra beetle have been identified using degree centrality (Paini and Yemshanov 2012). Additionally, nodes with the highest betweenness centrality were identified as the key players in the spread of foot-and-mouth disease in the livestock trade network in the United Kingdom (Ortiz-Pelaez et al. 2006). This strongly suggests that degree and betweenness central nodes are the critical points at which to target intervention efforts to prevent the movement of pests through networks. For biosecurity purposes, monitoring these nodes may help detect incursions early, and applying quarantine measures at these points could prevent the flow of potentially invasive organisms through networks. Therefore, for biosecurity purposes where trade network information may be available to NPPOs but not pest movement data, focussing surveillance and monitoring at betweenness central nodes may help detect incursions early, and applying quarantine measures at degree central nodes could prevent potential infestation and dispersal of potentially invasive organisms through these networks.
While the two centrality measures above were found useful in this study, the third, closeness centrality, was not effective in identifying particular nodes as being central to connectedness in any of the networks. Thus, no particular node stood out as having a shorter average distance from it to all other nodes in the network. This is unusual because this measure has been found to be important in studies of livestock networks. For example, the closeness centrality measures of source nodes of
100 infection were found critical to the extent of disease spread in simulations across the modelled Italian cattle trade network, with their proximity to all other nodes making them critical in the spread of disease across a network (Natale et al. 2009). This contradiction may be explained by the average path length between nodes in that particular livestock trade network (7.8), compared to that of the plant produce networks in the current study (less than 2). In plant produce networks, all nodes are close to all other nodes as they can be reached in very few links. This may explain the inability of the closeness centrality measure to identify critical nodes in the plant produce networks.
The major source and sink nodes in the trade networks were among the main source and sink nodes in the nematode and PPN movement networks, but were not always of the same rank. Thus, while a node in the trade network may have the greatest number of inward and outgoing links, there is no guarantee that a high proportion of those links will be infected with nematodes or, indeed, PPN. Interestingly, the top sink node in each trade network was always the top sink node in the nematode and PPN networks. This suggests that to find nematodes moving through the system, major sink nodes are likely to be the best places for surveillance, as opposed to the source nodes as these are the most likely places in the network to be infected if an outbreak were to occur. Nodes with high in-degree values have also been associated with infection and spread of animal diseases in livestock networks (Palisson et al. 2016). Thus, sink node hubs represent another critical point in plant trade networks to target surveillance and intervention efforts.
The major advantage of a network perspective to invasions is that it provides decision-makers with a broader view of the ‘field of play’, making it easier to determine which intervention and monitoring strategies would be the most effective and where (Banks et al. 2015). A network approach could also facilitate the production of more comprehensive models to test the efficacy of certain strategies on pest spread, before they are used in the real world.
Knowledge of how goods and organisms flow through networks and the important points that facilitate spread could be the most effective way to prevent the introduction and movement of IAS. This study determined the extent of nematode pest movement and identified the critical points in plant produce trade networks that facilitate that movement, through the use of network snapshots. Although a full 101 network model would result in greater confidence in the accuracy of these identifications, such data are often difficult to obtain or simply unavailable. Given this, we have shown that a network snapshot, such as the ones presented here, can be useful predictive tools, facilitating improved biosecurity systems.
One important consideration is the simplified sampling approach used here, which may increase uncertainty by under or overestimating the number of infected nodes and links in a network. For example, while infested market nodes indicate that the source farm nodes (connected to them via PPN-infested produce) are also infested, the infestation status of a source farm (where PPN were found) does not necessarily mean that all the nodes linked to that farm are automatically infested. Farm hygiene practices are important in determining whether organisms move off farm or not and including this factor was beyond the scope of this study.
CONCLUSION We have demonstrated empirically that nematodes (and potentially other plant pathogens) can move through plant produce trade network nodes and links. Further, the betweenness and degree (particularly in-degree) centrality measures of these nodes and links correspond with nematode and PPN infestation risk. This paper demonstrates that network analysis can help identify movement and critical points in plant trade networks. This may be useful to target placement of limited biosecurity resources, thus helping reduce the costs of adopting the most effective quarantine strategies and making them more feasible to more countries. When appropriate interventions are targeted at strategic points in a trade network, they could help reduce the potential for incursions, better manage the ones that do occur, and make future incursions or invasions more predictable.
ACKNOWLEDGEMENTS The authors would like to acknowledge the support of the Australian Government’s Cooperative Research Centres Programme and Murdoch University for financial support and the Commonwealth Scientific and Industrial Research Organisation, the Department of Agriculture, Thailand, as well as the Ministry of Agriculture and Forestry, Lao PDR for in-kind support.
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REFERENCES
Banks NC, Paini DR, Bayliss KL, Hodda M (2015) The role of global trade and transport network topology in the human-mediated dispersal of alien species. Ecol Lett 18:188–199
Bastian M, Heymann S, Jacomy M et al. (2009) Gephi: an open source software for exploring and manipulating networks. ICWSM 8:361–362
Bozick BA, Real LA (2015) The role of human transportation networks in mediating the genetic structure of seasonal influenza in the United States. PLOS Pathog 11. doi: e1004898
Büttner K, Krieter J, Traulsen A, Traulsen I (2013) Static network analysis of a pork supply chain in Northern Germany—Characterization of the potential spread of infectious diseases via animal movements. Prev Vet Med 110:418–428
Chitwood DJ (2003) Research on plant-parasitic nematode biology conducted by the United States Department of Agriculture-Agricultural Research Service. Pest Manag Scie 596–7:748–753
Ciccolini M, Dahl J, Chase-Topping M, Woolhouse M (2012) Disease transmission on fragmented contact networks: Livestock-associated methicillin-resistant Staphylococcus aureus in the Danish pig-industry. Epidemics 4:171–178
Food and Agriculture Organization of the United Nations Statistics Division (2015) Crops and livestock products. http://faostat3.fao.org/browse/T/TP/E. Accessed 8 April 2016
Hodda M, Davies K (2011) Nematodes in Cropping Systems: Identification and Techniques. The University of Adelaide and Commonwealth Scientific and Industrial Research Organization, Canberra 103
Kao RR, Green DM, Johnson J, Kiss IZ (2007) Disease dynamics over very different time-scales: foot-and-mouth disease and scrapie on the network of livestock movements in the UK. J R Soc Interface 4:907–916
Kiss IZ, Green DM, Kao RR (2006) The network of sheep movements within Great Britain: network properties and their implications for infectious disease spread. J R Soc Interface 3:669–677
Mai WF, Mullin PG et al. (1996) Plant-parasitic nematodes: a pictorial key to genera. Comstock Publishing Associates
McNeill M, Phillips C, Young S et al. (2011) Transportation of nonindigenous species via soil on international aircraft passengers’ footwear. Biol Invasions 13:2799–2815
Miano DW (2014) Maize Lethal Necrosis Disease: A real threat to food security in the eastern and central Africa region. Faculty of Agriculture, University of Nairobi
Natale F, Giovannini A, Savini L et al. (2009) Network analysis of Italian cattle trade patterns and evaluation of risks for potential disease spread. Prev Vet Med 92:341–350
Nelson MF, Bone CE (2015) Effectiveness of dynamic quarantines against pathogen spread in models of the horticultural trade network. Ecol Complex 24:14–28
Neher, D A (2010) Ecology of plant and free-living nematodes in natural and agricultural soil. Annu Rev Phytopathol 48:371–94. doi:10.1146/annurev-phyto- 073009-114439
Newman MEJ (2010) Networks: An Introduction. Oxford University Press, Inc. New York
104
Ortiz-Pelaez A, Pfeiffer D, Soares-Magalhaes R, Guitian F (2006) Use of social network analysis to characterize the pattern of animal movements in the initial phases of the 2001 foot-and-mouth disease (FMD) epidemic in the UK. Prev Vet Med 76:40–55
Page-Weir N, Jamieson L, Bell N et al. (2013) Interception and hot water treatment of mites and nematodes on root crops from the Pacific Islands. NZ Plant Prot 66:17– 28
Paini DR, Yemshanov D (2012) Modelling the Arrival of Invasive Organisms via the International Marine Shipping Network: A Khapra Beetle Study. PLOS ONE 7. doi: e44589
Palisson A, Courcoul A, Durand B (2016) Role of Cattle Movements in Bovine Tuberculosis Spread in France between 2005 and 2014. PLOS ONE 11. doi: e0152578
Pautasso M, Xu X, Jeger MJ et al. (2010) Disease spread in small-size directed trade networks: the role of hierarchical categories. J Appl Ecol 47. doi:1300–1309
Pautasso M, Jeger MJ (2014) Network epidemiology and plant trade networks. AoB Plants 6:1-14
Rasamoelina-Andriamanivo RH, Duboz R, Lancelot R et al. (2014) Description and analysis of the poultry trading network in the Lake Alaotra region, Madagascar: implications for the surveillance and control of Newcastle disease. Acta Trop 135:10–18
Salathé M, Kazandjieva M, Lee JW, et al. (2010) A high-resolution human contact network for infectious disease transmission. P Natl Acad Sci 107:22020–22025
Secretariat of the International Plant Protection Convention (2006) International Standards for Phytosanitary Measures. Food and Agriculture Organization of the United Nations, Rome 105
Standards and Trade Development Facility (2013) International Trade and Invasive Alien Species. World Trade Organization, Geneva
Tangchitsomkid N, Chanmalee T, Hodda M (2015) Ultrasonic extraction of plant- parasitic nematodes from plant roots. Australas Plant Path 44:87–96
Tinsley M, Lewis FI, Brulisauer F (2012) Network modeling of BVD transmission. Vet Res 43:11
Van der Graaff N, Khoury W (2009) Biosecurity in the movement of commodities as a component of global food security. In: Strange RN and Gullino ML (ed.) The Role of Plant Pathology in Food Safety and Food Security, Plant Pathology in the 21st Century 3. Springer, pp 25–39. doi: 10.1007/978-1-4020-8932-9_3
Wasserman S, Faust K (1994) Social network analysis: Methods and applications. Cambridge University Press, London
Zacheo G (1993) Introduction. In: Khan MW (ed.) Nematode interactions. 1st edition. Springer Science and Business Media, Dordrecht, pp 1-25 106
GENERAL DISCUSSION
This thesis generates new insights into the invasion process by providing the first empirical investigation into the role of plant trade networks in the dispersal of plant pests. This is highly significant, considering how important plant trade and agriculture are to the global economy and food security. It provides baseline qualitative and quantitative information and analysis for an important group of cryptic plant pests, nematodes, to better inform biosecurity surveillance strategies for these organisms at the farm, market and entry port level. This thesis also introduces the theory and techniques of network science to invasion science theory and practice, thereby demonstrating the utility of a network perspective and approach to IAS management and providing a new tool for invasion biologists and biosecurity specialists: network analysis. It demonstrates how this tool can help decision-makers predict patterns of invasive species movement and identify strategic, critical points in trade networks at which to target intervention strategies and thus better manage potential invasions.
KEY POINTS
1. The fundamental characteristics of trade and transport networks determining the spread of IAS are: hubs, shortcuts, clustering, centrality, directedness and mixing patterns.
2. Plant produce trade networks have the following characteristics: hub nodes, shortcuts, low clustering, and are directed, weakly-connected and disassortative.
3. Nematodes move through plant produce trade networks in the same structural pattern as goods, meaning the movement patterns of nematodes can be predicted from those of produce.
4. A large range and number of live, FLN and PPN are dispersing locally, nationally and internationally via plant produce trade networks.
5. Nematodes can move anywhere from 1 km to over 1000 km via these networks but survival numbers and diversity decrease with distance. 107
6. Betweenness and degree (particularly in-degree) centrality measures of network nodes and links correspond with nematode and PPN infestation risk.
APPLICATIONS OF FINDINGS
THEORETICAL APPLICATIONS FOR INVASION SCIENCE
A network perspective and approach to invasion science can be very useful in not only visualising the invasion system but also in analysing it in order to predict future invasion patterns as well as reveal past ones. Network models can include many of the different components of invasion systems. The basic elements, nodes and links, can represent anything from natural parks, bodies of water, habitat or agricultural crop patches, connected by habitat corridors, roads, rivers, sea or wind currents. Other components of invasion systems can also be included such as: site susceptibility to invasion, temporal fluctuations and propagule pressure. Network models are versatile and can be applied to a variety of organisms (individual species, as well as to species groups dispersing actively and passively) over various spatial scales (from individual plants to landscape patches to countries).
These models can then be analysed using network analysis, to determine the spread dynamics of different organisms. These include: direction and volume of movement, potential entry points, the most vulnerable areas as well as the areas significantly contributing to spread. They can be used to predict how a high risk pest might spread, should it breach national borders. They can also utilise data from previous invasions to show how isolated outbreaks were connected and the retrospective spread dynamics of the IAS. Past and future invasion scenarios as well as control strategies can then be simulated over the network model.
Invasion biologists are just beginning to apply network principles to invasion systems (e.g. Nelson and Bone 2015; Van Andel et al. 2016), but due to data availability and expertise, these are still necessarily simplistic. It would be beneficial for invasion biologists to work more closely with network scientists in order to fully realise the potential of the network approach to invasion science. 108
PRACTICAL APPLICATIONS FOR GLOBAL BIOSECURITY
Global biosecurity organisations as well as regional and national bodies would benefit from a network perspective on invasion management. Invasive species do not recognise national boundaries and globalisation has made the issue of IAS movement a worldwide problem. National trade networks are not isolated entities but, rather, nested within regional and global networks. Biosecurity organisations would benefit from an overview of these nested networks, identifying the critical points within them, in order to target and scale action appropriately. For example, if an IAS escaped national boundaries, rather than wait for different national plant protection organisations to report incursions to determine the extent of spread, a network model of the system could be used to trace back as well as forward from the site of first detection to predict other infested sites and determine the extent of spread. This strategy has yet to be utilised during a plant biosecurity incursion response, despite simulation models, loosely based on networks, proving valuable in identifying effective tracing strategies (e.g. Potts et al. 2013).
Global biosecurity surveillance also needs to pay greater attention to the general movement trends of organisms (especially cryptic plant pests). Plant protection organisations, at a national level, rely on lists of prioritised, known threats (Brasier 2010). This misses any undiscovered and underestimated IAS. It also relies on timely and transparent reporting of incursions (Brasier 2010). Such that, if a new IAS emerged and there was a delay in reporting it, the whole system would break down and the IAS would have the potential to spread unimpeded. The results of Chapter 4 demonstrate that quarantine inspections are not detecting cryptic groups, such as nematodes, at or within national borders. Biosecurity organisations need to understand how different organisms are moving generally in order to more accurately identify risks. However, cost can be a major issue as is the feasibility of looking for a wide range of organisms rather than just a few. In addition, IAS are moving on a variety of pathways such as nursery, horticultural and agricultural plants, vehicle tyres and shipping containers (McNeill et al. 2006; Roques and Auger-Rozenberg 2006; Paini and Yemshanov 2012). Knowledge of networks and how organisms move on them can reduce the financial investment required and increase efficiency by targeting high cost activities (in terms of time, effort and money) in strategic places. 109
For example, targeting surveillance at betweenness central nodes in the global network could detect multiple pest groups at once, moving over long distances. This would reduce the burden on importing and exporting countries by having a centralised surveillance point making it faster to trace backwards as well as forwards if and when a particular pest of interest was detected moving through the network. These points could be markets for plants or plant products or strategic habitats (sentinel vegetation or crop patches). Regional organisations could have a similar surveillance point, reflecting the most likely node through which a pest would move within the regional cluster. This would in turn reduce the burden on the global surveillance system and provide an early counter check point. The major sink nodes in the network (those with the highest in- degree centrality) may not just be the most vulnerable points in the system but could also prove of sentinel value in alerting authorities to incursions. Again, knowledge of the underlying network can assist in the rapid detection of other incursions.
Global and regional strategic network surveillance would be incredibly valuable for detecting a whole range of potentially invasive organisms moving worldwide, including those producing symptomatic as well as asymptomatic plant symptoms. It could form part of a broader initiative by the FAO to prevent plant health risks rather than merely respond to emergency situations. The costs of such an initiative could be covered by setting up a Risk Reduction Fund, similar to the OIE World Animal Health and Welfare Fund, paid for by contributions from IPPC contracting parties.
STRENGTHS, WEAKNESSES, CHALLENGES & RECOMMENDATIONS
METHODOLOGICAL STRENGTHS AND LIMITATIONS
1. This thesis presents the results of exploratory research, rather than those of controlled experiments. Therefore, the particular strength of the methodology, which uses network snapshots to provide an overview of a type of network (and the movement of organisms on it), is also its main weakness: it is only an overview. Now that this initial groundwork has been accomplished, experiments can be devised to test particular hypotheses regarding movement and to examine these networks in more detail. This is one recommendation for future studies in this area. 110
2. Nematode movement was found to follow the movement of produce through the network. This finding may have been a function of sampling produce for nematodes at particular points in the network, such as major markets, rather than at random points. In order to independently confirm this finding, therefore, nematode samples could be taken at random network points, sequenced using molecular analysis to identify point sources, and then traced back through major markets to determine more precisely that nematodes move through the network in the same pattern as produce. 3. Only one means of dispersal was examined: plant produce. However, nematodes may also disperse in soil on vehicle tyres, footwear or in containers contaminated with soil. Therefore, without this direct link from farms to markets and back to farms, only dispersal could be examined rather than spread of nematodes. However, this has not been empirically demonstrated for any soil- borne pathogens. 4. The nematode extraction methods in this study were focused on extracting live, vermiform nematodes from plant produce. They were not, therefore, efficacious in extracting all forms of PPN, such as adult female cyst nematodes, like Globodera. This level of detail may be obtained by future studies able to utilize extraction methods suitable for all forms of PPN.
TIME CONSTRAINTS
1. Nematodes could not be identified to species level because this would take considerably longer than the allocated time span for a PhD project. Therefore, the higher level of “genus” was employed. A genus was classified as a PPN genus if it contained one or more species that cause damage to plants in low or high numbers, as outlined by Yeates et al. (1993). However, not all species within these PPN genera categories are parasitic to plants. Only specimens with stomato-stylets (piercing stylets with bulbs for sucking) were identified as PPN in order to increase the accuracy of this classification system; however, this still represents an assumption and thus limitation of this study. The specific identity of pest species is, nevertheless, important for biosecurity. This level of detail could be provided in future studies using molecular identification techniques for both PPN and FLN. 111
2. Only a snapshot of the trade network in each country could be taken. In each country there are hundreds of small farms and far more markets than a small team are able to sample. A more extensive sampling strategy involving a larger team would produce a more detailed picture of the network in each country. Alternatively, statistical databases on the farms and markets, including the origins and destinations of produce may exist in the national plant protection organisations and could be used to generate the trade network. Data sharing alliances between scientific and government institutions could, therefore, yield highly beneficial information for both parties. 3. Produce sampled at markets could not be traced back to farms of origin and, therefore, nematode diversity and abundance on farm samples before transport (and thus true nematode survival) could not be ascertained. Market authorities could collect this information from growers selling to market traders (either manually or through the use of barcode and USB trackers) and collate it into databases ready for use by plant protection organisations tracing incursions or scientists developing network-based surveillance and monitoring strategies.
DATA AVAILABILITY CONSTRAINTS
1. Detailed information on PPN outbreaks is not available to verify the connection between trade networks and outbreaks. There may indeed be an indirect connection via the transport network through the movement of vehicles, people and containers transporting nematodes from farms to markets and back to farms. Again, data sharing alliances could rectify this and be the topic of future studies.
FUTURE DIRECTIONS
Future research in this area could take several directions. These include:
1. Generating a network model for emerging infectious disease (EID) or emerging plant pest (EPP) spread via trade and transport networks. This could be based on the Global distribution of the relative risk of a EID event map produced by Jones et al. (2008) or a global risk map for EPP based on the known origins of emerging plant pests, such as Phytophthora ramorum. The trade and transport networks connecting these (infected) areas with vulnerable populations of
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people, animals or plants could be modelled along with flow and other factors in order to give an estimate of the risk of an EPP or EID event and provide information as to where to best place surveillance in hospitals or plant and animal health clinics in these areas. 2. Investigating the movement of other cryptic plant pest groups, such as viruses, bacteria and fungi, via plant trade networks. Different pathways could also be investigated including horticultural or agricultural plant stock, seed or produce. 3. Modelling past invasions, where sufficient data are available, to test the predictive capacity of network models. Empirical data are crucial in predictive and retrospective modelling of invasions to accurately determine spread dynamics as well as critical points in the system and thus better inform plant pest incursion responses.
CONCLUSION
Despite the considerable gains in knowledge over the past five decades, the invasion process still remains challenging to predict. By understanding the human contribution to the movement of potential invasive species via networks, scientists and managers are in a better position to predict, prepare and prevent the spread of invasive species. A network approach has the potential to greatly improve our understanding of the introduction and spread phases of the invasion process as well as improve the efficiency and efficacy of global, regional and national biosecurity systems and, ultimately, contribute to safeguarding the world’s natural and managed ecosystems. 113
REFERENCES
Brasier C (2010) Scientific and operational flaws in international protocols for preventing entry and spread of plant pathogens via “plants for planting.” In: Fifth Commission on Phytosanitary Measures, International Plant Protection Convention, UN FAO, Rome, March 2010.
Jones KE, Patel NG, Levy MA et al. (2008) Global trends in emerging infectious diseases. Nature 451:990–993
Nelson MF, Bone CE (2015) Effectiveness of dynamic quarantines against pathogen spread in models of the horticultural trade network. Ecol Complex 24:14–28
Paini DR, Yemshanov D (2012) Modelling the Arrival of Invasive Organisms via the International Marine Shipping Network: A Khapra Beetle Study. PLOS ONE 7. doi: e44589
Potts JM, Cox MJ, Barkley P et al. (2013) Model-based search strategies for plant diseases: a case study using citrus canker (Xanthomonas citri). Divers Distrib 19:590–602
Roques A, Auger-Rozenberg M-A (2006) Tentative analysis of the interceptions of non-indigenous organisms in Europe during 1995-2004. EPPO Bulletin 36:490–496
Van Andel M, McInnes K, Tana T, French N (2016) Network analysis of wildlife translocations in New Zealand. New Zeal Vet J 64:169–173
Yeates GW, Bongers T, De Goede RG, et al. (1993) Feeding habits in soil nematode families and genera-an outline for soil ecologists. J Nematol 25:315–31 114
APPENDIX A
Table A.1: Centrality measures for nodes in the Australian trade network.
Node Degree Closeness Betweenness Albury Farmers’ Market, New South Wales 1 0 0 Alice Springs, Northern Territory 1 0 0 Argentina 1 1 0 Baldivis, Western Australia 2 2.33 0 Ballan, Victoria 1 0 0 Brisbane Wholesale Market, Queensland 4 1.8 10 Bullsbrook, Western Australia 1 2.3 0 Bundaberg, Queensland 2 1.75 0 Busselton, Western Australia 2 2.18 0 Carnarvon, Western Australia 1 2.85 0 Canning Vale Wholesale Market, Western Australia 27 1.44 181.5 Carabooda, Western Australia 4 1 0 Castlemaine, Victoria 1 0 0 China 2 2.18 0 Cobram, Victoria 7 1.13 0 Cookernup, Western Australia 6 2 14.5 Cowra Farmers’ Market, New South Wales 3 0 0 Cudgen, New South Wales 1 1 0 Daylesford, Victoria 4 1 0 Fairfield, Victoria 1 0 0 Wagga Wagga Farmers’ Market, New South Wales 2 0 0 Fernvale, New South Wales 1 1 0 Footscray Wholesale Market, Melbourne 28 1.5 51 Gatton, Queensland 2 1.8 0 Griffith, New South Wales 2 1 1 Gunning Farmers’ Market, New South Wales 1 0 0 Kalamunda, Western Australia 3 1 0 Karridale, Western Australia 3 1 0 Kyneton, Victoria 1 0 0 Lancefield Farmers’ Market, Victoria 4 0 0 Lawson, New South Wales 1 0 0 Liverpool, New South Wales 1 1 0 Macedon Ranges Farmers’ Market, Victoria 1 0 0 Maldon, Victoria 6 1 0 Mandurah Farmers’ Market, Western Australia 1 0 0 Mansfield Farmers’ Market, Victoria 1 0 0 Margaret River Farmers’ Market, Western Australia 5 0 0 Midland, Perth, Western Australia 1 2.3 0 115
Mudgee, New South Wales 1 0 0 Manjimup, Western Australia 2 2.18 0 Nannup, Western Australia 3 1 0 North Perth 2 2.2 0 Oakford, Western Australia 1 0 0 Orange, New South Wales 6 1 12 Queensland 3 2.07 0 Rapper 2 2.2 0 Riddells Creek, Victoria 1 0 0 Rossmore, New South Wales 1 1 0 Rutherglen, Victoria 1 0 0 South Fremantle Farmers’ Market, Western Australia 6 0 0 Stirling, Western Australia 1 1 0 Subiaco RM, Perth 1 2.3 0 Sydney Wholesale Market, Sydney 28 0 0 Talbot, Victoria 1 0 0 Tasmania 4 2.07 0 Temora, New South Wales 2 1 26 Thorpdale, Victoria 1 1 0 Tucha 1 0 0 Victoria 1 ^ 3 1 0 Seymour, Victoria 5 1 0 Victoria 1 2 0 Western Australia 2 2.3 0 Western Australia 1 ^ 2 1 0 Western Australia 6 ^ 7 1.44 92 Wanneroo, Western Australia 1 2.3 0 Windsor, New South Wales 1 1 0 Wodonga Farmers’ Market, Victoria 2 0 0 Woodend, Victoria 1 0 0 Yackandandah, Victoria 4 1 0 Flemington Farmers’ Market, Victoria 1 0 0 Myalup, Western Australia 1 2.3 0 Mexico 1 2.3 0 Dennington, Victoria 1 2 0 Montgomery, Victoria 1 1.5 0 Purnim, Victoria 1 2 0 South Australia 1 2.5 0 Western Australia Farm 1 ^ 9 1.8 0 Western Australia Farm 2 ^ 15 1.81 0 Japan 2 0 0 Singapore 2 0 0 Malaysia 1 0 0 Saudi Arabia 1 0 0 Coles 1 0 0 116
Woolworths 1 0 0 Hong Kong 1 0 0 Taiwan 1 0 0 The Maldives 1 0 0 Adelaide Wholesale Market, South Australia 2 0 0 Port Kelang, Malaysia 1 0 0 Dammam, Saudi Arabia 1 0 0 Doha, Qatar 1 0 0 Dubai, UAE 1 0 0 Mix Node* 1 3 1 2 Mix Node* 2 4 1 0 Mix Node* 3 4 2.33 0 Dernancourt, South Australia 1 1.5 0 Wemen, Victoria 1 1 0 Tatura, Victoria 1 1 0 Werribee, Victoria 2 1.75 0 Somerville, Victoria 2 1.75 0 Byrneside, Victoria 1 1 0 Holland 1 1 0 Robertson, New South Wales 1 1 0 Devon Meadows, Victoria 1 1 0 Ballandean, Queensland 1 1 0 Mulgowie, Queensland 2 1.75 0 Virginia, South Australia 1 2 0 Katikati, New Zealand 1 2 0 Munno Para Downs, South Australia 1 2 0 Spain 1 2 0 Bairnsdale, Victoria 1 2 0 Clyde, Victoria 1 2 0 Thailand 1 2 0 Lyndhurst, Victoria 1 2 0 Pearcedale, Victoria 1 2 0 Bordertown, South Australia 1 2 0 Sassafras, Tasmania 1 2 0 Mariginiup, Western Australia 1 2.3 0 Augusta, Western Australia 1 0 0 Boyanup, Western Australia 1 0 0 Leederville, Perth 1 0 0 Manning, Perth, Western Australia 2 0 0 Victoria Park, Perth 1 0 0 Mix Node 4* 3 2 3 New South Wales Farm 1 ^ 4 2.09 0 117
*The term mix node refers to an unidentified location where produce from two or more identified locations were mixed before being transported.
^ Nodes with numbers (e.g. New South Wales Farm 1) refer to farm codes where exact locations are not included due to the conditions of Ethics Permit 2013/158 or where exact locations could not be verified (e.g. Victoria 1). 118
Table A.2: Centrality measures for nodes in the Australian nematode movement network.
Node Degree Closeness Betweenness Argentina 1 1 0 Baldivis, Western Australia 1 2.6 0 Brisbane Wholesale Market, Queensland 2 0 0 Bundaberg, Queensland 1 1 0 Busselton, Western Australia 1 1 0 Canning Vale Wholesale Market, Western Australia 11 1.25 41 Carabooda, Western Australia 1 1.5 0 China 1 1 0 Cobram, Victoria 1 1 0 Cookernup, Western Australia 3 1 2 Cowra Farmers’ Market, New South Wales 3 0 0 Cudgen, New South Wales 1 1 0 Daylesford, Victoria 1 1 0 Wagga Wagga Farmers’ Market, New South Wales 2 0 0 Fernvale, New South Wales 1 1 0 Footscray Wholesale Market, Melbourne 7 0 0 Fremantle RM, Perth 1 0 0 Gatton, Queensland mixed with Dernancourt, South 1 1.5 0 Australia Griffith, New South Wales 1 0 0 Gunning Farmers’ Market, New South Wales 1 0 0 Kalamunda, Western Australia 1 1.5 0 Lancefield Farmers’ Market, Victoria 4 0 0 Liverpool, New South Wales 1 1 0 Maldon, Victoria 1 1 0 Mandurah Farmers’ Market, Western Australia 1 0 0 Margaret River Farmers’ Market, Western Australia 3 0 0 Manjimup, Western Australia 1 1 0 North Perth 1 2.6 0 Oakford, Western Australia 1 2.6 0 Orange, New South Wales 1 1 0 Queensland 3 1.625 0 Rapper 1 2.6 0 Rossmore, New South Wales 1 1 0 South Fremantle Farmers’ Market, Western Australia 2 0 0 Stirling, Western Australia 1 1 0 Sydney Wholesale Market, Sydney 15 0 0 Tasmania 1 1 0 Temora, New South Wales 1 1 0 Thorpdale, Victoria 1 1 0 Victoria 1 ^ 1 1 0 Seymour, Victoria 1 1 0 119
Victoria 1 1 0 Western Australia 1 0 0 Western Australia 1 ^ 1 1 0 Western Australia 6 ^ 4 2 8 Wanneroo, Western Australia 1 2 0 Windsor, New South Wales 1 1 0 Wodonga, Farmers’ Market, Victoria 1 0 0 Yackandandah, Victoria 1 1 0 Myalup, Western Australia 1 2 0 Mexico 1 2 0 Dennington, Victoria 1 1 0 Montgomery, Victoria 1 1 0 Purnim, Victoria 1 1 0 Western Australia Farm 1 ^ 9 1.384615385 0 Western Australia Farm 2 ^ 15 1.263157895 0 Japan 2 0 0 Singapore 2 0 0 Malaysia 1 0 0 Saudi Arabia 1 0 0 Coles 1 0 0 Woolworths 1 0 0 Hong Kong 1 0 0 Taiwan 1 0 0 The Maldives 1 0 0 Adelaide Wholesale Market, South Australia 2 0 0 Port Kelang, Malaysia 1 0 0 Dammam, Saudi Arabia 1 0 0 Doha, Qatar 1 0 0 Dubai, UAE 1 0 0 Mix Node* 1 3 1 2 Mix Node* 2 4 1 14 Mix Node* 3 4 2 8 New South Wales Farm 1 ^ 4 1 0
*The term mix node refers to an unidentified location where produce from two or more identified locations were mixed before being transported.
^ Nodes with numbers (e.g. New South Wales Farm 1) refer to farm codes where exact locations are not included due to the conditions of Ethics Permit 2013/158 or where exact locations could not be verified (e.g. Victoria 1). 120
Table A.3: Centrality measures for nodes in the Australian plant-parasitic nematode movement network.
Node Degree Closeness Betweenness Baldivis, Western Australia 1 1.5 0 Brisbane Wholesale Market, Queensland 2 0 0 Bundaberg, Queensland 1 1 0 Canning Vale Wholesale Market, Western Australia 6 1 4 China 1 1 0 Cobram, Victoria 1 1 0 Cookernup, Western Australia 1 1 0 Cowra Farmers’ Market, New South Wales 1 0 0 Daylesford, Victoria 1 1 0 Wagga Wagga Farmers’ Market, New South Wales 2 0 0 Fernvale, New South Wales 1 1 0 Footscray Wholesale Market, Melbourne 8 0 0 Gatton, Queensland mixed with Dernancourt, South Australia 1 1.5 0 Lancefield Farmers’ Market, Victoria 3 0 0 Maldon, Victoria 1 1 0 Margaret River Farmers’ Market, Western Australia 2 0 0 Manjimup, Western Australia 1 1 0 Oakford, Western Australia 1 1.5 0 Orange, New South Wales 1 1 0 Queensland 2 1.333333333 0 Rossmore, New South Wales 1 1 0 South Fremantle Farmers’ Market, Western Australia 1 0 0 Sydney Wholesale Market, Sydney 11 0 0 Tasmania 1 1 0 Temora, New South Wales 1 1 0 Thorpdale, Victoria 1 1 0 Victoria 1 ^ 1 1 0 Victoria 1 1 0 Western Australia 1 ^ 1 1 0 Western Australia 6 ^ 4 1 2 Windsor, New South Wales 1 1 0 Wodonga Farmers’ Market, Victoria 1 0 0 Yackandandah, Victoria 1 1 0 Mexico 1 1.5 0 Dennington, Victoria 1 1 0 Western Australia Farm 1 ^ 9 1.1 0 Western Australia Farm 2 ^ 14 1.066666667 0 Japan 2 0 0 Singapore 2 0 0 Malaysia 1 0 0 Saudi Arabia 1 0 0 121
Coles 1 0 0 Woolworths 1 0 0 Hong Kong 1 0 0 Taiwan 1 0 0 The Maldives 1 0 0 Adelaide Wholesale Market, South Australia 2 0 0 Port Kelang, Malaysia 1 0 0 Dammam, Saudi Arabia 1 0 0 Doha, Qatar 1 0 0 Dubai, UAE 1 0 0 Mix Node* 1 3 1 2 Dernancourt, South Australia 1 1.5 0 New South Wales Farm 1 ^ 4 1 0
*The term mix node refers to an unidentified location where produce from two or more identified locations were mixed before being transported.
^ Nodes with numbers (e.g. New South Wales Farm 1) refer to farm codes where exact locations are not included due to the conditions of Ethics Permit 2013/158 or where exact locations could not be verified (e.g. Victoria 1). 122
Table A.4: Centrality measures for nodes in the Thailand trade network.
Node Degree Closeness Betweenness Bangkhen Market, Bangkok 4 1 5 Simum Muang market, Bangkok 3 1.333333333 6 Talad Thai, Bangkok 29 1.714285714 241 Bangkok (general) 8 1.6875 27.5 Pak Khlong Market, Bangkok 1 0 0 Sapanmai Market, Bangkok 1 1.5 0 Nakhon Pathom 2 2.5 0 Nakhon Chai Si district, Nakhon Pathom 2 1 0 Pathom Mongkong Market, Nakhon Pathom 9 0 0 Nonthaburi 5 2.277777778 0 Bang Yai Markets, Nonthaburi 5 0 0 Tedsaban Neung Market, Kohn Kaen 8 0 0 Srimeungthong Market, Kohn Kaen 21 1.357142857 206 Balampoor Market, Kohn Kaen 1 1 0 Mueung district Kohn Kaen 3 2 0 Kohn Kaen 6 2.142857143 64 Loburi 1 1 0 Loei 1 2.266666667 0 Maesod district, Tak 1 1 0 Chiang Mai 4 1.733333333 0 Chiang Rai 2 2.8 0 Ang Thong 2 2 0 Sisakhet 3 1.866666667 0 Kanchanaburi 2 2.5 0 Lamphun 1 2.6 0 Cron Tuey Market 1 1.5 0 Su Ra Market, Nakhon Ratchasima 12 0 0 Kharm district, Nakhon Ratchasima 1 2.6 0 Nakhon Ratchasima 3 1.866666667 0 Nakhon Sawan 1 2.6 0 Phetchaburi 1 2.6 0 Pathum Thani 1 2.6 0 Phetchabun 3 1.875 0 Ratchaburi 2 1 0 Saraburi 1 1 0 Suphanburi 1 2.6 0 Ubon Ratchathani 3 3 3 Sri Cha Rean Market, Ubon Ratchathani 22 2.071428571 169 Yasothon province 2 0 0 Cambodia 2 1 0 Lao PDR 2 2 0 Vietnam 1 2.6 0 Myanmar 1 1 0 123
Malaysia 1 1 0 China 5 1.8125 0 India 4 1.875 0 Bangladesh 2 2.933333333 0 Samrong District, Ubon Ratchathani 1 2.588235294 0 Chom Bueng District, Ratchaburi 1 2.6 0 Pak Chong District, Nakhon Ratchasima 2 2.533333333 0 Po Tharam District, Ratchaburi 4 2 4 European Union 1 0 0 Mae Rim, Chiang Mai 1 2.6 0 Don Han subdistrict, Kohn Kaen 1 2.266666667 0 Banhad subdistrict, Kohn Kaen 1 3 0 Mix Node* 1 4 1 0 Phitsanulok 1 2.933333333 0 Sakon Nakhon 1 0 0 Udon Thani 2 0 0 Maha Sarakham 3 1 13.5 Kalasin 2 0 0 Nong Bua Lamphu 2 0 0 Dao Heuang Market, Champasak, Lao PDR 1 2.933333333 0 Mae Hong Son 1 2.266666667 0 Dan Khun Thot district, Nakhon Ratchasima 1 1 0 Nong Bun Mak district, Nakhon Ratchasima 1 1 0
*The term mix node refers to an unidentified location where produce from two or more identified locations were mixed before being transported. 124
Table A.5: Centrality measures for nodes in the Thailand nematode movement network.
Node Degree Closeness Betweenness Bangkhen Market, Bangkok 3 1 4 Simum Muang market, Bangkok 3 1.333333333 6 Talad Thai, Bangkok 17 1.333333333 43 Bangkok (general) 7 1.571428571 20 Pak Khlong Market, Bangkok 1 0 0 Sapanmai Market, Bangkok 1 1.5 0 Nakhon Pathom 1 2 0 Nakhon Chai Si district, Nakhon Pathom 2 1 0 Pathom Mongkong Market, Nakhon Pathom 8 0 0 Nonthaburi 4 2.222222222 0 Bang Yai Markets, Nonthaburi 5 0 0 Tedsaban Neung Market, Kohn Kaen 7 0 0 Srimeungthong Market, Kohn Kaen 5 0 0 Balampoor Market Kohn Kaen 1 1 0 Mueung district Kohn Kaen 2 2.222222222 0 Kohn Kaen 4 1 3 Chiang Mai 2 1.8 0 Chiang Rai 1 1 0 Sisakhet 3 1.666666667 0 Kanchanaburi 1 1 0 Lamphun 1 2 0 Su Ra Market, Nakhon Ratchasima 6 0 0 Kharm district, Nakhon Ratchasima 1 2 0 Nakhon Ratchasima 1 2 0 Pathum Thani 1 2 0 Phetchabun 2 1.8 0 Ratchaburi 2 1 0 Saraburi 1 1 0 Sri Cha Rean Market, Ubon Ratchathani 4 0 0 Cambodia 1 1.5 0 Myanmar 1 1 0 Malaysia 1 1 0 China 4 1.2 0 India 2 1.5 0 Samrong District, Ubon Ratchathani 1 2.375 0 Chom Bueng District, Ratchaburi 1 2.6 0 Pak Chong District, Nakhon Ratchasima 2 1.5 0 Po Tharam District, Ratchaburi 4 2 4 European Union 1 0 0 Mae Rim, Chiang Mai 1 2 0 Don Han subdistrict, Kohn Kaen 1 1 0 125
Banhad subdistrict, Kohn Kaen 1 1.666666667 0 Mix Node* 1 5 1 16 Maha Sarakham 1 1 0 Dan Khun Thot district, Nakhon Ratchasima 1 1 0 Nong Bun Mak district, Nakhon Ratchasima 1 1 0
*The term mix node refers to an unidentified location where produce from two or more identified locations were mixed before being transported. 126
Table A.6: Centrality measures for nodes in the Thailand plant-parasitic nematode movement network.
Node Degree Closeness Betweenness Bangkhen Market, Bangkok 3 1 2 Simum Muang market, Bangkok 1 1.5 0 Talad Thai, Bangkok 9 0 0 Bangkok (general) 3 0 0 Sapanmai Market, Bangkok 1 1.5 0 Nakhon Pathom 1 1 0 Pathom Mongkong Market, Nakhon Pathom 1 0 0 Nonthaburi 4 1 0 Tedsaban Neung Market, Kohn Kaen 1 0 0 Srimeungthong Market, Kohn Kaen 1 0 0 Mueung district Kohn Kaen 2 1 0 Kohn Kaen 2 0 0 Chiang Mai 1 1 0 Sisakhet 1 1 0 Lamphun 1 1 0 Kharm district, Nakhon Ratchasima 1 1 0 Nakhon Ratchasima 1 1 0 Ratchaburi 1 1 0 Sri Cha Rean Market, Ubon Ratchathani 3 0 0 Samrong District, Ubon Ratchathani 1 1 0 Chom Bueng District, Ratchaburi 1 1 0 Pak Chong District, Nakhon Ratchasima 1 1 0 Po Tharam District, Ratchaburi 1 0 0 European Union 1 0 0 Mae Rim, Chiang Mai 1 1 0 Don Han subdistrict, Kohn Kaen 1 1 0 Banhad subdistrict, Kohn Kaen 1 1 0 127
Table A.7: Centrality measures for nodes in the Lao PDR trade network.
Node Degree Closeness Betweenness Another Vang Vieng market, Vang Vieng 1 1.5 0 Champasak 3 1 0.333333333 China 1 1 0 Dao Heuang Market, Pakse, Champasak 9 0 0 Early Market, Vientiane Capital 22 0 0 Hadxayfong District, Vientiane Capital 3 1 0 Hanoi, Vietnam 3 1 0 Ho Chi Min, China 1 1 0 Japan 1 0 0 Kasy district, Vientiane 1 1.5 0 Kaysone Phomvihane district, Savannakhet 5 1 0 Mad district, Vientiane province 1 1.5 0 Mak Ming, Vietnam 1 1 0 Mayparkngum district, Vientiane Capital 1 1 0 Morning Market, Vang Vieng, Vientiane 11 1 7 Morning Market, Vientiane Capital (Khnadin) 2 1 0 Mueng Thong Market, Udon Thani, Thailand 1 1 0 Naxaithong district, Vientiane 1 1 0 Nongtaeng district, Vientiane Capital 1 1 0 Organic Market, Vientiane Capital 10 0 0 Pakse, Champasak 4 1 1.333333333 Paksong district, Champasak 11 1.333333333 0 Phonthong district, Champasak 1 1 0 Salakham Market, Vientiane Capital 6 0 0 Salavan 1 0 0 Savannakhet 1 0 0 Savanxai Market, Savannakhet 12 0 0 Sekong 1 0 0 Si Hi Market, Vientiane 2 1 0 Sikhottabong district, Vientiane Capital 1 1 0 Songkhone district, Savannakhet 1 0 0 Thailand 6 1.166666667 4 Thoulakhom district, Vientiane 2 1 0 Vang Tao Quarantine Station, (Lao-Thai border) 3 1.666666667 1 Vang Vieng district, Vientiane 3 1.25 0 Vientiane 3 1.25 0 Vientiane Capital 6 1.166666667 2.333333333 Vietnam 5 1 0 Xaysetha district, Vientiane Capital 2 1 0 Xaythany District, Vientiane Capital 2 1 0 Bangkok, Thailand 1 0 0 Lao PDR 3 1 0 Ngeun district, Vientiane Capital 1 1 0 128
Khantaboury district, Savannakhet 1 1 0 Bon Khon, Vientiane Capital Province 1 1 0 Don Hon, Vientiane Capital Province 1 1 0 Early Market, Savanxai, Savannakhet 1 1 0 Xayphouthong District, Savannakhet 1 1 0 Processing company in Savannakhet 1 0 0 Organic Market, Savannakhet 1 0 0 Mix Node* 1 4 1 0 Mix Node* 2 3 1 0 Mix Node* 3 3 1 0
*The term mix node refers to an unidentified location where produce from two or more identified locations were mixed before being transported. 129
Table A.8: Centrality measures for nodes in the Lao PDR nematode movement network.
Node Degree Closeness Betweenness Champasak 2 1 0.5 Dao Heuang Market, Pakse, Champasak 6 0 0 Early Market, Vientiane Capital 7 0 0 Hadxayfong District, Vientiane Capital 1 1 0 Hanoi, Vietnam (mixed with Chinese and Thai produce) 1 1.5 0 Kaysone Phomvihane district, Savannakhet 2 1 0 Morning Market, Vang Vieng, Vientiane 4 1 3 Morning Market, Vientiane Capital (Khnadin) 2 1 0 Organic Market, Vientiane Capital 3 0 0 Pakse, Champasak 2 1 1 Paksong district, Champasak 9 1.428571429 0 Phonthong district, Champasak 1 1 0 Salakham Market, Vientiane Capital 2 0 0 Salavan 1 0 0 Savannakhet 2 1 1 Savanxai Market, Savannakhet 5 0 0 Sekong 1 0 0 Si Hi Market, Vientiane 1 1.5 0 Vang Vieng district, Vientiane 1 1.5 0 Vientiane 2 1 0 Vientiane Capital 4 1.4 2.5 Vietnam 3 1 0 Xaythany District, Vientiane Capital 1 1 0 Bangkok, Thailand 1 0 0 Lao 1 1.5 0 Ban Yangso (Khantaboury district, Savannakhet or China) 1 1 0 Bon Khon, Vientiane Capital Province 1 1 0 Don Hon, Vientiane Capital Province 1 1 0 Early Market, Savanxai, Savannakhet 1 1 0 Xayphouthong District, Savannakhet 1 1 0 Processing company in Savannakhet 1 0 0 Organic Market, Savannakhet 1 0 0 Mix Node* 1 4 1 1 Mix Node* 2 3 1 2 Mix Node* 3 3 1 1
*The term mix node refers to an unidentified location where produce from two or more identified locations were mixed before being transported. 130
Table A.9: Centrality measures for nodes in the Lao PDR plant-parasitic nematode movement network.
Node Degree Closeness Betweenness Champasak 3 1 1.5 Dao Heuang Market, Pakse, Champasak 5 0 0 Early Market, Vientiane Capital 4 0 0 Hadxayfong District, Vientiane Capital 1 1 0 Kaysone Phomvihane district, Savannakhet 2 1 0 Morning Market, Vang Vieng, Vientiane 3 0 0 Morning Market, Vientiane Capital (Khnadin) 2 1 0 Organic Market, Vientiane Capital 4 0 0 Pakse, Champasak 1 0 0 Paksong district, Champasak 8 1.333333333 0 Phonthong district, Champasak 1 1 0 Salakham Market, Vientiane Capital 1 0 0 Salavan 1 0 0 Savannakhet 2 1 1 Savanxai Market, Savannakhet 3 0 0 Sekong 1 0 0 Si Hi Market, Vientiane 1 1 0 Vang Vieng district, Vientiane 1 1 0 Vientiane 2 1 0 Vientiane Capital 3 1 1.5 Vietnam 1 1 0 Xaythany District, Vientiane Capital 1 1 0 Bangkok, Thailand 1 0 0 Ban Yangso (Khantaboury district, Savannakhet or China) 1 1 0 Xayphouthong District, Savannakhet 1 1 0 Processing company in Savannakhet 1 0 0 Organic Market, Savannakhet 1 0 0 APPENDIX B 131
Ecology Letters, (2015) 18: 188–199 doi: 10.1111/ele.12397 REVIEW AND SYNTHESIS The role of global trade and transport network topology in the human-mediated dispersal of alien species
Abstract Natalie Clare Banks,1,2,3 Dean More people and goods are moving further and more frequently via many different trade and Ronald Paini,1,3* Kirsty Louise transport networks under current trends of globalisation. These networks can play a major role in Bayliss,2,3 and Michael Hodda,1,3 the unintended introduction of exotic species to new locations. With the continuing rise in global trade, more research attention is being focused on the role of networks in the spread of invasive species. This represents an emerging field of research in invasion science and the substantial knowledge being generated within other disciplines can provide ecologists with new tools with which to study invasions. For the first time, we synthesise studies from several perspectives, approaches and disciplines to derive the fundamental characteristics of network topology deter- mining the likelihood of spread of organisms via trade and transport networks. These characteris- tics can be used to identify critical points of vulnerability within these networks and enable the development of more effective strategies to prevent invasions.
Keywords Human-mediated spread, infectious diseases, invasive alien species, trade, transport networks.
Ecology Letters (2015) 18: 188–199
Understanding the characteristics of networks that affect INTRODUCTION the likelihood of organisms moving can assist in devising Current world trends, such as globalisation, have dramatically strategies for preventing incursions of IAS and thus prevent- increased the volume, frequency and range of movement of ing or reducing the impact of these organisms on natural and people and goods in the last few decades (Wilson 1995; Hulme managed ecosystems. 2009). These people and goods do not disperse by random or We present a synthesis of the role of human trade and diffusion processes, but rather via trade and transport net- transport network topology in the passive, unintended spread works, which operate at many levels, from local, through of organisms. Over the last 20 years, network science has national and regional levels, to global networks. These net- amassed a substantial body of knowledge on the characteris- works have been identified as key pathways for the unintended tics and nature of the spread of matter (e.g. people, patho- entry and spread of invasive alien species (IAS) (Perrings et al. gens, ships, ideas, etc.) through networks. The structural 2005, Hulme 2009), representing two of the three principal characteristics of real-world networks such as the World Air- mechanisms for the introduction of organisms to new locations port Network (WAN; Guimera & Amaral 2004; Guimera (Hulme et al. 2008). Trade and transport networks can be air, et al. 2005), Global Cargo Shipping Network (GCSN; Kaluza road, rail or shipping routes, and the organisms transported et al. 2010) and the World Trade Network (WTN; De Bene- can range from weed plants, to large animals (e.g. mammals, dictis & Tajoli 2011) have been studied and defined. Network reptiles, amphibians and fish), to small animals (e.g. arthro- theory and modelling have been successfully utilised in the pods, nematodes) and microbes (e.g. fungi, bacteria and field of epidemiology to understand and predict invasions by viruses) (Pimentel et al. 2001; Hulme et al. 2008). pathogens spreading through real-world trade, transport and Not all the organisms introduced to a new area become contact networks (e.g. Severe Acute Respiratory Syndrome invasive, and predicting which species could become invasive (SARS), Meyers et al. 2005; Foot and Mouth Disease is the subject of considerable research (e.g. Paini et al. 2010; (FMD), Ortiz-Pelaez et al. 2006; and avian influenza, Van Venette et al. 2010). However, the species that do become Kerkhove et al. 2009). More recently, network theory has invasive can cause significant damage. In the United States been applied to the movement of IAS by several authors (e.g. alone, the estimate of the direct economic cost from IAS is Keller et al. 2011; Kolzsch€ & Blasius 2011; Moslonka-Lefeb- almost US$120 billion per year (Pimentel et al. 2005). Glob- vre et al. 2012; Paini & Yemshanov 2012). These individual ally the figure is much greater, and it rises further when losses studies have shown that network science can provide insights in biodiversity, ecosystem services and amenities are included and immensely useful tools for ecologists studying invasions, (Pimentel et al. 2001). yet the unintended movement of organisms through trade and
1CSIRO Biosecurity Flagship, Dutton Park, 4102, Australia 3Plant Biosecurity Cooperative Research Centre, Bruce, 2617, Australia 2School of Veterinary and Life Sciences, Murdoch University, Murdoch, 6150, *Correspondence: E-mail: [email protected] Australia
© 2014 John Wiley & Sons Ltd/CNRS Review and Synthesis The spread of invasive species via networks 189 transport networks has not been extensively studied in a sys- while differences occur in the directionality of links and in the tematic way. However, when the many independent, unrelated mixing patterns and clustering of nodes. All these characteris- studies across several disciplines are assembled and synthes- tics are important to the spread of IAS, so all need to be con- ised, it is possible to identify some of the fundamental attri- sidered in applying network science to invasions. butes of network topology that influence the spread of IAS. This paper synthesises the current research in this area, dis- Scale-free network properties cusses the implications of this research for management of IAS, and suggests some future research directions. The ulti- Scale-free networks are heterogeneous, where most nodes mate aim of the paper therefore is to provide ecologists with (ports, cities or countries) have few edges (links) and a few an entry point into this new field, summarise and synthesise nodes have many edges; this ‘fat tailed’ or ‘right skewed’ dis- key results and insights from the full range of disciplines util- tribution in degree (number of edges), follows a power law ising network theory, and suggest how these insights may when logarithmically transformed, (Barabasi & Albert 1999) apply to invasion science. (Fig. 1; see Fig. 2 for illustrated network terminology). The paper is presented in three sections. The first section Invasive alien species may spread easily and quickly in briefly describes and summarises the features of network scale-free networks via highly connected ‘hub’ nodes, which topology most relevant to the spread of IAS and outlines how contain a large number of connections (Jeger et al. 2007). these contributions from network science and epidemiology Invasive alien species may not need to be highly infectious or can provide insights into IAS spread on networks. The second well adapted for transport to be spread via scale-free networks section suggests how the insights from network science can due to the high number of links from hub nodes to other sus- provide new approaches to managing IAS spread in relation ceptible nodes (Caton et al. 2006; Bigras-Poulin et al. 2007; to the entire invasion system. The third section discusses some Jeger et al. 2007). Severe acute respiratory syndrome (SARS), of the challenges in the application of network science to bio- for example is less infectious than influenza (WHO 2003), yet logical invasions, and how they may be addressed. it spread to 37 countries via scale-free global transport net- works within a matter of weeks, infecting approximately 10,000 individuals (Smith 2006). NETWORK TOPOLOGY World trade and transport networks – such as the WAN, Small-world network properties GCSN and the WTN – share some characteristics that affect the movement of people and goods as well as pests and patho- Small-world networks are homogeneous, with each node gens. Similarities include scale-free and small-world properties, having approximately the same number of edges (Fig. 1).
Figure 1 The degree distribution in a small-world network, illustrated by the Australian road network (left), follows a Poisson curve while the degree distribution in the Australian airline network, a scale-free network, follows a power law distribution (right).
© 2014 John Wiley & Sons Ltd/CNRS 190 N. C. Banks et al. Review and Synthesis
most of the nodes within the network linked together via multi- ple edges (e.g. Morris & Kretzschmar 1995; Kao et al. 2006; Aznar et al. 2011; Rautureau et al. 2011). These networks are very densely connected, which means that an infestation in any single node of the GSCC can lead to a widespread invasion in most of the network (Meyers et al. 2006). Real-world trade networks can also be made up of hierar- chical clusters of nodes, where a central ‘core’ node is linked to nodes in ‘lower’ levels of the hierarchy via the nodes in each successive level such that the network is structured like the trunk, branches and leaves of a tree (Newman 2010; Paut- asso et al. 2010; Rasamoelina-Andriamanivo et al. 2011). Stratification of networks into hierarchical clusters based on the characteristics of nodes (such as movement and behavio- ural patterns or identity) has been found to have important implications for epidemics in networks (Doherty et al. 2005; Ortiz-Pelaez et al. 2006; Keeling et al. 2010). For instance, when hierarchical clusters of farms, markets and traders cor- respond to high-risk animal movement in trade networks, the Figure 2 Illustrated network terminology (left) and real-world example of likelihood of disease transmission for the nodes within these a trade network (right). Groups of nodes with symbols ranging in colour particular clusters can be higher than for others (Bigras-Pou- from black to light grey represent hierarchical clusters within the network. lin et al. 2006; Ortiz-Pelaez et al. 2006). Key ‘spreaders’ in the initial outbreak of Foot and Mouth Disease in the United Kingdom (farms, markets and dealers) were identified in part They follow a Poisson distribution curve where the distribu- by identifying hierarchical clusters of nodes with links to each tion in the number of edges per node peaks at an average other as well as links to nodes in other clusters (Ortiz-Pelaez value and then decreases exponentially (Wang & Chen et al. 2006). In a hierarchical, scale-free network, once an 2003). Networks with small-world properties have shortcuts, organism infects or invades a hub, it can rapidly spread to where any node in the network can be reached from any other parts of the network through an invasion cascade from other node in a few steps. A small-world network has a well-connected hub nodes at the national (or global) level to small average path length (mean distance between any two nodes in regional clusters with few links in the network (Kiss nodes in a network) as well as a high clustering coefficient et al. 2006). (the degree to which nodes are connected together in clus- Many real-world trade and transport networks possess ters) (Watts & Strogatz 1998). In practical terms, this means small-world AND scale-free characteristics; that is, a small that most ports, cities and countries in such a network can average path length, a high clustering coefficient and a power be reached from any other port, city or country in a short law degree distribution (Boccaletti et al. 2006). This may be number of trade or transport connections. Furthermore, related to the way in which trade and transport networks nodes are grouped in densely connected clusters with rela- grow (Krapivsky & Redner 2001). New links are made in a tively fewer links between the clusters than within them. network when a new node connects to an existing one or These properties have implications for the spread of IAS as when previously unconnected existing nodes link together. with more shortcuts an IAS does not have to be well These connections can be made in various ways: by preferen- adapted for transport to spread through the network (Jeger tial attachment to a well-connected node in the wider net- et al. 2007) and can potentially spread to any receptive node work; or by preferential attachment to nearby nodes (Li et al. in the network via these shortcuts. 2012; Barth elemy 2011). This phenomenon is observed in the Increased clustering in networks can produce largely sepa- WTN in relation to regional cooperative organisations (net- rated communities (sometimes referred to as components). As work communities), such as the EU, ASEAN and NAFTA. A opposed to network shortcuts, the presence of network com- country aiming to expand its international trade may choose munities is believed to slow down the spread of pests and to become a member of one of these organisations and prefer- pathogens through the network due to their confinement entially link to one or several of the well-connected countries within these highly connected clusters (Moslonka-Lefebvre within these trade clusters (Li et al. 2012). However, trade et al. 2009), such as during the initial stages of the AIDS epi- barriers may reduce the impetus for this form of attachment demic in the United States (Szendroi & Csanyi 2004). Thus, and a country may choose instead to create local trade links clustering may produce faster invasions within communities, with countries which are geographically close to it (Li et al. even if there is a relatively low transmission between nodes, 2012). Preferential attachment produces power law distribu- but slower invasions over the whole network. tions of degrees, which is a scale-free network characteristic However, a high level of clustering in a network can produce (Newman 2010), while random or local preferential attach- one giant component, which can be either strongly or weakly ment produces a small average distance and a high clustering connected. Many real-world trade and human contact networks coefficient, which are small-world characteristics (Barth elemy have a giant strongly connected component (GSCC) containing 2011; Li et al. 2012).
© 2014 John Wiley & Sons Ltd/CNRS Review and Synthesis The spread of invasive species via networks 191
The different forms of attachment are generated from deci- by research on avian influenza outbreaks in a real-world trade sions made at the local level, but are influenced by drivers network (Martin et al. 2011). Potentially invasive pests or dis- operating at national, international and global levels (Barth el- eases originating at or near a well-connected source node will emy 2011; De Benedictis & Tajoli 2011). These underlying have a greater potential for spread throughout a network than mechanisms are critical for determining the structure of net- species originating near less well-connected sources. works (Newman 2010; Barth elemy 2011; Li et al. 2012) and In undirected networks – where node connections run in hence the flow of pests and diseases through them. both directions – spread of IAS is less predictable than in While trade and transport networks, such as the WAN, directed networks, particularly in the early stages of an inva- GCSN and the WTN, are similar in many of the general sion (Jeger et al. 2007). The symmetrical transportation links structural characteristics outlined above, they differ in other to and from airports and cities in the WAN and Global Road characteristics which are important to the spread of IAS, such Network (GRN) (Barrat et al. 2004; Barth elemy 2011) are as the directionality of links and in the patterns in which undirected networks in which IAS can potentially move in nodes link together. both directions. The poultry trading network in Madagascar is another real-world example of an undirected network, one in which exotic viruses, such as Newcastle disease and avian Directed and undirected networks influenza, have been able to spread (Rasamoelina-Andriama- In directed networks, such as the WTN and GCSN, goods nivo et al. 2011). flow from source nodes (places of production, such as farms) in one direction to final destination nodes (such as individual Mixing patterns households) often through other nodes (such as packing and distribution houses, wholesale markets and stores). The prob- Mixing patterns (measured by the assortativity coefficient and ability of invasions occurring across directed (and semi-direc- neighbour connectivity in network theory) refer to the ten- ted) networks is influenced by the number of links going in dency of nodes to link with other nodes in particular ways. specific directions, into and out of nodes (Meyers et al. 2006). Two main types of mixing are relevant to spread in real-world The more outward links from an initial node (source), the networks: assortative mixing and disassortative mixing greater the potential spread (Kiss et al. 2006) and the greater (Danon et al. 2010; Newman 2010). the potential final size of the invaded area (Pautasso et al. In networks with assortative mixing, nodes with similar 2010). The greater the number of inward and outward links, numbers of edges preferentially link to each other, such that the easier an organism will be able to spread throughout a those with numerous edges (highly connected nodes) link network and the less important will be specific adaptations for together and those with few edges (less well-connected nodes) long-distance travel (Pautasso & Jeger 2008). This is because preferentially link together. World transport networks, such in-degrees represent the number of nodes able to invade an as the GCSN and the WAN, are assortative networks: well- individual node, and out-degrees represent the number of connected ports and airports tend to connect with other well- nodes able to be invaded from that one node (Meyers 2007). connected port and airport nodes (Barrat et al. 2004; Ko€lzsch The more outgoing links an invaded node has, the greater its & Blasius 2011). This kind of mixing within networks can cre- spread potential. The number of outward links from invaded ate components that are well-connected internally but that are lakes, for example has been correlated with the subsequent relatively disconnected from each other at the network level invasion of previously un-invaded lakes by Bythotrephes (Doherty et al. 2005). longimanus, the spiny waterflea (Muirhead & MacIsaac 2005). In disassortive mixing, nodes with numerous edges link to The horticultural trade network is another real-world example nodes with few edges. This kind of mixing can create more of a directed network where the movement of traded plants links between different parts of the network and thus larger has been associated with the spread of the plant pathogens components (Morris & Kretzschmar 1995). Trade networks, Phytophthora ramorum and P. kernoviae (Harwood et al. such as the WTN, tend to be dissassortative networks, where 2009), although how its structure may increase the probability trade ties more commonly exist between well-connected coun- of spread has not been investigated in detail. Notwithstanding tries and poorly connected ones (Kiss et al. 2006; Fagiolo this example, among real-world networks, plant trade has et al. 2008; De Benedictis & Tajoli 2011). been relatively little studied from a network viewpoint. Con- The type of mixing pattern affects the spread of an invasion sidering the potential role of plant trade networks in facilitat- in a network. Assortative mixing can enable less infectious or ing invasions, this is a significant knowledge gap (Pautasso & well-adapted organisms to initially spread faster via the net- Jeger 2014). work (Gupta et al. 1989; Kiss et al. 2008). By contrast, disas- The number of outward links from an original source node sortative mixing can produce larger scale invasions on is also important in the spread of IAS in theoretical model- networks (Gupta et al. 1989; Morris & Kretzschmar 1995) and ling, and empirical studies of directed networks. In modelling the maximum extent of an invasion is reached faster (Kiss et al. of hierarchical, directed networks the probability of spread 2008). However, initial spread is slower in disassortative net- beyond the initial node and final extent of an invasion are works as invasive agents move from higher degree nodes (such both positively correlated with the number of outgoing links as well-connected markets) to lower degree nodes (such as from the source node, regardless of higher level structural farms with few connections) (Kiss et al. 2006; Kiss et al. 2008). topology and cohesion (Pautasso et al. 2010). The key role When the two types of mixing both occur in a network, the played by source nodes in spread via networks is supported degree of disassortative mixing may be most important
© 2014 John Wiley & Sons Ltd/CNRS 192 N. C. Banks et al. Review and Synthesis because it determines the extent of spread throughout the lar- network, acting as a channel or funnel. It is a measure of the ger network and connects any isolated clusters created by importance of a particular node in connecting other nodes in assortative mixing (Newman 2003). Even in networks with lar- a network: nodes with greater betweenness centrality spread gely assortative mixing, a small amount of disassortive mixing IAS faster and with higher probability. Nodes with high greatly increases the risk of infestation or infection for nodes betweenness centrality have been identified as ‘key players’ in in the rest of the network. This has been found in real-world the spread of viruses in real-world networks (Ortiz-Pelaez sexual contact and animal trade networks (Doherty et al. et al. 2006; Rasamoelina-Andriamanivo et al. 2011). A market 2005, Dukpa et al. 2011, Adams et al. 2012). with the highest betweenness of all the nodes in an animal Directedness and assortativity differ markedly in different movement network was pivotal in the transmitting Foot and real-world networks. The GCSN is directed and assortative Mouth Disease in the United Kingdom during the 2001 epi- (Ko€lzsch & Blasius 2011); the WTN is directed and disassorta- demic (Ortiz-Pelaez et al. 2006). This may also be the case for tive (De Benedictis & Tajoli 2011); the WAN is undirected plant pests. For example, the Netherlands is the most impor- and assortative (Barrat et al. 2004); and the GRN is also tant importer and distributer of ornamental plants in the undirected but its mixing patterns are undetermined because world (European Commission 2006; Dehnen-Schmutz et al. the whole network is yet to be characterised. Urban streets, 2010). Because it is on the paths between most producers and representing most of the network, appear to be disassortative importers, it could act as a gateway to Europe and the rest of (De Montis et al. 2005; Porta et al. 2006), while highways rep- the world for the introduction of pests such as the western resenting a smaller portion of the network appear to be assor- flower thrips (Kirk & Terry 2003). tative (Mukherjee 2012; Mohmand & Wang 2013). Thus, the Betweenness centrality can refer to edges as well as nodes. network as a whole may be disassortative. Edges with high betweenness centrality act as bridges between otherwise disconnected components of a network. In terms of invasions, bridges enable IAS to jump geographic or social bar- Connectance riers (long-distance jumps) to establish new populations (Jolly Network connectance (measured by k-core in social network & Wylie 2002). For example, in a human contact network one theory) refers to how well-connected the nodes are in the net- link between two people in Manitoba, Canada connected the work, and influences the incidence and prevalence patterns of population in the capital, Winnipeg, with the rural population IAS (Ghani et al. 1997; Doherty et al. 2005; Martin et al. in Manitoba, and formed a bridge for disease transmission 2011). Network connectance is the fraction of realised links between the two populations (Jolly & Wylie 2002). over the total number of possible links. The trend to globali- Closeness centrality is slightly different to the previous two sation is increasing connectance in networks such as the centrality measures in that a node’s importance is determined WTN, as each new trade connection (edge) between new trad- by the short average distance from that node to all other ing partners (nodes) increases the density of linkages in the nodes in the network, rather than the number of connections overall network (Foti et al. 2013). This improves the mobility it has (degree centrality), or how it acts as a channel to other of goods between any two nodes in the system but also means nodes (betweenness centrality). Nodes with a shorter total dis- IAS can move around more easily, via multiple links (Jolly & tance (closeness) to all other nodes may be more important in Wylie 2002; Bigras-Poulin et al. 2007). spreading diseases, because pathogens can move through these Centrality is another aspect of network connectance that nodes to the other nodes in a network in only a few steps influences the potential spread of organisms. Centrality mea- (Natale et al. 2009). sures the importance of a particular node to connectivity In simulations of the spread of cattle diseases through the within a network, and has been shown to be influential in IAS Italian cattle network, a short number of steps between an ini- spread in human and animal contact networks through three tial contaminated farm (source node) and all the other nodes different measures: degree centrality, betweenness centrality in the network strongly correlated with the final size of epi- and closeness centrality. demics (Natale et al. 2009). Other modelling studies based on Degree centrality determines the importance of a node to real-world networks have found that the distance between connectivity within a network by the large number of edges nodes in highly connected cores of networks can be the most the node has, linking it to other nodes in the network (Pir- influential determinant of the extent of spread (Kitsak et al. aveenan et al. 2013). Degree centrality determines the likeli- 2010). hood of a node becoming invaded and spreading the invader This section has summarised theoretical modelling and through the network. The degree centrality of the source node empirical studies of the topology of networks in the areas of of an invasion or epidemic may have a significant influence on human and animal health, aquatic invasion biology and, more the spread of the pest or pathogen (Pautasso et al. 2010). recently, plant pathology. Many different aspects of networks Degree centrality may also be important in the general spread have been investigated, not just those mentioned above. How- of IAS over the network, as well-connected areas are more ever, only some features of networks have been found impor- likely to be invaded than less well-connected areas. Nodes tant to the spread of IAS. These include small-world and with high degree centrality have been correlated with the scale-free properties, directedness of links, mixing patterns occurrence of exotic viral outbreaks in animal trade networks and aspects of network connectance. These characteristics (Rasamoelina-Andriamanivo et al. 2011). may help pinpoint those nodes within a network (e.g. lakes, Betweenness centrality refers to the extent to which a partic- ports or national parks) that are the most vulnerable to IAS ular node lies on the shortest paths between other nodes in a introductions and those nodes and edges that are likely to be
© 2014 John Wiley & Sons Ltd/CNRS Review and Synthesis The spread of invasive species via networks 193 the most influential in the dispersal of IAS. Some suggestions global or regional levels in world-wide trade and transport for IAS prevention and management are presented below. networks, rather than within individual states or countries, limited resources can be focused on the specific areas where they will be most effective, thus saving time and resources. A SYSTEMIC APPROACH TO PREVENTION AND Globalisation has increased the interconnectedness of world MANAGEMENT nations, thus making problems of IAS potentially global Currently, IAS management strategies are applied at the state, rather than local or national. A systemic approach therefore, national and, occasionally, international level (WTO 2013). recognising the whole global system, has particular relevance. These strategies typically target particular species or vectors. However, it may require changes to management of IAS such With increased globalisation, national and regional networks as: increased commitment to international cooperation and are becoming increasingly interconnected and incorporated collaboration in IAS management; coordination of different into global networks. Invasive alien species, vectors, networks levels of trade from global through regional to local levels; and the relationships between them represent parts of a sys- implementation of quarantine measures at levels other than tem for the spread of IAS around the world. Therefore, we the national level; consideration of IAS in decisions on mak- suggest that the prevention and management of IAS spread ing trade and transport links; and allocation of resources to within this globalised system requires a systemic perspective the countries with limited resources to deal with global IAS and a systemic approach, rather than one focussed on particu- problems because they represent the ‘weakest links’ in the net- lar species or vectors. The characteristics of the network, not work (Perrings et al. 2002). A systemic approach and perspec- just those of the pest, vector or location, are important. tive recognises that everyone is part of global, interconnected The key components of the system and their interrelation- networks and the IAS problems of one part influence the IAS ships contribute to the likelihood of transportation of IAS to problems of many other parts of the network. new environments (see Fig. 3 for conceptual diagram). These A network approach can also improve the accuracy of components may also represent the critical points in the sys- Import Risk Analyses (IRA’s). By incorporating the bigger tem at which to apply intervention efforts to prevent inva- picture of global and regional networks and analysing all sions. A systemic approach involves nested intervention pathways – not just particular species or pathways – IRA’s strategies targeting, specifically, those points within the system can better capture the full range of risks from IAS and that generate the highest likelihood of IAS movement. For become more reliable tools to prevent unintended introduc- example, focussing on bridges (edges with high betweenness tions. centrality), the specific vectors that link regional clusters There are other, more specific insights for the management (network communities) together via those bridges, and finally, of IAS which come from recognising the importance of net- targeting the means and sites of species attachment for these work topology in the human-mediated dispersal of alien spe- vectors (how IAS enter the network). By targeting prevention cies through global trade and transport networks. The and management measures at strategic, critical points at the insights from network science can apply to management at all stages of invasion, from before invasions (prevention and pre- paredness) to during invasions (decision-making for issues such as eradication or areas of monitoring and allocation of resources) to after invasions (implications and ongoing man- agement). To demonstrate how these can enhance invasion science, some implications are discussed briefly below. One important insight is, because species that are less infec- tious or well adapted for transport may spread in scale-free and small-world networks, more species may have the poten- tial to become IAS. Their characteristics may be different to those spreading by natural means and those organisms that have been invasive in the past. Another insight is that, because hubs have particular poten- tial to spread IAS, monitoring them may be a very effective way of detecting IAS, particularly in hierarchical networks. Targeting hubs can also be an effective strategy for contain- ment of the extent of invasions. These insights can also apply to hubs between different networks, for example transhipment or transfer points (hubs connecting the GCSN and GRN). Hubs may be increasing in their potential to spread IAS due to the increased mixing of produce from multiple sources and origins at these nodes (Barham & Sylvander 2011). Figure 3 Likelihood of invasive alien species (IAS) transportation is a Because high degrees of clustering may slow the spread of function of three factors: characteristics of species, behaviour of vectors and the topology of the network. Their influence on the likelihood of invasions and lead to their natural confinement within clus- movement (represented by the surface) is driven by the varying ability of ters, knowing the level of clustering in network communities these variables to transport IAS. may be an important part of preparedness for, and manage-
© 2014 John Wiley & Sons Ltd/CNRS 194 N. C. Banks et al. Review and Synthesis ment during, invasions. In addition to providing insight into Centrality measures may be useful in minimising costs of the time scale for dealing with an incursion most efficiently, IAS through targeting ports, cities or countries with the knowing the extent of clusters can help greatly in defining the greatest potential to cause large-scale invasions or epidemics. area for monitoring, eradication and management: the focus For example, source nodes with high degree centrality can be can be limited to the cluster and does not have to extend to targeted and their key connections broken in order to con- the entire network. By contrast, because invasions can spread tain invasions (Natale et al. 2009). Additionally, knowing the very rapidly and extensively in a network with a GSCC, then number of steps between an initial source node or farm and speed of response is more important. all the other nodes in the network is essential information As the potential for spread is great via the outward links for management, as sources that are closer to all other nodes from a source node in directed networks, invasions and inva- in the network (closeness central nodes) may be key to deter- sive species originating at or near a well-connected source mining the speed and final size of an invasion as well as the node have higher potential to spread. This may mean that likely costs and benefits of different responses. Traceability more resources are justified for dealing with invasions from is, therefore, vital in the prevention and management of such nodes than those from less well-connected sources. It invasions. may also mean that species with relatively low invasion poten- tial may be threats if they are near a well-connected node in a ISSUES directed network than if they are elsewhere, or part of a net- work with different characteristics. A systemic approach to IAS has many potential benefits, but The spread of IAS is less predictable in undirected net- it also requires a deep understanding of the invasion system, works, especially in the early stages of an invasion, so more how it operates and the relationships among components of uncertainty must be allowed for in responses, possibly involv- the system. All of these are complex, dynamic, heterogenous, ing greater margins of error. More strategic action may be highly sensitive to variation and are constrained by space. required for invasions in such networks. These factors present several issues which need to be consid- Mixing patterns can have different effects on the movement ered when applying network science to the spread of IAS. of IAS through networks and there can be several responses when using this information. In networks with assortative Heterogeneous, interacting components mixing, organisms can spread initially very quickly via the network but in the early stage of an invasion the spread of Invasion systems contain invasive species, vectors, habitats these organisms is likely to be isolated within a network clus- and communities of organisms as well as environmental vari- ter. The responses to IAS may, therefore, need to be more ables, all of which act and interact within various spatial and rapid in networks with assortative mixing, but these responses temporal scales. These components also have heterogeneous may only need to target smaller areas if the IAS is detected characteristics which can influence spread. For instance, habi- before it becomes widespread. The clustering associated with tats can vary in their susceptibility to invasion (Garc ıa-Roble- assortative mixing means that expansions of range may occur do & Murcia 2005), countries can vary in their phytosanitary in quite sudden jumps into new clusters rather than gradual policies and procedures (FAO 2000), environmental factors expansion. This sort of range expansion means that data on can favour or prevent establishment (Holway et al. 2002) and the spread of invasive organisms must be interpreted rather vector classes can have heterogeneous links to different habi- differently. For example, an apparent long stasis in range tats (Kinloch et al. 2003; Seebens et al. 2013). In addition to expansion may precede another rapid increase in range, rather these aspects, invasion systems also contain networks of than indicate that the maximum extent of an invasion has roads, air and shipping routes, connecting different environ- been reached. ments together in different ways, which can passively trans- As disassortative mixing is associated with slower initial port IAS to new localities. The role of species and vector rates of spread, rapid action may be less important than for characteristics in the transport component of invasions has networks with assortative mixing. Range expansion may be at been well documented (e.g. Ruiz & Carlton 2003; Leung et al. a steady rate, which can be used to inform monitoring and 2012) but the role of network topology on the spread of inva- management strategies better than in assortative networks. sive organisms and how it interacts with other components of This may be important because IAS may move farther via a invasion systems has only recently gained attention in inva- network with disassortative mixing, meaning that response sion science (e.g. Pautasso & Jeger 2014). For example, a spe- actions for an IAS will need to cover a wider area. cies’ characteristics may determine whether it survives in The increasing connectance of many networks means that sufficient numbers during transport to reach a new habitat, IAS can move around more easily and via multiple links. This while vector mobility patterns may determine when and has two implications. Firstly, identifying and targeting the where an organism is moved in the landscape, and network weakest links in biosecurity systems and whole networks are topology determines how far the potential IAS can spread required for effective management. Secondly, coordinated through the network. The different components, their hetero- action at several or many nodes may be required to manage geneity and interactions may increase or decrease the likeli- an invasion, and that the costs of management, therefore, hood of IAS movement. All the relevant components of may be high. However, high connectance also means that the invasion systems need to be included in network models in implications of an IAS entering a network may be substantial order to increase their utility and predictability for invasion and widespread, so that high costs may be justified. networks.
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ment data, presumably due to the large amount of data Temporal dynamics involved. This is effectively looking at a dynamic network as Temporal dynamics are important to include in consideration a snapshot in time, and it cannot reveal whether the network of networks because networks change over time. Over large remains constant over a longer time scale. Unfortunately, col- time scales, real-world systems develop and evolve (Holme & lecting temporal data can be difficult, time consuming and Saramaki€ 2012). Connectance and the number of hub nodes expensive. increase, clusters form and average path lengths change There are several strategies to combat this problem. For a (Barth elemy 2011; De Benedictis & Tajoli 2011). Over smaller general picture of how real-world networks operate and time scales, new links are created while old ones are lost their temporal dynamics, snapshots of several networks (Cocks et al. 2009), there are changes in volumes and flows could be taken and network sizes and factors influencing (Mart ınez-Lopez et al. 2009; Tatem 2009), and there are direc- movement compared. However, if a specific network is tional changes in the links according to the season (Aznar under examination, it can be monitored periodically (Bata et al. 2011; Cocks et al. 2009). et al. 2005) once other characteristics of the system have The changes in networks have implications for IAS move- been established. These are just a few examples of how net- ment. Increased connectance over time produces potentially work dynamics, a major gap in network analysis, could be larger and more rapid invasions: previously localised pests, addressed. such as western flower thrips and Colorado Potato Beetle, have quickly become cosmopolitan in a more connected net- Spatial issues work (Kirk & Terry 2003; Grapputo et al. 2005). The evolu- tion of nodes into hubs increases both their vulnerability and Real-world systems are constrained by space, unlike mathe- potential to spread IAS, together with increasing the probabil- matical representations. These spatial constraints can shape ity and speed of dispersal in the entire network. The growth some features of small-world and scale-free networks (Barth el- of regional clusters as networks evolve also means that IAS emy 2011). In the real world, moving longer distances costs may be more easily and rapidly spread but also that they may more, and this spatial constraint on human vectors can create be more easily isolated within geographical regions. As more clustering in a network (Barth elemy 2011). Habitats close to trade (or transport) links are made, the average distance each other link preferentially together and thus spread IAS between nodes in the network decreases, but as the network within these communities more readily than over longer dis- increases in size, so does the average distance between nodes tances. This same local preferential linking tends to produce (De Benedictis & Tajoli 2011). short average path lengths in spatially constrained networks Trade flow dynamics represent fluctuations in flow and vol- as well as more regional hubs and fewer global hubs (Barrat ume in time and space. They are particularly important in et al. 2005; Barth elemy 2011). The spatial constraints and affecting the spread, establishment and prevalence of IAS. costs associated with new links that apply in real-world trade Fluctuations in flow through a network can produce periods and transport networks can create a maximum in the number of high and low IAS spread through the system. The flow of of links a port, city, airport or country is able to have livestock trade, for example is affected by market forces (sup- (Barth elemy 2011). ply and demand) and these forces can change the flow of Other features of real-world networks can also be shaped trade during the course of a year as well as over larger time by space. For example, spatial constraints can influence the scales (Cocks et al. 2009). Additionally, periods of high and centrality of nodes in networks such as the WAN, where air- low demand during the year often correspond to different fes- ports with the greatest number of shortest paths passing tival times such as Christmas and Lent (Bigras-Poulin et al. through them to other airports (high betweenness), are not 2007; Leslie 2010). The peaks in commodity movement at those with the highest number of connections (high degree). these times can represent critical points in time when the like- They are instead the airports that are the most geo-politically lihood of spread is far higher than usual (Mart ınez-Lopez central in the network (Guimera & Amaral 2004; Barth elemy et al. 2009). 2011). Fluctuations in the volume of goods moving can also affect Spatial scales also influence how IAS may move through the introduction of IAS into new geographic areas. Changes networks. Different networks and movement patterns are in traffic volume, such as those modelled over the WAN over important at different scales. Long-range human travel is large time scales, may change the number of propagules arriv- dominated by airlines, which have been shown to spread ing in new areas and thus the probability of successful intro- viruses to new geographical areas, thus facilitating IAS spread duction of IAS into those new localities (Tatem 2009). on an international scale (Meyers et al. 2005; Balcan et al. Similarly, simulations of the spread of invasive marine species 2009). At a regional or country level, however, commuter over a regional water transport network found that locations transport networks have a greater effect, spreading viruses to receiving higher volumes of traffic were 75% more likely to different subpopulations within small areas (Balcan et al. become infected by an invasive marine species than quieter 2009). A similar phenomenon has been found for marine IAS ports (Floerl et al. 2009). They were also more likely to accel- introductions. Commercial ships have been implicated in the erate the spread of the IAS to other locations (Floerl et al. primary, long-distance spread of a now cosmopolitan tunicate, 2009). Botryllus schlosseri, whereas recreational boats are implicated Although networks change over time, most empirical studies in secondary, localised spread (Lacoursi ere-Roussel et al. on real-world networks examine no more than 1 year’s move- 2012).
© 2014 John Wiley & Sons Ltd/CNRS 196 N. C. Banks et al. Review and Synthesis
The movement patterns of the vectors of IAS may only be CONCLUSION revealed by data at particular spatial scales. For malaria, the high spatial resolution of mobility data on a regional scale This synthesis outlines some of the main factors implicated can pinpoint those human settlements expected to receive or in the passive, unintentional spread of exotic species via transmit more parasites than others in surrounding regions human trade and transport networks and these fundamental (Wesolowski et al. 2012). At a larger scale, however, move- factors cut across taxa as well as disciplinary and sector ment data are more general and have lower spatial resolution. boundaries. Network science has contributed a large body of Thus, some network features influencing spread may not be knowledge on spread via networks and provides new and visible at the larger scale. useful tools for ecologists to study invasion systems. Utilising Larger scale movement data (such as the WAN and GCSN) these new insights and tools in a systemic approach to the may be more valuable in visualising the bigger picture of glo- prevention of IAS movement can help decision-makers in bal trends. Trade networks are ultimately open systems, form- managing threats to national and regional biosecurity and, ing part of larger, increasingly globalised and increasingly ultimately, in safeguarding the world’s natural and managed integrated networks. Considering large-scale movement data ecosystems. may put regional networks into the context of these larger, more-encompassing networks and systems. ACKNOWLEDGEMENTS The authors acknowledge the support of the Australian Gov- Integration ernment’s Cooperative Research Centres Program, Murdoch Component interactions, heterogeneity, temporal and spatial University and the Commonwealth Scientific and Industrial characteristics of real systems are all issues, which need to be Research Organisation. We thank Tom Harwood and, espe- addressed in network modelling because, as discussed above, cially, Bruce Halliday for their comments on the manuscript. all are characteristics of networks and are important for This manuscript was significantly improved by suggestions understanding invasions. Some of these characteristics have from two anonymous reviewers. been incorporated into network models using constructs such as differential node status, weighted and directed edges AUTHORSHIP (Newman 2010), degree-block variables (Colizza & Vespignani 2007, 2008), time-scale separation (Keeling & Rohani 2002) All authors conceived of the study, NB wrote the first draft of and most recently with the design and use of dynamic net- the manuscript and all authors made substantial contributions work models (Ferrari et al. 2013). However, some features to revisions. Each author agrees to submission of the manu- have yet to be incorporated and a single model incorporating script and has no conflict of interest. all features remains to be developed. Thus, the utility of net- work models is limited by the number of these characteristics REFERENCES that can be incorporated. Despite their limitations, network models have been useful in Adams, L.B., Gray, D.G. & Murray, G. (2012). Animal biosecurity in the understanding and predicting the behaviour of some systems. Mekong: future directions for research and development. In: ACIAR – SARS, FMD and avian influenza are examples of diseases Proceedings,pp18 28. Aznar, M., Stevenson, M.A., Zarich, L. & Leon, E.A. (2011). Analysis whose spread on some real-world trade and transport networks of cattle movements in Argentina, 2005. Prev. Vet. Med., 98, 119– have been successfully modelled. In these cases, the network 127. approach still captures more of the important details of real- Balcan, D., Colizza, V., Goncßalves, B., Hu, H., Ramasco, J.J. & world networks than other approaches, such as gravity models, Vespignani, A. (2009). Multiscale mobility networks and the spatial which only capture trends (Kaluza et al. 2010; De Benedictis & spreading of infectious diseases. P. Natl. Acad. Sci. USA, 106, 21484– Tajoli 2011). Network models can, therefore, make more real- 21489. Barabasi, A.-L. & Albert, R. (1999). Emergence of scaling in random istic predictions of spread dynamics and are thus are more networks. Science, 286, 509–512. informative for international policy decisions and management Barham, E. & Sylvander, B. (eds.) (2011). Labels of Origin for Food: of IAS (Kaluza et al. 2010; Ferrari et al. 2013). Local Development, Global Recognition. CABI, Wallingford, UK. An interdisciplinary collaboration between the fields of net- Barrat, A., Barthelemy, M., Pastor-Satorras, R. & Vespignani, A. (2004). work science and invasion biology is necessary to further The architecture of complex weighted networks. P. Natl. Acad. Sci. develop an approach to understanding, modelling and manag- USA, 101, 3747–3752. ing IAS that includes the whole invasion system. The network Barrat, A., Barthelemy, M. & Vespignani, A. (2005). The effects of spatial constraints on the evolution of weighted complex networks. J. approach can help invasion biologists visualise, analyse and Statistical Mech.-Theor. Exper. P050035. potentially predict the introduction and spread of IAS, key Barth elemy, M. (2011). Spatial networks. Phys. Rep., 499, 1–101. steps in the invasion process. Highly complex information can Bata, M.O., Dradri, S., Chapasuka, E., Rodrigues, C.L., Mabota, A. & be visualised as network maps and the structure of the net- Samikwa, D. (2005). A report on a joint rapid assessment of informal work can be analysed. Conversely, invasion science can make cross border trade on the Mozambique-Malawi Border regions a significant contribution to the development of network the- conducted between 27 June–1 July 2005. Maputo, Sistema De ß~ ory by identifying the ecological variables important in real Informacao De Mercados Agricolas De Mozambique (SIMA). Bigras-Poulin, M., Thompson, R., Chri el, M., Mortensen, S. & Greiner, systems, and thus guiding discovery of the universal principles M. (2006). Network analysis of Danish cattle industry trade patterns as governing network structure.
© 2014 John Wiley & Sons Ltd/CNRS Review and Synthesis The spread of invasive species via networks 197
an evaluation of risk potential for disease spread. Prev. Vet. Med., 76, Ghani, A.C., Swinton, J. & Garnett, G.P. (1997). The role of sexual 11–39. partnership networks in the epidemiology of gonorrhea. Sex. Transm. Bigras-Poulin, M., Barfod, K., Mortensen, S. & Greiner, M. (2007). Dis., 24, 45–56. Relationship of trade patterns of the Danish swine industry animal Grapputo, A., Boman, S., Lindstroem, L., Lyytinen, A. & Mappes, J. movements network to potential disease spread. Prev. Vet. Med., 80, (2005). The voyage of an invasive species across continents: genetic 143–165. diversity of North American and European Colorado potato beetle Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. & Hwang, D.U. populations. Mol. Ecol., 14, 4207–4219. (2006). Complex networks: structure and dynamics. Phys. Rep., 424, Guimera, R. & Amaral, L.A.N. (2004). Modeling the world-wide airport 175–308. network. Eur. Phys. J. B, 38, 381–385. Caton, B.P., Dobbs, T.T. & Brodel, C.F. (2006). Arrivals of hitchhiking Guimera, R., Mossa, S., Turtschi, A. & Amaral, L.A.N. (2005). The insect pests on international cargo aircraft at Miami International worldwide air transportation network: anomalous centrality, community Airport. Biol. Invasions, 8, 765–785. structure, and cities’ global roles. P. Natl. Acad. Sci. USA, 102, 7794–7799. Cocks, P., Abila, R., Bouchot, A., Benigno, C., Morzaria, S., Inthavong, P. Gupta, S., Anderson, R.M. & May, R.M. (1989). Networks of sexual et al. (2009) FAO ADB and OIE SEAFMD Study on Cross-Border contacts: implications for the pattern of spread of HIV. Aids, 3, 807– Movement in the Market Chains of Large Ruminants and Pigs in the 817. Greater Mekong Sub-Region. Available at: http://ulm.animalhealth Harwood, T.D., Xu, X., Pautasso, M., Jeger, M.J. & Shaw, M.W. (2009). research.asia/newsletters/FAO_ADB_OIE_Cross-Border%20movement Epidemiological risk assessment using linked network and grid based %20study_Final%20Report.pdf. Last accessed 6 March 2013. modelling: Phytophthora ramorum and Phytophthora kernoviae in the Colizza, V. & Vespignani, A. (2007). Invasion threshold in heterogeneous UK. Ecol. Model., 220, 3353–3361. metapopulation networks. Phys. Rev. Lett., 99, 148701. Holme, P. & Saramaki,€ J. (2012). Temporal networks. Phys. Rep., 519, Colizza, V. & Vespignani, A. (2008). Epidemic modeling in 97–125. metapopulation systems with heterogeneous coupling pattern: theory Holway, D.A., Lach, L., Suarez, A.V., Tsutsui, N.D. & Case, T.J. (2002). and simulations. J. Theor. Biol., 251, 450–467. The causes and consequences of ant invasions. Annu. Rev. Ecol. Evol. Danon, L., Ford, A.P., House, T., Jewell, C.P., Keeling, M.J., Roberts, Syst., 33, 181–233. G.O. et al. (2010). Networks and the epidemiology of infectious Hulme, P.E. (2009). Trade, transport and trouble: managing invasive disease. Interdiscip. Perspect. Infect. Dis.,1,1–52. species pathways in an era of globalization. J. Appl. Ecol., 46, 10–18. De Benedictis, L. & Tajoli, L. (2011). The world trade network. World Hulme, P., Bacher, S., Kenis, M., Klotz, S., Kuhn,€ I., Minchin, D. et al. Econ., 34, 1417–1454. (2008). Grasping at the routes of biological invasions: a framework for De Montis, A., Barthe´ le´ my, M., Campagna, M., Chessa, A. & integrating pathways into policy. J. Appl. Ecol., 45, 403–414. Vespignani, V. (2005). Topological and dynamic properties emerging in Jeger, M.J., Pautasso, M., Holdenrieder, O. & Shaw, M.W. (2007). a real inter-municipal commuting network: perspectives for policy- Modelling disease spread and control in networks: implications for making and planning. Proceedings of the Congress of the European plant sciences. New Phytol., 174, 279–297. Regional Science Association 2005. Available at : http://www.ekf.vsb. Jolly, A. & Wylie, J. (2002). Gonorrhoea and chlamydia core groups and cz/export/sites/ekf/projekty/cs/weby/esf-0116/databaze-prispevku/clanky_ sexual networks in Manitoba. Sex. Transm. Infect., 78, 145–151. ERSA_2005/607.pdf. Last accessed 20 November 2014. Kaluza, P., Kӧlzsch, A., Gastner, M.T. & Blasius, B. (2010). The complex Dehnen-Schmutz, K., Holdenrieder, O., Jeger, M.J. & Pautasso, M. network of global cargo ship movements. J. R. Soc. Interface, 7, 1093– (2010). Structural change in the international horticultural industry: 1103. some implications for plant health. Sci Hortic-Amsterdam, 125, 1–15. Kao, R., Danon, L., Green, D. & Kiss, I. (2006). Demographic structure Doherty, I.A., Padian, N.S., Marlow, C. & Aral, S.O. (2005). and pathogen dynamics on the network of livestock movements in Determinants and consequences of sexual networks as they affect the Great Britain. Proc. R. Soc. B, 273, 1999–2007. spread of sexually transmitted infections. J. Infect. Dis., 191, S42–S54. Keeling, M.J. & Rohani, P. (2002). Estimating spatial coupling in Dukpa, K., Robertson, I.D., Edwards, J.R. & Ellis, T.M. (2011). A epidemiological systems: a mechanistic approach. Ecol. Lett.,5,20–29. retrospective study on the epidemiology of foot-and-mouth disease in Keeling, M.J., Danon, L., Vernon, M.C. & House, T.A. (2010). Bhutan. Trop. Anim. Health Pro., 43, 495–502. Individual identity and movement networks for disease European Commission (2006). Working document of The Commission metapopulations. P. Natl. Acad. Sci. USA, 107, 8866. staff on the situation of the flowers and ornamental plants sector. Keller, R.P., Drake, J.M., Drew, M.B. & Lodge, D.M. (2011). Linking Available at: http://www.coag.org/rep_ficheros_web/a52d1e6b51191d5e9 environmental conditions and ship movements to estimate invasive 1200925d642c108.pdf. Last accessed 26 September 2014. species transport across the global shipping network. Divers. Distrib., Fagiolo, G., Reyes, J. & Schiavo, S. (2008). On the topological properties 17, 93–102. of the world trade web: a weighted network analysis. Phys. A, 387, Kinloch, M., Summerson, R. & Curran, D. (2003). Domestic Vessel 3868–3873. Movements and the Spread of Marine Pests: Risk and Management Ferrari, J.R., Preisser, E.L. & Fitzpatrick, M.C. (2013). Modeling the Approaches. Department of Agriculture, Fisheries and Forestry, Bureau spread of invasive species using dynamic network models. Biol. of Rural Sciences, National Oceans Office, Canberra. Invasions, 16, 949–960. Kirk, W.D. & Terry, L.I. (2003). The spread of the western flower Floerl, O., Inglis, G., Dey, K. & Smith, A. (2009). The importance of thrips Frankliniella occidentalis (Pergande). Agr. Forest Entomol.,5, transport hubs in stepping-stone invasions. J. Appl. Ecol., 46, 37–45. 301–310. Food and Agriculture Organisation of the United Nations (2000) Kiss, I.Z., Green, D.M. & Kao, R.R. (2006). The network of sheep Multilateral trade negotiations on agriculture: a resource manual. movements within Great Britain: network properties and their Available at: http://www.fao.org/docrep/003/x7354e/X7354e00. implications for infectious disease spread. J. R. Soc. Interface, 3, 669– htm#TopOfPage. Last accessed 26 August 2014. 677. Foti, N.J., Pauls, S. & Rockmore, D.N. (2013). Stability of the World Kiss, I.Z., Green, D.M. & Kao, R.R. (2008). The effect of network Trade Web over time – an extinction analysis. J. Econ. Dyn. Control, mixing patterns on epidemic dynamics and the efficacy of disease 37, 1889–1910. contact tracing. J. R. Soc. Interface, 5, 791–799. Garc ıa-Robledo, C.A. & Murcia, C. (2005). Comparative habitat Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, susceptibility to invasion by Chinese ash (Fraxinus chinensis: Oleaceae) H.E. et al. (2010). Identification of influential spreaders in complex in a tropical Andean landscape. Biol. Invasions, 7, 405–415. networks. Lett. Nat. Phys., 6, 888–893.
© 2014 John Wiley & Sons Ltd/CNRS 198 N. C. Banks et al. Review and Synthesis
Ko€lzsch, A. & Blasius, B. (2011). Indications of marine bioinvasion from Pautasso, M. & Jeger, M.J. (2008). Epidemic threshold and network network theory. Eur. Phys. J. B, 84, 601–612. structure: the interplay of probability of transmission and of persistence Krapivsky, P.L.P. & Redner, L.S. (2001). Organization of growing in small-size directed networks. Ecol. Complex.,5,1–8. random networks. Phys. Rev. E, 63, 066123. Pautasso, M. & Jeger, M.J. (2014). Network epidemiology and plant Lacoursi ere-Roussel, A., Bock, D., Cristescu, M., Guichard, F., Girard, trade networks. AoB Plants,6,1–14. P., Legendre, P. et al. (2012). Disentangling invasion processes in a Pautasso, M., Xu, X., Jeger, M.J., Harwood, T.D., Moslonka-Lefebvre, M. dynamic shipping-boating network. Mol. Ecol., 21, 4227–4241. & Pellis, L. (2010). Disease spread in small-size directed trade networks: Leslie, E. (2010). Formal pig movements across Eastern Indonesia – Risk the role of hierarchical categories. J. Appl. Ecol., 47, 1300–1309. for Classical Swine Fever transmission. Society of Risk Analysis Perrings, C., Williamson, M., Barbier, E.B., Delfino, D., Dalmazzone, S., Conference 28–29th September 2010. University of Sydney, Sydney. Shogren, J. et al. (2002). Biological invasion risks and the public good: Leung, B., Roura-Pascual, N., Bacher, S., Heikkila,€ J., Brotons, L., an economic perspective. Conserv. Ecol., 6, 1. Available at: http://www. Burgman, M.A. et al. (2012). TEASIng apart alien species risk consecol.org/vol6/iss1/art1/. Last accessed 26 August 2014. assessments: a framework for best practices. Ecol. Lett., 15, 1475–1493. Perrings, C., Dehnen-Schmutz, K., Touza, J. & Williamson, M. (2005). Li, C., Liu, S., Fang, Z. & Chen, Y. (2012). Emergence of structural How to manage biological invasions under globalization. Trends Ecol. properties in economic networks: a multi-local-world evolving model. Evol., 20, 212–215. Simul. Model. Pract. Th., 21, 91–102. Pimentel, D., McNair, S., Janecka, J., Wightman, J., Simmonds, C., Martin, V., Zhou, X., Marshall, E., Jia, B., Fusheng, G., FrancoDixon, O’Connell, C. et al. (2001). Economic and environmental threats of M.A. et al. (2011). Risk-based surveillance for avian influenza control alien plant, animal, and microbe invasions. Agr. Ecosyst. Environ., 84, along poultry market chains in South China: the value of social 1–20. network analysis. Prev. Vet. Med., 102, 196–205. Pimentel, D., Zuniga, R. & Morrison, D. (2005). Update on the Mart ınez-Lopez, B., Perez, A. & Sanchez-Vizca ıno, J. (2009). Combined environmental and economic costs associated with alien-invasive species application of social network and cluster detection analyses for in the United States. Ecol. Econ., 52, 273–288. temporal-spatial characterization of animal movements in Salamanca. Piraveenan, M., Prokopenko, M. & Hossain, L. (2013). Percolation Spain. Prev. Vet. Med., 91, 29–38. centrality: quantifying graph-theoretic impact of nodes during Meyers, L.A. (2007). Contact network epidemiology: bond percolation percolation in networks. PLoS ONE, 8, e53095. applied to infectious disease prediction and control. B. Am. Math. Soc., Porta, S., Crucitti, P. & Latora, V. (2006). The network analysis of urban 44, 63–86. streets: a primal approach. Environ. Plann. B, 33, 705–725. Meyers, L.A., Pourbohloul, B., Newman, M.E.J., Skowronski, D.M. & Rasamoelina-Andriamanivo, R.H., Duboz, R., Lancelot, R., Maminiaina, Brunham, R.C. (2005). Network theory and SARS: predicting outbreak O.F., Jourdan, M., Rakotondramaro, T.M.C. et al. (2011). Analysis of diversity. J. Theor. Biol., 232, 71–81. poultry trading network and its implication for surveillance of avian Meyers, L.A., Newman, M., Pourbohloul, B. et al. (2006). Predicting influenza and Newcastle disease, Alaotra Lake Madagascar. In: Society epidemics on directed contact networks. J. Theor. Biol., 240, 400– for Veterinary Epidemiology and Preventive Medicine (eds Fourichon, C. 418. & Pfeiffer, D.U. and the SVEPM Executive Committee). Leipzig, Mohmand, Y.T. & Wang, A. (2013). Weighted complex network analysis Germany, pp. 193–204. of Pakistan highways. Discrete Dyn. Nat. Soc., 2013, 1–5. Rautureau, S., Dufour, B. & Durand, B. (2011). Vulnerability of animal Morris, M. & Kretzschmar, M. (1995). Concurrent partnerships and trade networks to the spread of infectious diseases: a methodological transmission dynamics in networks. Soc. Networks, 17, 299–318. approach applied to evaluation and emergency control strategies in Moslonka-Lefebvre, M., Pautasso, M. & Jeger, M.J. (2009). Disease cattle, France, 2005. Transbound. Emerg. Dis., 58, 110–120. spread in small-size directed networks: epidemic threshold, correlation Ruiz, G.M. & Carlton, J.T. (2003). Invasion vectors: a conceptual between links to and from nodes, and clustering. J. Theor. Biol., 260, framework for management. In Invasive Species: Vectors and 402–411. Management Strategies. (ed Carlton, J.T.). Island Press, Washington, Moslonka-Lefebvre, M., Harwood, T., Jeger, M.J. & Pautasso, M. (2012). D.C., pp. 459–504. SIS along a continuum (SISc) epidemiological modelling and control of Seebens, H., Gastner, M. & Blasius, B. (2013). The risk of marine diseases on directed trade networks. Math. Biosci., 236, 44–52. bioinvasion caused by global shipping. Ecol. Lett., 16, 782–790. Muirhead, J.R. & MacIsaac, H.J. (2005). Development of inland lakes as Smith, R.D. (2006). Responding to global infectious disease outbreaks: hubs in an invasion network. J. Appl. Ecol., 42, 80–90. lessons from SARS on the role of risk perception, communication and Mukherjee, S. (2012). Statistical analysis of the road network of India. management. Soc. Sci. Med., 63, 3113–3123. Pramana-J. Phys., 79, 483–491. Szendroi, B. & Csanyi, G. (2004). Polynomial epidemics and clustering in Natale, F., Giovannini, A., Savini, L., Palma, D., Possenti, L., Fiore, G. contact networks. P. Roy. Soc. Lond. B. Bio., 271, 364–366. et al. (2009). Network analysis of Italian cattle trade patterns and Tatem, A.J. (2009). The worldwide airline network and the dispersal of evaluation of risks for potential disease spread. Prev. Vet. Med., 92, exotic species: 2007-2010. Ecography, 32, 94–102. 341–350. Van Kerkhove, M.D., Vong, S., Guitian, J., Holl, D., Mangtani, P., San, Newman, M.E.J. (2003). The structure and function of complex networks. S. et al. (2009). Poultry movement networks in Cambodia: implications SIAM Rev., 45, 167–256. for surveillance and control of highly pathogenic avian influenza Newman, M. (2010). Networks: An Introduction. Oxford University Press, (HPAI/H5N1). Vaccine, 27, 6345–6352. New York, pp. 107–701. Venette, R.C., Kriticos, D.J., Magarey, R.D., Koch, F.H., Baker, Ortiz-Pelaez, A., Pfeiffer, D., Soares-Magalhaes, R. & Guitian, F. (2006). R.H.A., Worner, S.P. et al. (2010). Pest risk maps for invasive alien Use of social network analysis to characterize the pattern of animal species: a roadmap for improvement. Bioscience, 60, 349–362. movements in the initial phases of the 2001 foot and mouth disease Wang, X.F. & Chen, G. (2003). Complex networks: small-world, scale- (FMD) epidemic in the UK. Prev. Vet. Med., 76, 40–55. free and beyond. IEEE Circuits Syst. Mag.,3,6–21. Paini, D.R. & Yemshanov, D. (2012). Modelling the arrival of invasive Watts, D. & Strogatz, S. (1998). Collective dynamics. Nature, 393, 440–442. organisms via the international marine shipping network: a Khapra Wesolowski, A., Eagle, N., Tatem, A.J., Smith, D.L., Noor, A.M., Snow, Beetle Study. PLoS ONE, 7, e44589. R.W. et al. (2012). Quantifying the impact of human mobility on Paini, D.R., Worner, S.P., Cook, D.C., De Barro, P.J. & Thomas, M.B. malaria. Science, 338, 267–270. (2010). Threat of invasive pests from within national borders. Nat. Wilson, M.E. (1995). Travel and the emergence of infectious diseases. Commun., 1, 115. Emerg. Infect. Dis.,1,39–46.
© 2014 John Wiley & Sons Ltd/CNRS Review and Synthesis The spread of invasive species via networks 199
World Health Organisation (2003). Frequently Asked Questions on Editor, Montserrat Vila Severe Acute Respiratory Syndrome (SARS). Available at: http://www. Manuscript received 22 June 2014 who.int/csr/sars/sarsfaq/en/. Last accessed 3 July 2013. First decision made 4 August 2014 World Health Organisation Standards & Trade Development Facility Manuscript accepted 4 November 2014 (2013). International Trade and Invasive Alien Species. Available at: http://www.standardsfacility.org/Files/IAS/STDF_IAS_EN.pdf. Last accessed 2 August 2013.
© 2014 John Wiley & Sons Ltd/CNRS APPENDIX C 143
Interview location: ______Interviewee # ______
Initial Questions: (Please write in the space provided or place a cross in the appropriate box)
1. Are you a:
producer,
wholesaler, or
both?
Have you been read the information sheet and do you consent to be interviewed? (Mark with X if “yes”)
2. What produce do you grow? (Farmer sellers only) ______
3. What produce do you sell? ______
Observation: Do you sell different types of produce or the just one type?
Different ______
One type ______
4. (Farmer sellers) Where do you get seeds from? 5. Where do you get planting material from?
______
6. (Trader sellers) Where do you get your produce from?
______i) Do you buy them from one place or several?
One place
Several ii) Where?
______
7. How much do you normally sell per day? (Roughly how many boxes or kg?)
______
i) Does this vary? (Are there times of year when you normally sell a lot and other times when you don’t?)
______
8. Do you:
Only sell your produce here?
Sell your produce at other markets ? Where? ______
Give them away to people? Where? ______
9. Who do you sell or give your produce to?
Are they:
Friends &Relatives Primary Producers Commercial Buyers
General Public Wholesalers (restaurants, supermarkets)
10. Roughly how many people do you sell your crops to?
1-5 5-10 10-20 20-40 40-60 60-100 100+ Observation or ask: How do you transport the produce?
Walking Car Bike/Motorbike Boat Truck/Van
Other means: ______
Ask Market Manager, observe or ask seller: Under what conditions is the produce moved? (e.g. air-conditioned truck, cardboard box in the back of the car)
______
11. How much moved at one time? (Estimate boxes or kgs).
______
12. How is your produce stored before sale?
______
i) Where? ______ii) For how long? ______
Observation: Do you put different types of produce together in one container or keep them separate?
______
13. Are you aware of any pests or diseases affecting your crops or crops in your region?
______
14. Have you ever noticed damage such as twisted dead leaves and dead or dark marks on roots? (Nematode damage).
______
15. What do you do with damaged or diseased produce?
Sell
Dispose of. How?______
16. Do you import or export to other states or regions? If so, where?
______