The fungal microbiome, or “mycobiome” of plants is diverse and important to host health, but the fluxes of fungi among plant hosts and with the surrounding environment are poorly understood. In chapter two, we employed sterile culture techniques and spatial sampling to examine leaves as possible vectors for transfer of their endophytic fungi from the canopy to substrate on the forest floor, as predicted by the Foraging Ascomycete hypothesis. Some foliar endophytic fungal species are also present as wood-decomposing fungi on the forest floor, that transfer of mycelium across these two life history stages can occur, that endophytic life history stages are buffered from environmental conditions in comparison to wood-decomposing fungi, and that spatial linkages between the two life history stages can be observed. In another study, described in chapter 3, wood and leaf wood endophytes were sampled across a 25 ha plot, to explore landscape patterns of mycobiomes, and to explore the concept of a core microbiome in aerial plant tissues. We found that core microbiomes may be observed in a real ecological setting, but that the concept of core must be carefully defined and that some level of buffering from disturbance may be necessary to allow core microbiomes to assemble. In chapter four, we return to examine some of the assumptions and implications of the Foraging Ascomycete hypothesis, with an agent-based model. We model the conditions under which dispersal through falling leaves may represent a fitness-enhancing dispersal strategy for fungi, and that deforestation as is currently underway throughout the world may have impacts on fungi that rely upon a canopy- inhabiting life stage for dispersal. In chapter five, some challenges associated with environmental sampling of microbes using illumina© MiSeq sequences are critically examined. We find that biases introduced by random sampling at various stages of IVenvironmental DNA extraction and illumina© MiSeq sequencing are not well corrected by currently accepted bioinformatic algorithms. In addition, information loss from differential extraction, PCR amplification, and sequencing success, requires that users of MiSeq read libraries to interpret read abundances carefully. This dissertation includes previously published, co-authored material.

Este trabalho propõe um modelo de um mercado que pode ser simulado em diversas condições. Pode-se pensar neste modelo como um laboratório para simulação e estudo de diferentes configurações de mercado. O modelo foi desenvolvido sob a plataforma de software NetLogo para a execução das simulações. Apresenta-se também, de forma sintética um conceito de mercado, uma visão de economia, de marketing, de estratégia e do comportamento do consumidor. Forma analisadas diversas formas de modelagem e decidiu-se utilizar sistemas multiagentes, pois são flexíveis e permitem a simulação de diversos tipos de fenômenos. A primeira tentativa de desenvolver este modelo apresentou resultados distintos de um mercado real, pois revelou-se muito mecânico e processual. Isso nos levou a busca de modelos adaptativos inteligentes para o comportamento dos agentes, bem como, nos concentrar na dinâmica de interação entre os agentes / This work aims at developing a model to simulate a market under several conditions. This model can be viewed as a laboratory to simulate and study different markets configurations. The model simulation was developed using the NetLogo language. A concept of market, economy, marketing, strategy and consumer behavior are briefly presented. After analyzing several ways of implementing the model it was decided to use agent based simulation because of its flexibility and possibility of simulating a variety of behaviors. A first approach to develop the model resulted in a very mechanic and artificial market simulation. This result lead the simplification of the model and focus on adaptive behavior and the interaction among the agents

Este trabalho propõe um modelo de um mercado que pode ser simulado em diversas condições. Pode-se pensar neste modelo como um laboratório para simulação e estudo de diferentes configurações de mercado. O modelo foi desenvolvido sob a plataforma de software NetLogo para a execução das simulações. Apresenta-se também, de forma sintética um conceito de mercado, uma visão de economia, de marketing, de estratégia e do comportamento do consumidor. Forma analisadas diversas formas de modelagem e decidiu-se utilizar sistemas multiagentes, pois são flexíveis e permitem a simulação de diversos tipos de fenômenos. A primeira tentativa de desenvolver este modelo apresentou resultados distintos de um mercado real, pois revelou-se muito mecânico e processual. Isso nos levou a busca de modelos adaptativos inteligentes para o comportamento dos agentes, bem como, nos concentrar na dinâmica de interação entre os agentes / This work aims at developing a model to simulate a market under several conditions. This model can be viewed as a laboratory to simulate and study different markets configurations. The model simulation was developed using the NetLogo language. A concept of market, economy, marketing, strategy and consumer behavior are briefly presented. After analyzing several ways of implementing the model it was decided to use agent based simulation because of its flexibility and possibility of simulating a variety of behaviors. A first approach to develop the model resulted in a very mechanic and artificial market simulation. This result lead the simplification of the model and focus on adaptive behavior and the interaction among the agents

Sexually transmitted diseases affect millions of lives every year. In order to most effectively use prevention resources epidemiologists deploy models to understand how the disease spreads through the population and which intervention methods will be most effective at reducing disease perpetuation. Increasingly agent-based models are being used to simulate population heterogeneity and fine-grain sociological effects that are difficult to capture with traditional compartmental and statistical models. A key challenge is using a sufficiently large number of agents to produce robust and reliable results while also running in a reasonable amount of time. In this thesis we show the effectiveness of agent-based modeling in planning coordinated responses to a sexually transmitted disease epidemic and present efficient algorithms for running these models in parallel and in a distributed setting. The model is able to account for population heterogeneity like age preference, concurrent partnership, and coital dilution, and the implementation scales well to large population sizes to produce robust results in a reasonable amount of time. The work helps epidemiologists and public health officials plan a targeted and well-informed response to a variety of epidemic scenarios.

Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section. Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity. Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena. Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool.

The whole in a complex system is the sum of its parts, plus the interactions between the parts. Understanding social, biological, and economic systems therefore often depends on understanding their patterns of interactions---their networks. In this thesis, the approach is to understand complex systems by making simple network models with nodes and links. It is first of all an attempt to investigate how the communication over the network affects the network structure and, vice versa, how the network structure affects the conditions for communication. To explore the local mechanism behind network organization, we used simplified social systems and modeled the response to communication. Low communication levels resulted in random networks, whereas higher communication levels led to structured networks with most nodes having very few links and a few nodes having very many links. We also explored various models where nodes merge into bigger units, to reduce communication costs, and showed that these merging models give rise to the same kind of structured networks. In addition to this modeling of communication networks, we developed new ways to measure and characterize real-world networks. For example, we found that they in general favor communication on short distance, two-three steps away in the network, within what we call the information horizon. / Helheten i ett komplext system är mer än summan av dess delar, då den även inbegriper interaktionerna mellan dem. Att studera sociala, biologiska och ekonomiska system blir därför ofta en fråga om att förstå deras interaktionsmönster, d.v.s. deras nätverk av noder och länkar. Med utgångspunkt i enkla nätverksmodeller undersöker avhandlingen i huvudsak hur kommunikation i nätverk påverkar nätverksstrukturen och, vice versa, hur nätverksstrukturen påverkar villkoren för kommunikation. Vi utforskade mekanismerna bakom hur nätverk är organiserade genom att modellera effekten av kommunikation i förenklade sociala system. En låg kommunikationsnivå visade sig ge upphov till kaotiska nätverk där ingen nod i princip hade fler länkar än någon annan. En hög kommunikationsnivå resulterade däremot i strukturerade nätverk, med några få centrala noder med många länkar, medan flertalet noder var perifera med enbart några få länkar. Det visade sig också att alla aktörer i nätverket gynnades av kommunikation, även när den var ojämnt fördelad. Kvaliteten på kommunikationen, d.v.s. informationens giltighet, var också avgörande för vilka positioner som gynnades i ett nätverk, vilket vi visade genom att studera aktörer som spred falsk information. Eftersom effektiv kommunikation är en viktig del i många nätverk betraktar vi utvecklingen av dem som en optimeringsprocess. Varje kommunikationshandling mellan noderna tar tid och genom att slå sig samman till större enheter begränsas dessa kostnader och gör nätverket effektivare. Dessa s.k. sammanslagningsmodeller gav upphov till samma typ av strukturerade nätverk som ovan. Genom att utveckla olika sätt att mäta nätverksstrukturer visade vi bland annat att många verkliga system främjar kommunikation över korta avstånd, två-tre steg bort i nätverket, innanför det vi kallar informationshorisonten. Vi uppskattade också den mängd information som krävs för att orientera sig i städer, och fann att det är lättare att hitta i moderna, planerade städer än i äldre städer som utvecklats under lång tid.

This dissertation investigates selected empirical and theoretical aspects of land-use and land-cover change (LUCC) in exurban areas. Two challenges – observation and monitoring of LUCC, and spatially explicit modeling, are addressed using three main approaches – measuring, reviewing and agent-based modeling (ABM). All of these approaches focus on LUCC at the individual household level, investigating how micro-scale elements interact to influence macro-scale functional patterns—bottom-up analysis. First, the temporal change of the quantity and pattern of land-cover types within exurban residential parcels in three townships in the southeastern Michigan is examined using landscape metrics and local indicators of spatial association at the parcel and parcel-neighborhood level respectively. The results demonstrate that the number and area of exurban residential parcels increased steadily from 1960 to 2000, and different land-cover types have distinctive temporal changes over time. The results also indicate that there is a convergence process at the neighborhood level through which the quantity and pattern of land cover in parcels conform with the neighborhood appearance. Second, 51 urban residential choice models based on ABM are reviewed. The results divide these models into three categories (i.e. models based on classical theories, models focusing on different stages of urbanization process; and integrated ABM and microsimulation models). This review also compares the differences among these models in their representations of three essential features brought by the technique of ABM: agent heterogeneity, the land market and output measurement. Challenges in incorporating these features, such as the trade-off between the simplicity and abstraction of model and the complexity of urban residential system, interactions of multiple features and demands for data at individual level, are also discussed. Third, the effects of agent heterogeneity on spatial and socioeconomic outcomes under different levels of land-market representations are explored through three experiments using a stylized agent-based land-market model. The results reveal that budget heterogeneity has prominent effects on socioeconomic outcomes, while preference heterogeneity is highly pertinent to spatial outcomes. The relationship between agent heterogeneity and macro-measures becomes more complex as more land-market mechanisms are represented. The results also imply that land-market representation (e.g., competitive bidding) is indispensable to reproduce the results of classical urban land market models (e.g., monocentric city model) in a spatial ABM when agents are heterogeneous.

Agriculture is undergoing extreme change. The introduction of new generation agricultural products has generated an increased need for efficient and accurate product segregation across a number of Canadian agricultural sectors. In particular, monitoring, controlling and preventing commingling of various wheat grades is critical to continued agri-food safety and quality assurance in the Canadian grain handling system. The Canadian grain handling industry is a vast regional supply chain with many participants. Grading of grain for blending had historically been accomplished by the method of Kernel Visual Distinguishability (KVD). KVD allowed a trained grain grader to distinguish the class of a registered variety of wheat solely by visual inspection. While KVD enabled rapid, dependable, and low-cost segregation of wheat into functionally different classes or quality types, it also put constraints on the development of novel traits in wheat. To facilitate the introduction of new classes of wheat to enable additional export sales in new markets, the federal government announced that KVD was to be eliminated from all primary classes of wheat as of August 1, 2008. As an alternative, the Canadian Grain Commission has implemented a system called Variety Eligibility Declaration (VED) to replace KVD. As a system based on self-declaration, the VED system may create moral hazard for misrepresentation. This system is problematic in that incentives exist for farmers to misrepresent their grain. Similarly, primary elevators have an incentive to commingle wheat classes in a profitable manner. Clearly, the VED system will only work as desired for the grain industry when supported by a credible monitoring system. That is, to ensure the security of the wheat supply chain, sampling and testing at some specific critical points along the supply chain is needed. While the current technology allows the identification of visually indistinguishable grain varieties with enough precision for most modern segregation requirements, this technology is relatively slow and expensive. With the potential costs of monitoring VED through the current wheat supply chain, there is a fundamental tradeoff confronting grain handlers, and effective handling strategies will be needed to maintain historical wheat uniformity and consistency while keeping monitoring costs down. There are important operational issues to efficiently testing grain within the supply chain, including the choice of the optimal location to test and how intensively to test. The testing protocols for grain deliveries as well as maintaining effective responsiveness to information feedback among farmers will certainly become a strategic emphasis for wheat handlers in the future. In light of this, my research attempts to identify the risks, incentives and costs associated with a functional declaration system. This research tests a series of incentives designed to generate truthful behavior within the new policy environment. In this manner, I examine potential and easy to implement testing strategies designed to maintain integrity and efficiency in this agricultural supply chain. This study is developed in the first instance by using an analytic model to explore the economic incentives for motivating farmer’s risk control efforts and handlers’ optimal handling strategies with respect to testing cost, penalty level, contamination risks and risk control efforts. We solve for optimal behavior in the supply chain assuming cost minimization among the participants, under several simplifying assumptions. In reality, the Canadian grain supply chain is composed of heterogeneous, boundedly rational and dynamically interacting individuals, and none of these characteristics fit the standard optimization framework used to solve these problems. Given this complex agent behavior, the grain supply chain is characterized by a set of non-linear relationships between individual participants, coupled with out of equilibrium dynamics, meaning that analytic solutions will not always identify or validate the set of optimized strategies that would evolve in the real world. To account for this inherent complexity, I develop an agent-based (farmers and elevators) model to simulate behaviour in a more realistic but virtual grain supply chain. After characterizing the basic analytics of the problem, the grain supply chain participants are represented as autonomous economic agents with a certain level of programmed behavioral heterogeneity. The agents interact via a set of heuristics governing their actions and decisions. The operation of a major portion of the Canadian grain handling system is simulated in this manner, moving from the individual farm up through to the country elevator level. My simulation results suggest testing strategies to alleviate misrepresentation (moral hazard) in this supply chain are more efficient for society when they are flexible and can be easily adjusted to react to situational change within the supply chain. While the idea of using software agents for modeling and understanding the dynamics of the supply chain under consideration is somewhat novel, I consider this exercise a first step to a broader modeling representation of modern agricultural supply chains. The agent-based simulation methodology developed in my dissertation can be extended to other economic systems or chains in order to examine risk management and control costs. These include food safety and quality assurance network systems as well as natural-resource management systems. Furthermore, to my knowledge there are no existing studies that develop and compare both analytic and agent-based simulation approaches for this type of complex economic situation. In the dissertation, I conduct explicit comparisons between the analytic and agent-based simulation solutions where applicable. While the two approaches generated somewhat different solutions, in many respects they led to similar overall conclusions regarding this particular agricultural policy issue.

Literature on the modeling and simulation of complex adaptive systems (cas) has primarily advanced vertically in different scientific domains with scientists developing a variety of domain-specific approaches and applications. However, while cas researchers are inherently interested in an interdisciplinary comparison of models, to the best of our knowledge, there is currently no single unified framework for facilitating the development, comparison, communication and validation of models across different scientific domains. In this thesis, we propose first steps towards such a unified framework using a combination of agent-based and complex network-based modeling approaches and guidelines formulated in the form of a set of four levels of usage, which allow multidisciplinary researchers to adopt a suitable framework level on the basis of available data types, their research study objectives and expected outcomes, thus allowing them to better plan and conduct their respective research case studies. Firstly, the complex network modeling level of the proposed framework entails the development of appropriate complex network models for the case where interaction data of cas components is available, with the aim of detecting emergent patterns in the cas under study. The exploratory agent-based modeling level of the proposed framework allows for the development of proof-of-concept models for the cas system, primarily for purposes of exploring feasibility of further research. Descriptive agent-based modeling level of the proposed framework allows for the use of a formal step-by-step approach for developing agent-based models coupled with a quantitative complex network and pseudocode-based specification of the model, which will, in turn, facilitate interdisciplinary cas model comparison and knowledge transfer. Finally, the validated agent-based modeling level of the proposed framework is concerned with the building of in-simulation verification and validation of agent-based models using a proposed Virtual Overlay Multiagent System approach for use in a systematic team-oriented approach to developing models. The proposed framework is evaluated and validated using seven detailed case study examples selected from various scientific domains including ecology, social sciences and a range of complex adaptive communication networks. The successful case studies demonstrate the potential of the framework in appealing to multidisciplinary researchers as a methodological approach to the modeling and simulation of cas by facilitating effective communication and knowledge transfer across scientific disciplines without the requirement of extensive learning curves.

This thesis explores a border security system based on agent-based modeling and simulation (ABMS). The ABMS software platform, map aware non-uniform automata, is used to model various scenarios and evaluate the border security system given a set of infiltrators who have evolutionary behavior governed by a genetic algorithm (GA). The GA is used to represent adaptive behavior of the enemy when the friendly force has deployed our border security at a maximum level. By using a near optimal Latin hypercube design, our simulation runs are implemented efficiently and the border security system is analyzed using four different kinds of measures of effectiveness. / text