Suzanne Sadedin

Complexity in landscape ecology

Foreword.- Preface.- 1. Complexity and ecology.- 2. Seeing the wood for the trees.- 3. Complexity in landscapes.- 4. Oh, what a tangled web.- 5. The imbalance of nature.- 6. Populations in landscapes.- 7. Living with the neighbours.- 8. Genetics and adaptation in landscapes.- 9. Virtual worlds.- 10. Digital ecology.- 11. The global picture.- References.- Index.

Case studies and mathematical models of ecological speciation. 3: Ecotype formation in a Swedish snail

Formation of partially reproductively isolated ecotypes in the rough periwinkle, Littorina saxatilis, may be a case of incipient nonallopatric ecological speciation. To better understand the dynamics of ecotype formation, its timescale, driving forces and evolutionary consequences, we developed a spatially explicit, individual‐based model incorporating relevant ecological, spatial and mate selection data for Swedish L. saxatilis. We explore the impact of bounded hybrid superiority, ecological scenarios and mate selection systems on ecotype formation, gene flow and the evolution of prezygotic isolation. Our model shows that ecotypes are expected to form rapidly in parapatry under conditions applicable to Swedish L. saxatilis and may proceed to speciation. However, evolution of nonrandom mating had complex behaviour. Ecotype evolution was inhibited by pre‐existing mating preferences, but facilitated by the evolution of novel preferences. While in many scenarios positive assortative mating reduced gene flow between ecotypes, in others negative assortative mating arose, preferences were lost after ecotype formation, preferences were confined to one ecotype or the ancestral ecotype became extinct through sexual selection. Bounded hybrid superiority (as observed in nature) enhanced ecotype formation but increased gene flow. Our results highlight that ecotype formation and speciation are distinct processes: factors that contribute to ecotype formation can be detrimental to speciation and vice versa. The complex interactions observed between local adaptation and nonrandom mating imply that generalization from data is unreliable without quantitative theory for speciation.

Biology of Applied Digital Ecosystems

A primary motivation for research in digital ecosystems is the desire to exploit the self-organising properties of natural ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the biological processes that contribute to these properties have not been made explicit in digital ecosystem research. Here, we discuss how biological properties contribute to the self-organising features of natural ecosystems. These properties include populations of evolving agents, a complex dynamic environment, and spatial distributions which generate local interactions. The potential for exploiting these properties in artificial systems is then considered. An example architecture, the digital business ecosystem (DBE), is considered in detail. Simulation results imply that the DBE performs better at large scales than a comparable service-oriented architecture. These results suggest that incorporating ideas from theoretical ecology can contribute to useful self-organising properties in digital ecosystems.

The Emergence of Social Consensus in Boolean Networks

Social order and unity require consensus among individuals about cooperation and other issues. Boolean network models (BN) help to explain the role played by peer interactions in the emergence of consensus. BN models represent a society as a network in which individuals are the nodes (with two states, e.g. agree/disagree) and social relationships are the edges. BN models highlight the influence of peer interactions on social cooperation, in contrast to models, such as prisoners dilemma, that focus on individual strategies. In BN models, the behavior that emerges from peer interactions differs in subtle, but important ways from equivalent mathematical models (e.g. Markov, dynamic systems). Despite their simplicity, BN models provide potentially important insights about many social issues. They confirm that there is an upper limit to the size of groups within which peer interactions can create and maintain consensus. In large social groups, a combination of peer interaction and enforcement is needed to achieve consensus. Social consensus is brittle in the face of global influences, such as mass media, with the peer network at first impeding the spread of alternative views, then accelerating them once a critical point is passed. BN models are sensitive both to the network topology, and to the degrees of influence associated with peer-peer connections

Dual-phase evolution in complex adaptive systems

Understanding the origins of complexity is a key challenge in many sciences. Although networks are known to underlie most systems, showing how they contribute to well-known phenomena remains an issue. Here, we show that recurrent phase transitions in network connectivity underlie emergent phenomena in many systems. We identify properties that are typical of systems in different connectivity phases, as well as characteristics commonly associated with the phase transitions. We synthesize these common features into a common framework, which we term dual-phase evolution (DPE). Using this framework, we review the literature from several disciplines to show that recurrent connectivity phase transitions underlie the complex properties of many biological, physical and human systems. We argue that the DPE framework helps to explain many complex phenomena, including perpetual novelty, modularity, scale-free networks and criticality. Our review concludes with a discussion of the way DPE relates to other frameworks, in particular, self-organized criticality and the adaptive cycle.

Dual phase evolution — a mechanism for self-organization in complex systems

A key challenge in complexity theory is to understand self-organization: how order emerges out of the interactions between elements within a system. [1980] pointed out that in dissipative systems (open systems that exchange energy with their environment), order can increase. Rather then being suppressed, positive feedback allows local irregularities to grow into global features. [1978] introduced the idea of an order parameter and pointed out that critical behaviour (e.g. the firing of a laser) always occurs at some predictable value of the parameter. Nevertheless, many questions remain, especially about the ways in which different processes act in concert with one another. In particular, the relationships between self-organization, natural selection and the evolution of complexity remain unclear.

A dual phase evolution model of adaptive radiation in landscapes

In this study, we describe an evolutionary mechanism - Dual Phase Evolution (DPE) - and argue that it plays a key role in the emergence of internal structure in complex adaptive systems (CAS). Our DPE theory proposes that CAS exhibit two well-defined phases - selection and variation - and that shifts from one phase to the other are triggered by external perturbations. We discuss empirical data which demonstrates that DPE processes play a prominent role in species evolution within landscapes and argue that processes governing a wide range of self-organising phenomena are similar in nature. In support, we present a simulation model of adaptive radiation in landscapes. In the model, organisms normally exist within a connected landscape in which selection maintains them in a stable state. Intermittent disturbances (such as fires, commentary impacts) flip the system into a disconnected phase, in which populations become fragmented, freeing up areas of empty space in which selection pressure lessens and genetic variation predominates. The simulation results show that the DPE mechanism may indeed facilitate the appearance of complex diversity in a landscape ecosystem.

COMPLEXITY IN LANDSCAPES

Complexity often arises in the way things are distributed in a landscape. Sampling is subject to scale and can display properties of fractals. Cellular automata, which represent a landscape as a grid of sites, are often used to model processes in landscapes. These models highlight the phase change that occurs between connected and fragmented landscapes.

A toy model of faith-based systems evolution

A simple agent-based model of the evolution of faith-based systems (FBS) in human social networks is presented. In the model, each agent subscribes to a single FBS, and may be converted to share a different agents FBS during social interactions. FBSs and agents each possess heritable quantitative traits that affect the probability of transmission of FBSs. The influence of social network conditions on the intermediate and final macroscopic states is examined.

Implications of the social brain hypothesis for evolving human-like cognition in digital organisms

Data show that human-like cognitive traits do not evolve in animals through natural selection. Rather, human-like cognition evolves through runaway selection for social skills. Here, we discuss why social selection may be uniquely effective for promoting human-like cognition, and the conditions that facilitate it. These observations suggest future directions for artificial life research aimed at generating human-like cognition in digital organisms.