Individual-based modeling (Evolution)

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.

An ever-growing number of IBMs have been developed by modellers, who aim to aid practitioners and inform a range of issues related to conservation, natural resource management, wildlife management and human socio-economic activities. The range of practitioners using IBMs to inform their decision-making processes include statutary authorities with responsibilities in environmental and natural resource management, non-governmental organizations such as conservation charities, and those interested in the sustainable use of natural resources. Thanks to advances in computational power, data availability and ecological theory, increasingly complicated, sophisticated IBMs can be produced. Yet this does not mean that these models will be more useful in informing environmental decision-making. IBMs typically require specialist computational knowledge to build and refine the model, and analyse the model outputs, and so practitioners are unlikely to have the requisite skills to use IBMs directly. Conversely, practitioners may have greater knowledge of the system being modelled, so their input into the modelling process is highly desirable. Thus, it is important that modellers and practitioners collaborate effectively to develop models which can address the key questions that practitioners are interested in. In this paper we highlight the uses of IBMs in environmental decision-making, identify potential obstacles to their successful use, and discuss how such obstacles can be overcome. We aim to help practitioners understand the potential benefits of IBMs, and to help modellers to understand how to develop IBMs which will better aid practitioners and inform environmental management and policy. We refer to a coastal bird IBM case study, from which general lessons can be learned.

Agent-Based Computing is a diverse research domain concerned with the building of intelligent software based on the concept of "agents". In this paper, we use Scientometric analysis to analyze
all sub-domains of agent-based computing. Our data consists of 1,064 journal articles indexed in the ISI web of knowledge published during a twenty year period: 1990-2010. These were retrieved using a topic search with various keywords commonly used in sub-domains of agent-based computing. In our proposed approach, we have employed a combination of two applications for analysis, namely Network Workbench and CiteSpace - wherein Network Workbench allowed for the analysis of complex network aspects of the domain, detailed visualization-based analysis of the bibliographic data was performed using CiteSpace. Our results include the identification of the largest cluster based on keywords, the timeline of publication of index terms, the core journals and key subject categories. We also identify the core authors, top countries of origin of the manuscripts along with core research institutes. Finally, our results have interestingly revealed the strong presence of agent-based computing in a number of non-computing related scientific domains including Life Sciences, Ecological Sciences and Social Sciences.

Long-term studies are those studies with a longer duration than usual in its topic. They have been essential in ecology to understand the ecological processes that happen in long temporal scales, to determine how general is a pattern emerging from a punctual finding, and to connect cause and effect in biological processes happening with some time lag. Most of ecologists agree about the scientific relevance of long-term studies, but they seldom carry out them (0.3% of published studies). The main handicap is to keep continuous funding during a long time because funding and evaluation system of science is based on short periods. However, there are opportunities to develop long-term studies with available data yet, such as monitoring programs of protected areas or threatened species, museum collections, hunting bags, fisheries or citizen sciences projects; although we will need to keep in mind that those data were not gathered with a long-term aim and this fact may constrain soundness of our conclusions. In spite of the fact that behavioral ecology is a young discipline in Spain, there are already some notable examples of long-term studies. For instance, it has been demonstrated that birds and insects are changing their life cycles in response to climate change and these changes can be leading to phenological mismatchings with the trophic levels in which they rely on. Marking and monitoring of the same individuals along years in the host-parasite system of magpies and cuckoos demonstrated that magpies learn to protect themselves against parasites, while have an inflexible reproductive phenology unable to adapt to environmental variation. Individual-based monitoring of black kites showed the effect of senescence and learning in migratory behavior, breeding territory acquisition, and fitness changes along the life of these birds. Similar results were found for other long-lived birds such as white storks, gulls or storm petrels. Individual marking has allowed building complete pedigrees in bird populations using nest boxes for breeding. Such pedigrees have been essential to understand heritability of several coloration traits of plumage directly linked to the phenotypic individual quality.

Keywords: animal models; individual-based monitoring; microevolution; phenotypic plasticity; replication; temporal trends.

We review and critique an range of perspectives on the scientific role of individual-based evolutionary simulation models as they are used within artificial life. We find that such models have the potential to enrich existing modelling enterprises through their strength in modelling systems of interacting entities. Furthermore, simulation techniques promise to provide theoreticians in various fields with entirely new conceptual, as well as methodological, approaches. However, the precise manner in which simulations can be used as models is not clear. We present two apparently opposed perspectives on this issue: simulation models as "emergent computational thought experiments" and simulation models as realistic simulacra. Through analysis the role that armchair thought experiments play in science, we develop a role for simulation models as opaque thought experiments, that is, thought experiments in which the consequences follow from the premises, but in a non-obvious manner which must be revealed through systematic enquiry. Like their better-known transparent cousins, opaque thought experiments, when understood, result in new insights and conceptual reorganisations. These may stress the current theoretical position of the thought experimenter and engender empirical predictions which must be tested in reality. As such, simulation models, like all thought experiments, are tools with which to explore the consequences of a theoretical position.

The inventorying of biological diversity and studies in biocomplexity require the management of large electronic datasets of organisms. While species inventory has adopted
structured electronic databases for some time, the computer modelling of the functional interactions between biological entities at all levels of life is still in the stage of development. One of the challenges for this type of modelling is the biotic interactions that occur between large datasets of entities represented as computer algorithms. In real-time simulation that models the biotic interactions of large population datasets, the use of computational processing time could be extensive. One way of increasing the efficiency of such simulation is to partition the landscape so that entities need only traverse its local space for entities that falls within the interaction proximity. This article presents an efficient segmentation algorithm for biotic interactions for research related to the modelling and simulation of biological systems.

Plants stand still and interact with their immediate neighbors. Theory has shown that the distances over which these interactions occur may have important consequences for population and community dynamics. In particular, if intraspecific competition occurs over longer distances than interspecific competition (heteromyopia), coexistence can be promoted. We examined how intraspecific and interspecific competition scales with neighbor distance in a target‐neighbor greenhouse competition experiment. Individuals from co‐occurring forbs from calcareous grasslands were grown in isolation and with single conspecific or heterospecific neighbors at distances of 5, 10, or 15 cm (Plantago lanceolata vs. Plantago media and Hieracium pilosella vs. Prunella grandiflora). Neighbor effects were strong and declined with distance. Interaction distances varied greatly within and between species, but we found no evidence for heteromyopia. Instead, neighbor identity effects were mostly explained by relative size differences between target and neighbor. We found a complex interaction between final neighbor size and identity such that neighbor identity may become important only as the neighbor becomes very large compared with the target individual. Our results suggest that species‐specific size differences between neighboring individuals determine both the strength of competitive interactions and the distance over which these interactions occur.