Pattern-oriented Modelling Research Articles

Model the Real, Artificial, or Stylized Iguana? Artificial Life and Adaptive Behavior Can Be Linked Through Pattern-Oriented Modeling

Model the Real, Artificial, or Stylized Iguana? Artificial Life and Adaptive Behavior Can Be Linked Through Pattern-Oriented Modeling

Proposing an information criterion for individual-based models developed in a pattern-oriented modelling framework

Individual-based models (IBMs) have been improved in quality and reliability in recent years with an approach called pattern-oriented modelling (POM). POM proposes guidelines to develop models reproducing multiple patterns observed on the field and to test systematically how well the IBMs reproduce them. POM studies used generally traditional methods of goodness of fit such as the sum of squares evaluation or ad hoc comparisons of fitting errors and variations. Model selection, however, can be a rigorous statistical approach based on information theory and information criteria such as the Akaike's information criterion (AIC) or the deviance information criterion (DIC). So far, it has not been tried to link POM to these rigorous techniques. The main problems to achieve that are: (a) the difficulty to have likelihood functions for IBMs’ parameters and (b) the possibility to obtain posterior distributions of IBMs’ parameters given the patterns to reproduce. In a first part, this paper answers problem (a) by proposing and explaining how to calculate a deviance measure (POMDEV) for models developed in a context of POM. And while answering the second problem, a second part of the paper proposes an information criterion for model selection in a POM context (the pattern-oriented modelling information criterion: POMIC). This criterion does not yet have the same theoretical foundation as, e.g., AIC, but uses formal analogies to the DIC. In a third part POMIC is tested with a modelling exercise. This exercise shows the potential of POMIC to use multiple patterns for selecting among multiple potential submodels and eventually select the most parsimonious and well fitting model version. We conclude that POMIC, although being a heuristically derived approach, can greatly improve the POM framework.

Modelling Mediterranean landscape succession-disturbance dynamics: A landscape fire-succession model

We present a spatially explicit Landscape Fire-Succession Model (LFSM) developed to represent Mediterranean Basin landscapes and capable of integrating modules and functions that explicitly represent human activity. Plant-functional types are used to represent spatial and temporal competition for resources (water and light) in a rule-based modelling framework. Vegetation dynamics are represented using a rule-based community-level modelling approach that considers multiple succession pathways and vegetation climax states. Wildfire behaviour is represented using a cellular-automata model of fire spread that accounts for land-cover flammability, slope, wind and vegetation moisture. Results show that wildfire spread parameters have the greatest influence on two aspects of the model: land-cover change and the wildfire regime. This sensitivity highlights the importance of accurately parameterising this type of grid-based model for representing landscape-level processes. We use a pattern-oriented modelling approach in conjunction with wildfire power-law frequency-area scaling exponent β to calibrate the model. Parameters describing the role of soil moisture on vegetation dynamics are also found to significantly influence land-cover change. Recent improvements in understanding the role of soil moisture and wildfire fuel loads at the landscape-level will drive advances in Mediterranean LFSMs.

A new hypothesis concerning the nature of small pelagic fish clusters: An individual-based modelling study of Sardinella aurita dynamics off West Africa

Coastal populations of small pelagic fish display nested aggregation levels. Above the level of the school structure, clusters are observed the nature of which has not been definitively determined. We hypothesized that these clusters corresponded to a materialisation of the microcohorts originating from successive spawnings of fish populations in their vital domain. A candidate individual-based model was developed to investigate this hypothesis. This model is based on pattern-oriented modelling of a concrete documented case: the dynamics of the round sardinella ( Sardinella aurita) population living off the West African coasts and subject to environmental fluctuations caused by seasonal upwelling. The simulated agents were round sardinella microcohorts situated and moving in a discretised physical environment. The combined effects of environmental forcing (temperature, wind, retention) and inner biological dynamics (reproduction, growth and mortality, competition) condition the dynamics of this population. The modelled behaviour generated realistic dynamic patterns (population distribution, spawning zones, periods and plasticity, biomass fluctuations), which were obtained simultaneously and successfully compared with observations. The steady-state number of microcohorts obtained after simulation convergence was similar to the number of clusters observed in situ in this area for this population. The realism and diversity of the patterns simultaneously simulated suggested the cluster-microcohort equivalence hypothesis as a candidate framework accounting for the origin of the clusters observed in situ. Within this preliminary exploration, we discuss the consistency of the hypothesis and the accuracy of the model. If the correspondence between clusters and microcohorts proves to be real, it may be transient and progressively modified by other environmental factors. If stable over time, as simulated in the model, the number of observed clusters should be related to the number of spawning events in the species’ lifetime.

Projecting population trends of endangered amphibian species in the face of uncertainty: A pattern-oriented approach

Amphibian populations have been declining worldwide for the last three decades. Determining the risk of extinction is one of the major goals of amphibian conservation, yet few quantitative models have been developed for amphibian populations. Like most rare or threatened populations, there is a paucity of life history data available for most amphibian populations. Data on the critical juvenile life stage are particularly lacking. Pattern oriented modeling (POM) has been used successfully to estimate life history parameters indirectly when critical data lacking, but has not been applied to amphibian populations. We describe a spatially explicit, individual-based, stochastic simulation model developed to project population dynamics of pond-breeding amphibian populations. We parameterized the model with life history and habitat data collected for the endangered Houston toad ( Bufo houstonensis), a species for which there is a high degree of uncertainty for juvenile and adult male survival. During model evaluation, we focused on explicitly reducing this uncertainty, evaluating 16 different versions of the model that represented the range of parametric uncertainty for juvenile and adult male survival. Following POM protocol, we compared simulation results to four population-level patterns observed in the field: population size, adult sex ratio, proportion of toads returning to their natal pond, and mean maximum distance moved. Based on these comparisons, we rejected 11 of the 16 model versions. Results of the remaining versions confirmed that population persistence depends heavily on juvenile survival, and further suggested that probability of juvenile survival is likely between 0.0075 and 0.015 (previous estimates ranged from 0.003 to 0.02), and that annual male survival is near 0.15 (previous estimates ranged up to 0.43). Thus, although the uncertainty associated with population projections for the Houston toad remains large, we were able, via the POM approach, to (1) narrow the range of plausible survival estimates, (2) calculate a first estimate of over-wintering metamorph survival, and (3) provide a set of alternative model versions that quantifies the current level of uncertainty associated with population projections. These results suggest that POM is particularly well suited for modeling amphibian systems, and other ecological systems characterized by low structural uncertainty and high parametric uncertainty.

An Overview of Methods for Developing Bioenergetic and Life History Models for Rare and Endangered Species

AbstractMany fish species are at risk to some degree, and conservation efforts are planned or underway to preserve sensitive populations. For many imperiled species, models could serve as useful tools for researchers and managers as they seek to understand individual growth, quantify predator‐prey dynamics, and identify critical sources of mortality. Development and application of models for rare species however, has been constrained by small population sizes, difficulty in obtaining sampling permits, limited opportunities for funding, and regulations on how endangered species can be used in laboratory studies. Bioenergetic and life history models should help with endangered species‐recovery planning since these types of models have been used successfully in the last 25 years to address management problems for many commercially and recreationally important fish species. In this paper we discuss five approaches to developing models and parameters for rare species. Borrowing model functions and parameters from related species is simple, but uncorroborated results can be misleading. Directly estimating parameters with laboratory studies may be possible for rare species that have locally abundant populations. Monte Carlo filtering can be used to estimate several parameters by means of performing simple laboratory growth experiments to first determine test criteria. Pattern‐oriented modeling (POM) is a new and developing field of research that uses field‐observed patterns to build, test, and parameterize models. Models developed using the POM approach are closely linked to field data, produce testable hypotheses, and require a close working relationship between modelers and empiricists. Artificial evolution in individual‐based models can be used to gain insight into adaptive behaviors for poorly understood species and thus can fill in knowledge gaps.

The role of size inequality in self-thinning: A pattern-oriented simulation model for arid savannas

The self-thinning line is a very robust pattern, which can be obtained in modeling studies by a variety of different mechanistic assumptions. Our opinion is that we can only advance in our understanding of mechanisms leading to the self-thinning relationship if we demand that the model also reproduces several other characteristic features (patterns) of the self-thinning process such as the degree of size inequality and the average size. We use a pattern-oriented modeling approach to develop a model of self-thinning under size inequality in overcrowded, even-aged stands, which reproduces these three patterns simultaneously. Our approach is to first develop an initial model based on our current ecological knowledge and then to refine the model by modifying the initial model to derive the model that reproduces all patterns of interest. The initial model is as simple as possible while avoiding incidental, ecologically unjustified, assumptions. It is a further development of zone of influence-simulation models: each plant is described by two circles, one describing a minimum-domain-area and one describing the zone of influence. In the initial model, mortality is “death-by-contact” of minimum-domain-areas and growth is a function of inter-tree competition, i.e. overlapping zones of influence. Model parameterization is based on field data on Acacia reficiens in southern Africa. Simulations follow patches of initially small trees through time for up to 1000 years with five parameters, three describing growth and two describing inter-tree competition. A sensitivity analysis shows that all parameters of the initial model contribute significantly to the number and size of plants through time. The two competition parameters, which describe competitive asymmetry and the size of the zone of influence relative to canopy size, are both important for generating size inequality. Thus, both competitive asymmetry and spatial pattern contribute to size inequality, and their relative importance may vary greatly. The sensitivity analysis suggests that all processes included in the initial model are essential to the evolution of size inequality. However, size inequality under the initial model is below field values, meaning that additional, as yet unconsidered processes, contribute to size inequality. Our best-fit model additionally contains details on growth stochasticity. This study establishes the often-proposed direct link between mortality driven by local competition and self-thinning and highlights the importance of stochasticity in ecological processes.

POSAML: A visual modeling language for middleware provisioning

Next generation distributed applications are often hosted on heterogeneous platforms including different kinds of middleware. Due to the applications’ growing functional complexity and their multiple quality of service (QoS) requirements, system developers are increasingly facing a substantial number of middleware provisioning challenges, which include configuring, optimizing and validating the middleware platforms for QoS properties. Traditional techniques for middleware provisioning tend to use non-intuitive, low-level and technology-specific approaches, which are tedious, error prone, and non-reusable across different technologies. Quite often the middleware provisioning activities are carried out by different actors without much interaction among them, which results in an iterative trial-and-error process to provisioning. Higher level abstractions, particularly those that use visual models, are effective in addressing these challenges. This paper describes the design of a visual modeling language called POSAML (pattern-oriented software architecture modeling language) and associated tools that provide an intuitive, higher level and unified framework for provisioning middleware platforms. POSAML provides visual modeling capabilities for middleware-independent configurations and optimizations while enabling automated middleware-specific validation of system QoS properties.

Simulating cryptic movements of a mangrove crab: Recovery phenomena after small scale fishery

The semi-terrestrial burrowing crab Ucides cordatus is an important ecological component and economic resource of Brazilian mangrove forests. The crab population of the Caeté peninsula (the location of our study site) has been exploited for the last 40 years. Recovery of fished areas by crabs from non-fished areas under the roots of the mangrove tree Rhizophora mangle was hypothesized to be an important buffer mechanism against rapid overfishing. However, catch per unit effort decreased in recent years, suggesting that the sustainability of the crab fishery might become endangered. It is therefore important to better understand the movement behaviour of these crabs, even though it is hard to observe directly. Following the approach of pattern-oriented modelling, we developed an individual-based model to infer movement behaviour from patterns in density recovery that were observed in field experiments. Two alternative submodels simulating factors causing movement were contrasted: with and without local competition among crabs. To describe local competition, the field-of-neighbourhood (FON) approach was used, which was originally designed for sessile organisms. Without competition, unrealistically high movement frequencies were required to fit the observed linear recovery patterns. With competition included, better fits to the recovery patterns were obtained, and lower and thus more realistic movement frequencies were sufficient. This indicates that local competition between crabs is the main reason for them to move and change their burrows. Our work shows that the FON approach is suitable to describe local interactions not only among sessile organisms, but also among mobile organisms in conditions of competition for resources. The simulation results illustrate the importance of the non-fished rooted areas as buffers against rapid over-fishing. The IBU model presents a potential for future analysis of these buffer mechanisms and thus for a better understanding of the crab fishery and its management.

Patterns for parameters in simulation models

Population simulation models are often used in conservation biology to assess human impact on species survival, but have been under heavy critique due to parameterization problems. The general notion is that only models for which parameters were directly assessed from field data can show that a certain process is working, in contrast to using field data to fit models. We would like to provide an update regarding the relationship between simulation model parameterization and the use of field data as ‘pattern’ for revealing ‘structurally realistic’ parameters and processes. ‘Pattern-oriented modelling’ is an inverse modelling technique in ecology that considers the use of multiple field data pattern simultaneously to filter the parameterizations which were successfully tested against all available data on system dynamics. We highlight this technique with an example of our own research and conclude that this approach is especially suitable for models in conservation of rare and elusive species, where data are generally scarce.

Pattern-oriented modelling for estimating unknown pre-breeding survival rates: The case of the Lesser Spotted Woodpecker ( Picoides minor)

In population viability analysis we are often faced with a lack of knowledge of survival rates in animal populations. In particular, survival of recruits is usually hard to assess. However, data on population structure might be considered as patterns that contain valuable information to estimate missing parameters indirectly. As an example for this pattern-oriented modelling and parameterization, pre-breeding survival rate of the endangered Lesser Spotted Woodpecker ( Picoides minor) was determined here using data on population structure (e.g. sex ratio) and reproductive success at the population level (e.g. nesting success). Therefore, an individual-based model was developed simulating the population dynamics for two different populations that had been empirically studied at Lake Möckeln, Sweden, and Taunus, Germany. For both populations, a small range for pre-breeding survival rates could be identified wherein all simulated patterns corresponded best to the empirical values. Pre-breeding survival rate was found to be higher in the German scenario than in the Swedish and geographical variation in life-history traits is discussed as a possible reason. It is concluded that the pattern-oriented approach is a valuable method for estimating missing demographic parameters, even when using weak patterns from empirical investigations. Furthermore, it was shown that the use of multiple patterns is necessary for this purpose.

Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam

The tools and methods developed by different scientific communities to simulate the dynamics of land use have emphasised either processes or patterns of changes. Agent-based models (ABM) belong to the former category while many spatially explicit simulation models belong to the latter. These two different modelling approaches were jointly implemented at a study site in Vietnam to assess their respective strengths and weaknesses with respect to their capacity to support the formulation of land-use policy and to influence decision-making by multiple groups of stakeholders. SAMBA is a people-centred approach combining an ABM, a role-playing game and a geographic information system. Participatory simulations help elicit the rules of the ABM and calibrate the model, while the model supports the participatory exploration of land-use change scenarios over longer time periods. CLUE-s is a spatial simulation model which explores changes in land-use patterns within user-specified rules of permissible change and rates of change. Driving factors that influence changes from one land-use type to another are defined by combining spatially explicit data on land use and supposed driving factors in a logistical regression analysis. Alternatively, the decision rules that were revealed during the participatory simulations – with the role plays and the multi-agent modelling of the SAMBA approach – were incorporated in the CLUE-s model to provide more realistic estimates for the varying influence of land-use drivers. We checked the respective validity of the two models by applying them at the same site and comparing their outputs. As a result, no single approach was obviously superior according to the validation statistics. The three approaches turned out to be complementary in simulating land-use patterns, while providing different types of information. Integration of the two models into a rule-based version of CLUE-s helped reconciling data-driven statistical models and process-driven agent-based models in LUCC research. This new model reinforced the overall framework by facilitating the partnership between researchers from different scientific communities and between researchers and multiple groups of stakeholders. It may also better respond to the expectations of land users at different levels of the decision-making hierarchy.

Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology

Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.

Power-driven technology mapping using pattern-oriented power modelling

Power consumption has become a great concern for IC and system designs. As a consequence, power-driven technology mapping has attracted several pieces of research. However, the power model they used cannot properly capture the power dissipation when the output of a gate does not switch. In this paper, we propose a pattern-oriented power modelling for improved technology mapping. We first perform a profitability study using the complete pattern to pattern transition data organised in tabular form. Then, we propose a probability-based, pattern-oriented technology mapping method. Empirical results on benchmark circuits demonstrate that the proposed method delivered an average of 13% power reduction compared with the traditional mapping method.

On a pattern-oriented model for intrusion detection

Operational security problems, which are often the result of access authorization misuse, can lead to intrusion in secure computer systems. We motivate the need for pattern-oriented intrusion detection, and present a model that tracks both data and privilege flows within secure systems to detect context-dependent intrusions caused by operational security problems. The model allows the uniform representation of various types of intrusion patterns, such as those caused by unintended use of foreign programs and input data, imprudent choice of default privileges, and use of weak protection mechanisms. As with all pattern-oriented models, this model cannot be used to detect new, unanticipated intrusion patterns that could be detected by statistical models. For this reason, we expect that this model will complement, not replace, statistical models for intrusion detection.

Pattern-oriented modelling in population ecology

Ecological modelling should take its orientation more from real patterns observed in nature, than has been the case up to now, to overcome the deficiencies of the present strategies. Firstly, the orientation towards patterns provides guidelines about the manner and extent of the aggregation of biological information in the model. Modelling thereby loses much of its arbitrariness; secondly, pattern-oriented models are not ‘scale-free’, i.e. they relate explicitly to spatial and temporal scales; and finally, they produce comparative predictions which are better suited for testing than the predictions achieved by models, which are only either complex or generalizing. To demonstrate the strategy of pattern-oriented modelling and its advantages, three examples from population ecology are presented: (1) In a model concerning density dependence and individual variability, the orientation towards a pattern in weight distributions requires explicit inclusion of the within-generation time scale in the model. (2) Orientation towards a pattern in the dispersal capabilities of small organisms leads to a metapopulation model, where environmental correlations about certain distances are taken into account. The survival of the metapopulation depends mainly on the ratio of the correlation distance to the range of dispersal of the organisms. (3) The wave-like pattern of the spread of rabies is reproduced by an extremely simple one-dimensional model, which is based on an extended cellular automaton approach. From this basic model, a description on finer spatial and temporal scales can be developed with the aim of constructing a model which allows for the investigation of spatial barriers against the spread of rabies. A comparison of the three example models shows that the main features of pattern-oriented models are generic. In producing comparative predictions which are related explicitly to scales, pattern-oriented modelling seems to be a strategy well suited to the ‘scaling up’ from population ecology to community and ecosystem ecology.