Abstract
To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Pattern-oriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.
Highlights
Environmental change threatens the loss of biodiversity and associated ecosystem functions, services, and stability
Individual or agent-based modelling (IBM/agent-based models (ABMs)) is increasingly used to synthesise knowledge gained through empirical studies, assess whether hypotheses developed about system functions are plausible, and make predictions about system dynamics for potential scenarios (Vincenot, 2018)
In this review we aim to make Patternoriented modelling (POM) development more accessible by providing detailed answers to the questions: (i) where in the ecological hierarchy can we find patterns; (ii) what kinds of patterns are useful; (iii) how should simulations and observations be compared; and (iv) when in the modelling cycle are patterns used? Each section builds on the previous text by using introduced terms and concepts to describe characteristics of useful patterns and guide modellers through effective implementation strategies
Summary
Environmental change threatens the loss of biodiversity and associated ecosystem functions, services, and stability. POM is a strategy to achieve structural realism of models It encourages use of multiple patterns, observed at different scales and hierarchical levels of ecological systems, as filters for (i) selecting variables including input and output, (ii) designing submodels for processes including individual behaviour and feedback mechanisms with the environment, and (iii) parameterising and optimising models (Grimm et al, 2017). These guidelines would help modellers identify which patterns would most benefit the determination of model parameters and processes, and enhance empirical practice by highlighting the value of specific biological information for predictive modelling We present such a classification of empirical patterns and provide specific guidelines throughout on how properly to apply empirical patterns in ABMs. In this review we aim to make POM development more accessible by providing detailed answers to the questions (see Fig. 2A): (i) where in the ecological hierarchy can we find patterns; (ii) what kinds of patterns are useful; (iii) how should simulations and observations be compared; and (iv) when in the modelling cycle are patterns used? We are convinced that the given examples and classifications are useful for readers from other disciplines and encourage both ecologists and other scientists to recognise patterns in their own systems and to use them effectively
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