Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology

Abstract

Modern ecology recognizes that modelling systems across scales and at multiple levels-especially to link population and ecosystem dynamics to individual adaptive behaviour-is essential for making the science predictive. 'Pattern-oriented modelling' (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions.

Spatiotemporal variation in flow-dependent recruitment of long-lived riverine fish: Model development and evaluation

“…We initially parameterized the model with literature-derived parameter values (Appendix B (Supplementary material)) and then calibrated the submodel and full model with historical field data of the LPR population of shovelnose sturgeon Schwarz et al, 2006;Swigle, 2003) using the pattern-oriented modeling (POM) approach (Grimm and Railsback, 2012). We simultaneously used the following four patterns from field observations; (a) length-at-age, (b) mass-at-age, (c) spatial distribution, and (d) age 3+ population size as model calibration filters.…”

“…To evaluate model applicability in projecting shovelnose sturgeon populations in the field, we evaluated the model (primary and secondary) predictions (Bart, 1995) using the POM approach (Grimm and Railsback, 2012) (2015) and Rugg, (2013) for details of field sampling and sample processing). We evaluated these patterns by comparing annual means of the 2009-2011 model outputs from the 17-year simulation experiments (above) with the field data using rootmean-square error (RMSE); we calculated RMSE values for each replicated simulation run (n = 30) and then used mean values of these replicates for model evaluation.…”

Design and Analysis of Ranavirus Studies: Surveillance and Assessing Risk

“…Multiple possible forms of an IBM are created, representing different hypotheses about host-pathogen interactions. The different IBMs are evaluated based on their ability to recreate the salient patterns Grimm and Railsback 2012). When a model is able to match multiple patterns, it is more likely to be structurally realistic (Wiegand et al 2003), and capable of producing testable predictions.…”

Predictive ecology: systems approaches

“…POM concerns itself with identifying a set of patterns that can be observed at different scales and levels and are associated with a real-life problem of interest. Models from which the same patterns emerge are likely to contain the right mechanisms to describe the problem [48].…”

“…In order to help with the endeavour of creating a modern systems ecology, there are techniques and approaches that already exist in other fields and which can be applied to ecological systems [46]. If we wish to consider the creation of an ecological parallel to bioinformatics, then we will need to create new tools and approaches to allow us to cope with the fact that ecology has different constraints and advantages to molecular systems biology [48]. Further developing systems ecology to a point where it can make useful predictions about the ecological impact of environmental change, equivalent to those regularly made by climatologists using GCMs about the future state of the climate, will be demanding.…”