From theory to practice in pattern‐oriented modelling: identifying and using empirical patterns in predictive models

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. Patternoriented 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.

A review of bioenergetic modelling for marine mammal populations

“…‘Traditional’ bioenergetic models ( sensu Nisbet et al, 2012 ) describe energy acquisition from feeding and its partitioning among maintenance, activity, growth, reproduction and excretion; the advantage being that these processes have a clear empirical interpretation, which facilitates measuring them using explicit units, but the resulting models are often parameter-rich and hard to generalize across species. These models generally follow a hierarchical allocation, as proposed by Sibly et al (2013) and extended by Beltran et al (2017) and Gallagher et al (2021a) , whereby an individual expends assimilated energy in order of the importance of the processes to survival, that is, for maintenance, thermoregulation, locomotion, growth, reproduction and energy storage (up to an optimal amount of reserves). In contrast, Dynamic Energy Budget (DEB) theory ( Kooijman, 2010 ) considers these same processes from a formal and more general perspective, using fundamental principles of mass–energy balance to relate sub-organismal (biochemical, genetic and physiological) processes to organismal performance ( Martin et al, 2012 ; Nisbet et al, 2012 ; van der Meer, 2006 ).…”

Energy‐mediated responses to changing prey size and distribution in marine top predator movements and population dynamics

“…Figure 3 in Horn et al., 2016). The benefit of using a HMM approach lies in the ability to communicate and extract statistics from movement behaviour of modelled animals in a way that enables direct comparison with empirical movement analyses, using, for example, pattern‐oriented modelling (Gallagher, Chudzinska, et al., 2021).…”

“…Here we apply an ABM of harbour porpoise energetics and movements to theoretical scenarios of two potential responses to climate change in marine ecosystems: altered length and spatial aggregation of prey resources. The ABM has been rigorously developed and tested using empirical data for this species (Gallagher, Chudzinska, et al., 2021; Nabe‐Nielsen et al., 2018). By implementing the model in theoretical, yet realistic, seascapes, we control for tested impacts and assess the two drivers in tandem to investigate isolated and interacting effects.…”

abmR: an R package for agent-based model analysis of large-scale movements across taxa

“…Over the last decades, spatially explicit simulations, pattern-oriented modelling, approximate Bayesian computing, and agent-based models have become more popular in ecological and evolutionary studies (Railsback et al, 2006; DeAngelis & Grimm, 2014; van der Vaart et al 2015; Gallagher et al 2021). Analytical platforms, such as InSTREAM, a simulation model approach designed to understand how stream and river salmonid populations respond to habitat alteration (Railsback et al, 2009), or ALMaSS, a predictive modeling tool for answering environmental policy questions regarding the effect of changing landscape structure on threatened animal species (Topping et al, 2003), allow investigation of specific ecological systems using ABM.…”