A conceptual framework for scaling up emergent predictions from mechanistic individual-based models

Abstract

Environmental decision-makers need robust, landscape-level predictions of population responses to inform management decisions before implementation. Mechanistic individual-based models (IBMs) can capture how individual behaviour and interactions in heterogeneous environments generate emergent population dynamics, but high computational costs typically restrict applications to small spatial extents. To address this limitation, we synthesise spatial modelling strategies across subfields of ecology and introduce the Spatial Threshold of Emergent Behaviour Stabilisation (STEBS) framework. STEBS capitalises on the biological realism of mechanistic IBMs through an in silico modelling experiment to quantify the Critical Emergence Threshold (CET) — the smallest spatial extent at which emergent system behaviour stabilises. The CET provides a biologically meaningful and computationally efficient scale for developing meta-models that relate environmental variables to emergent population patterns, which can then be extrapolated across unsimulated regions to predict landscape-level dynamics. This approach enables tractable scaling up of mechanistic IBMs, while retaining their biological realism. STEBS therefore offers a systematic pathway for applying IBMs to real-world environmental challenges, enhancing the evidence base for policy and management under accelerating global change. Future development of STEBS into an operational and transferable tool will require empirical validation across diverse species, landscapes and IBM structures, alongside evaluation of whether the upfront investment required to estimate CETs improves predictive efficiency compared to brute-force scaling approaches.