Evaluating biological realism in ecological modelling: application of a novel framework to compare mechanistic and process-based earthworm and wild pollinator population models

Abstract

Ecological models can support land management decisions and optimisation schemes that need to account for invertebrate population responses at the field to landscape level. However, models that incorporate greater biological detail (e.g. individual-level physiological and behavioural responses) often become computationally intractable at larger spatial extents. Such trade-offs in model development lead to ad hoc model design for different species and management questions, hindering generalisable insights needed to advance predictive ecological models for decision support. To facilitate model comparison, we developed and applied a novel approach to quantify the biological realism of models for two functionally important invertebrate groups commonly targeted by management interventions. Mechanistic and process-based population models for earthworms (n = 23) and wild pollinators (n = 24) were identified through a structured review. We find that earthworm models are predominantly non-spatial or micro-scale (<10 m extent) and often incorporate detailed physiological mechanisms. Pollinator models frequently simulate landscape-scale scenarios (≥1 km extent) and typically rely on aggregated processes to predict population dynamics or crop visitation rates, although some include detailed individual-level movement behaviours. Species- and scale-specific model structures highlight the need for greater integration of physiological and behavioural mechanisms across broader spatial extents. We recommend systematic strategies to build on the progress made by existing models, aiming to resolve the trade-off between realism and tractability for more informed population predictions at management-relevant spatial scales. Our framework complements existing efforts towards greater transparency in model development, communication, and application for robust environmental decision support.