Towards more reliable interdisciplinary modelling of global human–environment dynamics driving biodiversity change

Abstract Environmental problems are typically vast, urgent and complex. Confronted with such problems, we are often tempted to act fast by pulling together little bits and pieces from different fields and simply adding these to pre‐existing models and frameworks. Seldom, though, do we pause long enough to look whether and for how long those larger structures we build can support reliable answers to our questions. In this Perspective, I critically discuss the current state of broad‐scale, interdisciplinary coupled modelling of human–environment relationships, with a focus on the classical model virtues of precision, generality and realism. I focus on models used to address land‐use‐driven biodiversity change in the context of broader global change and sustainability challenges, for which I draw examples mainly from agroeconomic land‐use models and integrated assessment models—popular coupled modelling frameworks that are increasingly coupled further with ecological models. Specifically, I discuss (i) how limitations in our models' training data and underpinning theories translate into excessively uncertain predictions, (ii) how coupling even highly general sub‐models can lead to hardly generalizable representations of indirect human–environment relationships and (iii) how representing ever more processes decreases rather than increases realism due to greater average measurement bias, a problem further exacerbated as we add processes based on their relevance for our own systems of interest, rather than for the real‐world systems' dynamics. Together, these limitations can render models unfit for some of the purposes for which they are applied. I also explore barriers to advancing scientific modelling virtues amid other, non‐scientific motivations for interdisciplinary modelling (e.g. cultural, economic, normative). Finally, I offer suggestions to modellers and other actors in science, science administration and science policy to help promote a transition to more reliable interdisciplinary coupled models that can remain powerful for addressing major sustainability challenges far beyond the next iteration of science‐policy assessments. Read the free Plain Language Summary for this article on the Journal blog.