Building clone-consistent ecosystem models

Abstract

Non-mechanistic ecosystem models are employed in many ecological studies ranging from purely theoretical to data-driven ones. With such models in mind, we derive fundamental consistency criteria (axioms) from rst principles. ese particularly cover what we call clone consistency: e outcome does not change if a population is split into two with identical properties. We mathematically prove that, these axioms are ful lled if and only if the model is based on linear combinations of powers of parameters and abundances. Using this insight, we formulate a framework that allows to quickly assess the consistency of existing models and to build new models. We demonstrate our approach by invalidating a data-based model proposed for polymicrobial urinarytract infections and developing an alternative. We argue that our framework reveals implicit assumptions and informs the general modelling studies by narrowing the space of possible models or pointing to new forms of models.