Its widely recognised successful fisheries management requires understanding the human drivers that determine how fishers, individually and collectively, respond to changing fishing opportunities. Decisions about where and when to fish have a fundamental impact on the level and pattern of exploitation of different fish stocks and on the economic success of fishers. There has, however, been limited progress in integrating such considerations into operational management decision making tools thus increasing uncertainty in evaluation of management strategies. Improved understanding and prediction of effort allocation in mixed fisheries is vital if we are to design better management approaches. We compare different methods of predicting spatiotemporal allocation of fishing effort in mixed fisheries, with a focus on clarifying the methodological similarities and differences. We distinguish statistical models (e.g. Random Utility Modelling, Markov and Semi-Markov models) from mechanistic models (e.g. gravity models, dynamic state modelling) and review their application. We highlight the key properties with some simplified examples to draw inference on their suitability to predict effort allocation under plausible management interventions (seasonal closures, gear regulations, catch restrictions). We find many similarities in formulation and structure to the models. Equally distinction between statistical and mechanistic methods may lead to the use of one or the other depending on the intended role. We consider the characteristics of the different models in application to Management Strategy Evaluation (MSE) routines, to understand how to further promote their incorporation in fisheries management advice.