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Abstract Statistical models use observations of animals to make inferences about the abundance and distribution of species. However, the spatial distribution of animals is a complex function of many factors, including landscape and environmental features, and intra‐ and interspecific interactions. Modelling approaches often have to make significant simplifying assumptions about these factors, which can result in poor model performance and inaccurate predictions. Here, we explore the implications of complex spatial structure for modelling the abundance of the Serengeti wildebeest, a gregarious migratory species. The social behaviour of wildebeest leads to a highly aggregated distribution, and we examine the consequences of omitting this spatial complexity when modelling species abundance. To account for this distribution, we introduce a multi‐latent framework that uses two random fields to capture the clustered distribution of wildebeest. Our results show that simplifying assumptions that are often made in spatial models can dramatically impair performance. However, by allowing for mixtures of spatial models accurate predictions can be made. Furthermore, there can be a non‐monotonic relationship between model complexity and model performance; complex, flexible models that rely on unfounded assumptions can potentially make highly inaccurate predictions, whereas simpler more traditional approaches involve fewer assumptions and are less sensitive to these issues. We demonstrate how to develop flexible spatial models that can accommodate the complex processes driving animal distributions. Our findings highlight the importance of robust model checking protocols, and we illustrate how realistic assumptions can be incorporated into models using random fields.