Resumen
In their 2023 book, ?The Blue Compendium: From Knowledge to Action for a Sustainable Ocean Economy?, Lubchenko and Haugan invoked alternate stable (AS) states marginally as an undesired consequence of sources of disturbance on populations, communities and ecosystems. They did not provide detailed arguments, but considered the existence of AS states as a given. Conversely, May, in his 1977 Nature article, pointed out that, when applied to systems that are complex, ?the [AS states] theory remains largely metaphorical?. This is the starting point of this critical review, which aims to re-examine the general theory behind AS states in ecological systems and its applications to marine ecology and conservation. The focus is first on theory, taking as examples communities that sustain competition and studying the relative importance of the fluxes of individuals between simple low-dimension, interconnected systems. We find that a minimal formulation of fluxes is sufficient to obtain a set of non-null multiple stable (MS) states and to trigger shifts between AS states when fluxes become large enough. This provides new insights into the theory of rescue and mass effects by distinguishing them through a threshold at which the system dynamics shift from one stable equilibrium to another. Then, we consider how the theoretical framework of AS states has been applied in marine environments. It appears that many applications have confounded shifts between AS states and changes in the structure of systems, particularly when the complexity of the systems increases. The main difficulty for any application remains that the concepts of MS and AS states can only be established and validated for low-dimension systems and simplified experiments. This is because the mathematical properties of models that describe large-dimension, complex systems deviate from the observed characteristics of their real-world counterparts. There are many intriguing scientific challenges around the plausible shifts between AS states, but a deeper understanding and characterization of their occurrence in nature would require a significant investment in modeling to formulate predictive ecosystem models.