Abstract: A fundamental understanding of the biology of a species is both intrinsically important and imperative to maintaining healthy populations under harvesting pressure. Recent growth in marine invertebrate fisheries across the world is accompanied by recognition of the myriad uncertainties in our understanding of their population biology and a need to support fishery health by addressing those knowledge gaps. Spot prawns (Pandalus platyceros) live in deep water on the seabed off the Pacific coast of Canada and the US and begin their lives as males before transitioning to females for the final year of their life – they are sequential hermaphrodites. To investigate the consequences of life history and fishery dynamics on the population biology of spot prawns, we developed and analysed a discrete-time stage-structured two sex population model. We developed two versions of the model – a relatively simple annual model that is analytically tractable and a detailed model capturing seasonal dynamics within the year that facilitates asking specific questions through simulation. Analysis of the first model revealed three equilibria with an Allee threshold introduced by a mating function whereby female fertilization depends on the presence of sufficient males. We find that the Allee effect is only biologically relevant in scenarios where the male population is extremely low (e.g., recolonization of extirpated habitat). We show that the detailed seasonal version of the model can be rolled up such that it is exactly equivalent to the annual model. The connection between models allows us to investigate the emergent dynamics at an annual scale of varying life history assumptions and fishery scenarios at a seasonal scale. We find that the consequences of making alternative life history assumptions (e.g., whether females are itero- or semelparous) depend on the management of the fishery (e.g., length of fishery, selectivity). For a relatively young fishery and for a species whose life history is fairly uncertain, our modelling approach provides a useful, flexible framework for testing the population consequences of a broad range of assumptions & scenarios.
