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Using theory-driven Integrated Population Models to evaluate competitive outcomes in stage-structured systems

Predicting competitive outcomes typically requires fitting dynamical models to data, from which interaction strengths and coexistence indicators such as invasion criteria can be produced. Methods that allow to propagate parameter uncertainty are particularly indicated. These should ideally allow for competition between and within species at various life-stages, and make the best out of multiple data sources, each of which can be relatively scarce by statistical standards. Here, we embed a mathematical model of stage-structured competition between two species, producing analytical invasion criteria, into a two-species Integrated Population Model. The community-level IPM allows to combine counts, capture-recapture, and fecundity data into a single statistical framework, and the Bayesian formulation of the IPM fully propagates parameter uncertainty into invasion criteria. Model fitting demonstrates that we can correctly predict coexistence through reciprocal invasion when present, but that interaction strengths are not always estimable, depending on the prior chosen. Our competitive exclusion scenario is shown to be harder to identify, although our model allows to at least flag this scenario as uncertain rather than mistakenly present it as coexistence. Our results confirm the importance of accounting for uncertainty in the prediction of competitive outcomes.

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