Predicting marine species distribution and identifying priority areas for conservation – comparing and coupling food web and Bayesian hierarchical modelling approaches

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Marine ecosystems are impacted by numerous anthropogenic stressors, with overexploitation of resources being among the central threats. A prominent tool to mitigate anthropoogenic pressures and to protect marine biodiversity and ecosystem functioning is the implementation of marine protected areas. For a successful identification and prioritization of suitable conservation areas it is crucial to understand the geographic distribution of biodiversity, target species and essential habitats. A wide variety of statistical modelling approaches for understanding and predicting species distribution have been developed along with different conservation planning algorithms. However, the resulting predictions are always associated with varying degrees of uncertainty; and although the knowledge on the level of uncertainty facilitates decision-making in the face of high risks involved and allows for the adaption of management plans, uncertainty is rarely accounted for in conservation planning tools. Furthermore, most correlative species distribution models and reserve selection algorithms only account for associated environmental factors, while ecological processes and human activities can also drive species distribution. A crucial step towards better predicting the distribution of species and towards an enhanced identification of conservation areas, therefore, encompasses the development of approaches that explicitly account for ecological processes and human activities and that quantify uncertainties associated with resulting conservation plans. Bayesian hierarchical species distribution (B-HSD) modelling is a novel technique that allows the incorporation of spatial random-effect terms, spatial correlation of the variables and the uncertainty of the parameters in the modelling process, resulting in a more realistic and accurate estimation of uncertainty. Ecological processes and human activities can be explicitly considered in mechanistic modelling such as the Ecospace habitat capacity (E-HFC) model. E-HFC allows to spatially drive foraging capacity of species from cumulative physical, oceanographic and environmental effects in conjunction with food web dynamics and fisheries impacts. Furthermore, the ‘objective function’ in Ecospace allows for the identification of effective conservation areas. However, as most ecosystem models and conservation planning tools the E-HFC model does not incorporate the uncertainty related to species distribution. The objective of the project is, thus, to explore the complementarity and applicability of B-HSD models and the E-HFC model for the prediction of species distribution and the identification of priority areas for conservation. For this purpose I use the tropical bay system Chwaka Bay (Tanzania) and its recently developed Ecopath model as case study. With this I attempt to further develop the E-HFC model applicability and to contribute to the development of hybrid approaches for the prediction of species distribution.