Systematic evaluation of a spatially explicit ecosystem model to inform area-based management in the deep-sea

Abstract

The long-term provision of ecosystem goods and services depends on the operationalisation of ecosystem-based management approaches that ensure effective conservation and sustainable use of marine resources. This management challenge is addressed internationally through two United Nations instruments: Sustainable Development Goal (SDG) 14 - Life Below Water, and the Convention on the Law of the Sea (UNCLOS) for the Conservation and Sustainable Use of Marine Biological Diversity in Areas Beyond National Jurisdiction (BBNJ Agreement). To achieve sustainability and conservation goals as described in SDG 14 and the BBNJ Agreement, a combination of management tools, including area-based management tools (ABMTs), is necessary. Spatially explicit ecosystem models can inform policy frameworks by enabling ecosystem-wide assessments of ABMTs with indicators that track their management performance. However, the operational use of these complex models depends on the confidence and uncertainty of their predictions. Here, we present a framework to systematically evaluate the performance of a spatially explicit ecosystem model for deep-sea and open-ocean environments, using the ecosystem model for the EEZ of the Azores (NE Atlantic, Portugal) as a case study. The systematic approach aimed to determine the model's suitability as a tool to inform area-based management in the deep-sea. The framework was applied to Ecospace, the spatial-temporal module of the ecological modelling suite Ecopath with Ecosim. It consisted of a stepwise approach for model development and assessment through key parameterisation steps. The steps served for calibration of model parameter values and formal evaluation of temporal and spatial results against the best available reference data. Overall, this approach proved useful in identifying key model sensitivities and sources of uncertainty that arise when considering spatial variability in trophodynamics in the ecosystem model. Moreover, we concluded the model i) effectively predicted the observed inter-annual variability of benthic fish stocks in response to fisheries, trophic interactions, and environmental factors and ii) showed good and moderate spatial goodness-of-fit in replicating reference spatial distribution patterns of stocks and fishing activities. Despite its strengths, the spatial model has limitations related to uncertainties in model parameterisation and the spatial variability of trophodynamics. The systematic assessments presented in this study provide a framework for future model applications to predict the ecosystem-wide impacts of alternative spatial management measures in the deep-sea.