Systematic Evaluation of a Spatially Explicit Ecosystem Model to Inform Area-Based Management in the Deep-Sea DOI
Joana Brito, Ambre Soszynski, Christopher K. Pham

et al.

Published: Jan. 1, 2023

The long-term provision of ecosystem goods and services depends on the operationalisation ecosystem-based management approaches that ensure effective conservation sustainable use marine resources. At international level, this challenge is addressed through two United Nations instruments - Sustainable Development Goal (SDG) 14: Life Below Water, Convention Law Sea (UNCLOS) Conservation Use Marine Biological Diversity in Areas Beyond National Jurisdiction (BBNJ). To achieve resource goals described SDG 14 BBNJ Convention, it necessary to a combination tools, including area-based tools (ABMTs). In context, spatially explicit models can inform policy frameworks by enabling ecosystem-wide assessments ABMTs with indicators track their performance. However, operational these complex confidence uncertainty predictions. Here, we present framework for systematically evaluate performance model open-ocean deep-sea environments Azores (NE Atlantic, Portugal), draw conclusions about suitability as tool deep-sea. was applied Ecospace, spatial-temporal module ecological modelling suite Ecopath Ecosim, consisted stepwise approach development assessment key parameterisation steps allow calibration parameter values formal temporal spatial results against best available reference data. Overall, proved useful identifying sensitivities sources arise when accounting variability trophodynamics model. addition, concluded able reproduce well patterns characterising dynamics biological human components ecosystem. i) successfully predicted observed interannual fish stocks deep sea response fisheries, trophic interactions environment, ii) showed good moderate goodness fit replicating distribution fishing activities derived from species vessel monitoring data, respectively. It noteworthy comes limitations related uncertainties systematic presented study provide future applications predict impacts alternative measures sea.

Language: Английский

Systematic Evaluation of a Spatially Explicit Ecosystem Model to Inform Area-Based Management in the Deep-Sea DOI
Joana Brito, Ambre Soszynski, Christopher K. Pham

et al.

Published: Jan. 1, 2023

The long-term provision of ecosystem goods and services depends on the operationalisation ecosystem-based management approaches that ensure effective conservation sustainable use marine resources. At international level, this challenge is addressed through two United Nations instruments - Sustainable Development Goal (SDG) 14: Life Below Water, Convention Law Sea (UNCLOS) Conservation Use Marine Biological Diversity in Areas Beyond National Jurisdiction (BBNJ). To achieve resource goals described SDG 14 BBNJ Convention, it necessary to a combination tools, including area-based tools (ABMTs). In context, spatially explicit models can inform policy frameworks by enabling ecosystem-wide assessments ABMTs with indicators track their performance. However, operational these complex confidence uncertainty predictions. Here, we present framework for systematically evaluate performance model open-ocean deep-sea environments Azores (NE Atlantic, Portugal), draw conclusions about suitability as tool deep-sea. was applied Ecospace, spatial-temporal module ecological modelling suite Ecopath Ecosim, consisted stepwise approach development assessment key parameterisation steps allow calibration parameter values formal temporal spatial results against best available reference data. Overall, proved useful identifying sensitivities sources arise when accounting variability trophodynamics model. addition, concluded able reproduce well patterns characterising dynamics biological human components ecosystem. i) successfully predicted observed interannual fish stocks deep sea response fisheries, trophic interactions environment, ii) showed good moderate goodness fit replicating distribution fishing activities derived from species vessel monitoring data, respectively. It noteworthy comes limitations related uncertainties systematic presented study provide future applications predict impacts alternative measures sea.

Language: Английский

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