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1. Background



It is fundamental to marine environmental management that countries have the capability and capacity to measure and monitor the condition and trend of ecosystems in their marine jurisdictions.  Undertaking integrated assessments can be expensive and time consuming, but sound information is critical to understand the state of the marine environment (SOME) to underpin decision-making and achieve or maintain ocean health. Most importantly, such large-scale and integrated assessments must not be overly influenced by information that is limited only to places or issues that are well studied, since this might result in outcomes that are not balanced or properly represent conditions across the whole of a region.

The SOME assessments are a critical data source used by the UN World Ocean Assessment (www.worldoceanassessment.org), discussed further below, which has conducted a series of workshops over the last 2 years to identify relevant assessments, regional experts and capacity gaps.  At the workshops for the SE Asian Seas (Sanya City, China), the Caribbean (Miami, USA), Western Indian Ocean (Maputo, Mozambique), the South Atlantic (Abidjan, Côte d’Ivoire) and recently the Eastern Indian Ocean (Chennai, India), experts from developing countries have articulated that the capability to undertake SOME reporting is a major gap.  There are experts available with knowledge of their marine environments, but there is a lack of SOME reporting skills among the government agencies responsible for this task.

To meet this demand, capacity-building pilot workshops have been held in Bangkok Thailand (Sept., 2012; Ward, 2012), Abidjan Côte d’Ivoire (Oct., 2013) and in Freetown Sierra Leone (Feb., 2014) which have been well attended and acknowledged as being very useful by stakeholders.   The methodology used is based on expert elicitation[1], essentially a scientific consensus methodology, which is a process that synthesises existing assessments, data and information in conjunction with the subjective judgment of experts across a broad base of evidence.  The method has been applied successfully in a range of situations, including the 2011 Australian SOME Report, and has the advantages that it is cost- and time-effective, it utilizes the existing knowledge of marine experts from the target region and it can incorporate non-conventional knowledge and information. 

In the absence of useful regional or national indicator datasets, the State of Marine Environment Expert Elicitation (SOME-EE) process uses consultation with national and regional experts to gauge expert opinion about the condition of the marine and coastal ecosystems and dependent socio-economic sectors.  There are commonly datasets from local areas, and there are many sub-regional scale studies and short-term datasets about various aspects of marine ecosystems, but these have often a too coarse resolution and are not part of a systematic collection of data and knowledge routinely synthesised for reporting purposes.  The SOME-EE process draws upon these disparate datasets and the knowledge-base dispersed across a range of sources and institutions to capture a representative sample of existing expert knowledge about the condition of the environment in a manner that can be used for reporting purposes.

This paper is based upon the Australian SOME process carried out in 2011 and we acknowledge the authors of that report for a significant portion of the content presented below (Department of Environment, 2011; Ward et al., in press). This paper provides background and guidelines for experts who are intending to participate in a SOME-EE workshop; it explains the underlying concepts and the approach that will be adopted during the workshop so that experts can be prepared to fully participate at an optimum level of engagement.  It is also intended to provide background information for individuals or agencies interested in learning more about the SOME-EE process and its advantages and disadvantages.



[1] Expert elicitation is the synthesis of opinions of experts on a subject where there is uncertainty due to insufficient data.