Through this review of the literature, the authors developed a list of inputs that are likely to contribute to successful MPA outcomes and incorporated these into a framework (Table 1). The proposed framework consists of a series of questions that correspond with indicators for governance, management and local development inputs. The potential utility of the inputs framework is threefold. First, it might provide governors and managers with a list of best practices or recommendations to lay the groundwork for creating more successful MPAs. Governors and managers could refer to the framework during the design www.selleckchem.com/products/Dasatinib.html and implementation

phases of individual sites or entire systems of MPAs. Second, it could serve as a monitoring and evaluation tool for examining whether, and to what extent, the recommended inputs require attention in individual sites or in entire systems of MPAs. Using either a semi-structured interview questionnaire, a series of triangulated qualitative interviews, or focus-group discussions with stakeholders representing different groups (e.g., government, natural and social scientists, NGOs,

community representatives, fishers), each indicator in the framework might be explored in a qualitative manner or assigned a quantitative value. For a quantitative Dinaciclib ic50 approach, the authors suggest using a similar rating method to that used by Timko and Satterfield [219]. Indicators might be rated on a scale from 0 to 4, where 0=very unsatisfactory, 1=unsatisfactory, 2=neutral, 3=satisfactory, and 4=very satisfactory based on individual interviews with various stakeholder groups. Mean scores could be calculated for each indicator as well as for each group to show which factors needed to be addressed. One of the benefits of this approach is that it would allow for comparisons among different sites, among different systems of MPAs or among different stakeholder groups׳ perceptions on each indicator. Repeated quantitative application

of the framework would also allow changes to be easily tracked over time. Some indicators may not be applicable or not appropriate (n/a) in a particular context and could be excluded. Third, the framework might be used to advocate for improved MPA practice by taking a scorecard approach—for example, through Mirabegron calculating likelihood of success scores. An overall score for each category – i.e., governance, management, local development – for an MPA could be calculated using the formula below. equation(1) Categoryscore=SumofindicatorscoresforcategoryTotalpossiblescoreforcategory(numberofindicatorsused×4)×100 This formula will calculate a percentage (%) out of 100 for each category—which might be assigned values as follows: 0–25%=very unlikely to succeed; 25–50%=unlikely to succeed; 50–75%=likely to succeed; 75–100%=highly likely to succeed.