Designing MPA Networks in Indonesia

The planning area spans 1,823 km from east to west (and 1,607 km from north to south), and encompasses an area of 1,613,457 km2 (161,345,748 hectares) which is 49.5% of Indonesia’s marine waters (3, 257, 483 km2: Table 5). The planning area includes both shallow (<200m) and deep water (>200m) areas, including a large proportion of the shallow water habitats in Indonesia (34.9% of coral reefs, 22.7% of mangroves, and 67.3% of seagrasses).

We divided up the planning area into stratification units that represent the range of environmental, geographic and political (provinces) variation in the planning area. We stratified the planning area for the analysis in two ways: 1. By province, so the results can used to refine or develop MPA network designs for each of the six provinces that intersect with FMA715, and 2. By ecoregion to ensure that the design criteria of habitat representation and replication will be applied to each of the shallow water habitats (coral reefs, mangroves and seagrasses) that occur in each ecoregion.

We conducted the Marxan analysis in two steps, because we had better quality data (higher resolution with more validation) for shallow water (<200 m) than deepwater (> 200m) habitats. First, we ran Marxan for the shallow water analysis only. Then we locked in the best solution from the shallow water analysis, and ran the shallow and deepwater analysis combined. This ensured that higher priority was given to using the better quality data in the analysis. It also prioritized protecting shallow water habitats and critical, special and unique areas, while considering connectivity between shallow and deepwater habitats. To do this, we used different data layers and planning units for each analysis. For the shallow water analysis, we used: data layers for MPA status (existing and proposed MPAs in Marine Spatial Plans), conservation features (shallow water habitats and critical, special and unique areas), and other uses; and smaller planning units for a finer scale analysis. For the combined shallow and deepwater analysis, we used: data layers for the result (best solution) from the shallow water analysis, conservation features (deepwater habitats) and other uses, and larger planning units for a coarser scale analysis.

We explored several scenarios that used different data layers, planning areas, cost surfaces, locked in and locked out areas, and/or other parameters for targets and costs. We selected a scenario that included using: All of the data layers, targets for conservation features, and locked in and locked out features, and cost surfaces. We ran Marxan 100 times, which produced 100 possible solutions for the MPA network design. We set up Marxan to develop solutions that generate a compact network (with more clumping i.e., less, larger areas) and avoid fragmentation (i.e., many small areas). Marxan provided several outputs (for both the shallow water only and the combined shallow and deepwater analyses) that we used to design the MPA network including: 100 individual solutions, where each solution identifies areas that efficiently met the targets for the conservation features while minimizing costs, the sum solution, which shows how often each planning unit was selected in the 100 solutions (i.e., areas that were always, often, rarely or never selected). The best solution, which identifies areas that most efficiently meet the targets for minimal cost. This is not necessarily the ideal solution, rather it is a very good solution based on the information available. We used the best solution for the shallow water analysis only, and locked it in for the combined shallow and deepwater analysis; and we used the sum solution for the combined shallow and deepwater analysis to identify priority areas for including in the MPA network (areas selected in 70-100 solutions).