Zoning for aquaculture and biodiversity

Reconciliation of competing human uses lay at the centre of marine spatial planning challenges in the Adriatic Sea. Therefore, we were primarily interested in understanding how priorities for biodiversity and aquaculture changed depending on the treatment of human uses and industries used in the prioritization analysis. We develop and evaluate six alternative planning scenarios for the region. We used Marxan and Marxan with Zones to meet predetermined targets for conservation features whilst minimizing impacts (also termed “costs”) to industries. We used different combinations of objectives, targets, Marxan softwares, and costs to develop six planning scenarios (Table 1). To reduce the spatial scattering and increase the compactness of the outputs in Marxan and Marxan with Zones scenarios we apply the boundary length between respectively planning units and zones.

For the single-objective scenarios important conservation features were assigned a 30% target in accordance with the world's oceans target established at the IUCN World Conservation Congress in Hawaii (2016). We also set 10% targets for broad ranging species distributions in line with Aichi Target 11 under the Convention on Biological Diversity (2010). For the first four scenarios, we kept these targets consistent while we varied the way in which the cost of a planning unit was calculated. The costs for the four scenarios were: S1) area of the planning unit - a baseline approach to set equal costs across the planning region so that priorities are solely determined by the conservation features, S2) aquaculture profitability based on the distance to port function, S3) human uses based on the number of maritime activities found in a planning unit and S4) the aquaculture suitability surface.

For the multi-objective scenarios, we developed two additional multi-objective prioritization scenarios (S5 and S6) aimed at simultaneously meeting targets for biodiversity and aquaculture whilst minimizing costs to maritime industries. Marxan with Zones allows for more sophisticated planning problems than standard Marxan, allowing users to make specific allocations of features, targets and costs to different more than just one kind of zone. We organized this analysis around three zones: Biodiversity (Zone 1) with the aim of conserving our biodiversity features; Aquaculture (Zone 2) with the aim of capturing suitable sites for aquaculture; and Multiple-Use (Zone 3) where any activity can take place, but which has no biodiversity or aquaculture targets set. We kept the biodiversity targets consistent with the single objective scenarios for the biodiversity zone, set targets for 40% for the Aquaculture Zone, applied to the profitability surface used in Scenario 5 (S5Z2) and the suitability surface used in Scenario 6 (S6Z2). We also applied two different treatments of the human use cost surfaces, using all human uses in the Biodiversity and Multiple-Use Zones and human uses deemed incompatible with aquaculture in the Aquaculture Zone.

We compare results from different scenarios according to how well they capture suitable aquaculture habitats and minimize impacts. We introduce a new evaluation method for scenario comparison in spatial optimization using a nearest-neighbour analysis for spatial pattern similarities. Lastly, we test the “value of information” provided by our investment in developing the fine-scale suitability surface to improve efficiencies.

We found that planning for both biodiversity and aquaculture simultaneously resulted in more efficient conservation zones placed in areas less likely to generate conflict with other human uses, including aquaculture. Additionally, higher quality suitable habitat was retained for aquaculture zoning compared to when biodiversity alone was prioritized in an effort to minimize conflict with industries. Thus, we advocate for the integration of multi-objective seascape zoning to improve efficiency and minimize conflicts when industry and conservation are both considered parts of blue economic growth strategies. Further, embedding multi-objective optimization and decision-support tools into blue economy frameworks will help planners and stakeholders better understand the balances between objectives that need to reconcile critical biodiversity protection, food production and economic growth.

An important part of our analysis was developing a fine-scale suitability surface for the target aquaculture species. It takes a long time and substantial resources to gather the environmental and biophysical characteristics of the region, as well as validating weightings with regional experts on the ecological and socio-economic parameters affecting suitability. Given the investment in time and resources required to generate this suitability surface (S6), we also wanted to retrospectively understand the value of that information to inform the optimization compared to the proxy distance surface computed in less than 1 hour in a Geographic Information System - the profitability surface (S5). Value of information theory frames the investments scientists make in data collection in terms of its ability to help managers make better decisions. In spatial planning, value of information can help us direct limited resources towards collected data where it will reduce key uncertainties in our knowledge of marine ecosystem processes, and subsequently, impact or change what emerges as priority areas. Our analysis can be applied in any context where multiple objectives occur for aquaculture sector growth and biodiversity conservation.