An accessible optimisation method for barrier removal planning in stream networks

Abstract Barriers associated to human infrastructure are a widespread impact in freshwater ecosystems worldwide, disrupting connectivity along river networks and key processes. Restoration of connectivity has risen in the last decade, with thousands of dams, weirs and culverts removed. Spatial optimisation methods can help inform decision on what barriers to remove to maximise gain in connectivity under limited budgets. However, current optimisation approaches rely on programming skills that are not easily accessible to stakeholders, which restrict the use of these methods. We demonstrate how Marxan, a publicly available tool, can be used to prioritise the allocation of barrier removal projects. We mapped the distribution of >900 barriers in the Tagus River (Iberian Peninsula) and 29 freshwater fish species with different movement abilities and needs. We assessed the passability of each barrier by all species and relative removal cost. We then identified priority barriers for removal to increase connectivity of populations of all species simultaneously. We tested two alternative scenarios: i) locking out barriers assesses as non-removable for their high strategic value or removal cost and ii) making all barriers available for removal. We found that connectivity recovery targets could be achieved by removing a small proportion of barriers, and avoiding large infrastructure. However, for some species, large recovery targets could only be achieved by removing some of these large infrastructures at high increases in cost. We also found some spatial differences in the recovery value of particular barriers for improving upstream and downstream connectivity. Our study demonstrates how to use a robust optimisation approach in an accessible tool, to address the complexity of prioritisation exercises commonly faced by stakeholders when deciding where to invest in barrier removal projects. This will improve decision-making for river connectivity restoration through a transparent, reproducible, and better-informed approach than traditional opportunistic or ranking-based approaches.