restoptr: an R package for ecological restoration planning

Ecological restoration is essential to curb the decline of biodiversity and ecosystems worldwide. Since the resources available for restoration are limited, restoration efforts must be cost-effective to achieve conservation outcomes. Although decision support tools are available to aid in the design of protected areas, little progress has been made to provide such tools for restoration efforts. Here, we introduce the restoptr R package, a decision support tool designed to identify priority areas for ecological res-toration. It uses constraint programming-an artificial intelligence technique-to identify optimal plans given ecological and socioeconomic constraints. Critically, it can identify strategic locations to enhance connectivity and reduce fragmentation across a broader landscape using complex landscape metrics. We illustrate its usage with a case study in New Caledonia. By applying this tool, we identified priority areas for restoration that could reverse forest fragmentation induced by mining activ-ities in a specific area. We also found that relatively small investments could deliver large returns to restore connectivity. The restoptr R package is a free and open-source decision support tool available on the Comprehensive R Archive Network (https:// cran.r-project.org/package=restoptr).

Automated summary

Ecological restoration is crucial for addressing the decline of biodiversity and ecosystems worldwide. However, resources for restoration are limited, highlighting the need for cost-effective efforts. The restoptr R package is introduced as a decision support tool that utilizes constraint programming to identify optimal areas for ecological restoration. By applying this tool in a case study in New Caledonia, priority areas for restoration were identified and the potential for connectivity restoration was demonstrated with relatively small investments. The restoptr R package is an open-source tool for ecological restoration available on the Comprehensive R Archive Network.