optimLanduse: A package for multiobjective land-cover composition optimization under uncertainty

How to simultaneously combat biodiversity loss and maintain ecosystem functioning while improving human welfare remains an open question. Optimization approaches have proven helpful in revealing the trade-offs between multiple functions and goals provided by land-cover configurations. The R package optimLanduse provides tools for easy and systematic applications of the robust multiobjective land-cover composition optimization approach of Knoke et al. (2016). The package includes tools to determine the land-cover composition that best balances the multiple functions a landscape can provide, and tools for understanding and visualizing the reasoning behind these compromises. A tutorial based on a published dataset guides users through the application and highlights possible use-cases. Illustrating the consequences of alternative ecosystem functions on the theoretically optimal landscape composition provides easily interpretable information for landscape modelling and decision-making. The package opens the approach of Knoke et al. (2016) to the community of landscape modellers and planners and provides opportunities for straightforward systematic or batch applications.

Automated summary

How to simultaneously address biodiversity loss, maintain ecosystem functioning, and improve human welfare remains a puzzle. Optimization approaches have proved useful in uncovering trade-offs between multiple functions and goals provided by land-cover configurations. The R package optimLanduse offers user-friendly tools for applying the robust multiobjective land-cover composition optimization approach developed by Knoke et al. (2016).