Options
optimLanduse: A package for multiobjective land‐cover composition optimization under uncertainty
ISSN
2041-210X
Date Issued
2022
Author(s)
DOI
10.1111/2041-210X.14000
Abstract
1. 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 optim-Landuse provides tools for easy and systematic applications of the robust multiobjective land-cover composition optimization approach of Knoke et al. (2016).
2. The package includes tools to determine the land-cover composition that bestbalances 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.
3. Illustrating the consequences of alternative ecosystem functions on the theoretically optimal landscape composition provides easily interpretable information for landscape modelling and decision-making.
4. 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.
2. The package includes tools to determine the land-cover composition that bestbalances 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.
3. Illustrating the consequences of alternative ecosystem functions on the theoretically optimal landscape composition provides easily interpretable information for landscape modelling and decision-making.
4. 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.