Keywords
ecosystem services; uncertainty assessment; valuation; multi-criteria
Start Date
25-6-2018 2:00 PM
End Date
25-6-2018 3:20 PM
Abstract
Spatial prioritization, or spatial targeting, involves determining where to conduct or incentivize different activities on a landscape, in order to achieve a desired combination of benefits (often formalized within an ecosystem services framework). It has a long history in conservation targeting for protected areas, but is significantly more complicated when multiple actions can be considered, to achieve many objectives. And while it is somewhat straightforward to construct a formal optimization problem, there is often a significant gap between formal problem specification and results that are useful in a particular policy or decision making context, especially in light of parametric, structural, and scenario uncertainties.
This talk will discuss recent advances, experiences, and tools used to address these practical complications. We present an R package (“uncertitude”) that can be used in concert with multiple spatial prioritization tools such as prioritizr in R and ROOT in python. We discuss dynamic and static decision-aiding visualizations and the computational workflows necessary to create them, including explorations of uncertainties. These are applied in multiple locations including the Tana Basin of Kenya, and the Tahoe-Truckee Watershed in the Sierra Nevada, and the ongoing work in the Central Valley of California.
Progress on integrating and communicating uncertainties in robust spatial targeting for multiple ecosystem services provision
Spatial prioritization, or spatial targeting, involves determining where to conduct or incentivize different activities on a landscape, in order to achieve a desired combination of benefits (often formalized within an ecosystem services framework). It has a long history in conservation targeting for protected areas, but is significantly more complicated when multiple actions can be considered, to achieve many objectives. And while it is somewhat straightforward to construct a formal optimization problem, there is often a significant gap between formal problem specification and results that are useful in a particular policy or decision making context, especially in light of parametric, structural, and scenario uncertainties.
This talk will discuss recent advances, experiences, and tools used to address these practical complications. We present an R package (“uncertitude”) that can be used in concert with multiple spatial prioritization tools such as prioritizr in R and ROOT in python. We discuss dynamic and static decision-aiding visualizations and the computational workflows necessary to create them, including explorations of uncertainties. These are applied in multiple locations including the Tana Basin of Kenya, and the Tahoe-Truckee Watershed in the Sierra Nevada, and the ongoing work in the Central Valley of California.
Stream and Session
In approximate order of suitability:
C6, E3, C8, C9