On which targets should we compromise in conservation prioritization problems?

Abstract

Systematic conservation planning is an essential part of biodiversity preservation. In the context of conservation prioritization problems, the total cost of the entire reserve system is highly dependent on how big we set targets (e.g. 10% or 30%) for conservation features (e.g. species or habitats). Thus, it is of interest to conservation planners how targets could be adjusted in a reasonable way, in order to decrease total cost. The aim of this paper is to rank features based on their influence on total cost. Focusing on the minimum set coverage problem—an integer linear optimization problem (ILP)—we developed a method to rank features according to their influence on total cost. Since the computation time is often too high to solve the ILP, we approximated its optimal solutions by the results of a linear optimization problem (LP). For the feature ranking, we used the shadow prices of the LP, validating it with rankings created by methods which used an ILP solver and the software Marxan, which uses a simulated annealing algorithm. The results show that shadow prices can be used to rank features in terms of their impact on the total cost of the reserve system thereby identifying those feature targets that could yield better compromises. Thus, the shadow price ranking provides a novel tool for conservation planners to help set feature targets in conservation prioritization problems. Furthermore, the results of the study can be used to improve Marxan.

Revisiting the minimum set cover, the maximal coverage problems and a maximum benefit area selection problem to make climate‐change‐concerned conservation plans effective

“…These results follow‐up analyses made by Moilanen and Arponen (2011) using Zonation software (Moilanen, Kujala, et al, 2009) in which the overlapping patterns of species' ranges, combined with the costs to protect sites, emerged as the key factors that make target setting designs complex and idiosyncratic. More recently, Kaim, Watts, and Possingham (2017) used a shadows‐pricing technique to fully optimize target setting in Marxan. We extended assessments on the effect of target setting to three PA selection problems that integrate climate change effects in a multi‐period timeline.…”

Managing risk and uncertainty in systematic conservation planning with insufficient information

“…Discrete conservation prioritisation problems, such as identifying locations for protected areas, are commonplace (Margules & Pressey, 2000). Yet, conservation planning is now increasingly focused on more general problems that aim to identify where to invest resources in different actions, such as management activities or incentives, to achieve conservation outcomes (Crowe & Parker, 2008; Kaim et al, 2017; Marinoni et al, 2011). In these cases the true decision variables (i.e.…”