Integer linear programming outperforms simulated annealing for solving conservation planning problems
Richard Schuster,Richard Schuster,Jeffrey O. Hanson,Matthew Strimas-Mackey,Joseph R. Bennett +4 more
TL;DR: Given the performance of ILP solvers, they can be used to generate conservation plans in real-time during stakeholder meetings and can facilitate rapid sensitivity analysis, and contribute to a more transparent, inclusive, and defensible decision-making process.
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Abstract: The resources available for conserving biodiversity are limited, and so protected areas need to be established in places that will achieve objectives for minimal cost. Two of the main algorithms for solving systematic conservation planning problems are Simulated Annealing (SA) and Integer linear programming (ILP). Using a case study in British Columbia, Canada, we compare the cost-effectiveness and processing times of SA versus ILP using both commercial and open-source algorithms. Plans for expanding protected area systems based on ILP algorithms were 12 to 30% cheaper than plans using SA. The best ILP solver we examined was on average 1071 times faster than the SA algorithm tested. The performance advantages of ILP solvers were also observed when we aimed for spatially compact solutions by including a boundary penalty. One practical advantage of using ILP over SA is that the analysis does not require calibration, saving even more time. Given the performance of ILP solvers, they can be used to generate conservation plans in real-time during stakeholder meetings and can facilitate rapid sensitivity analysis, and contribute to a more transparent, inclusive, and defensible decision-making process.
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Figures
Figure 1. Solution cost and time comparisons. a) The lines represent costs compared to the 373 Gurobi cost baseline. The numbers on the blue line represent total cost of a solution in million $ 374 and the numbers on the green line represent how much more expensive, again in million $, the 375 SA/Marxan solution is compared to the ILP solutions. b) Time to solution comparisons between 376 solvers. Marxan parameters used are: 72 features, 148,510 planning units, 108 iterations, using 377 mean cost and time. Note that in a) gurobi (red) and Rsymphony (blue) yielded optimal solutions 378 for all target values and so their lines are plotted exactly on top of each other. 379 
Table 1. Scenarios investigated in our analysis. The total number of scenarios tested for both 369 Gurobi and SYMPHONY are 135. For Marxan analysis, we included calibration steps as well, 370 which brought the total number of scenarios to 2700 for that algorithm. 371 
Figure 2. Objective function value and time comparisons using a boundary penalty to achieve 381 spatially compact solutions. a) Deviation from lowest objective function value for solvers used 382 and over a range of boundary penalty or boundary length modifier values (BLM); zero deviation 383 indicates optimal solution. b) Time to solution comparisons between solvers and across BLM 384 values. Note that in a) gurobi (red) and Rsymphony (blue) yielded optimal solutions for all target 385 values and so their lines are plotted exactly on top of each other. 386
Citations
Guidelines for using A global standard for the identification of Key Biodiversity Areas: version 1.2
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TL;DR: The KBA Guidelines as discussed by the authors provide an overview of the steps for identifying and delineating key biodiversity areas, together with explanation of how the KBA criteria, thresholds and delineation procedures should be applied in practice.
Narrowly distributed taxa are disproportionately informative for conservation planning
TL;DR: In this article , the authors quantitatively compared the informativeness of narrowly distributed and widespread taxa in identifying areas that meet taxon-specific conservation targets, and also measured the cost-efficiency of meeting those targets.
Integrating connectivity in marine protected area design: A case study between the Philippines and Taiwan
Monique Mercado-Vicentillo,Pierre-Alexandre Château,Yang-Chi Chang,Nien-Tsu Alfred Hu +3 more
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References
Optimization by Simulated Annealing
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46.9K
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Linear Programming and Extensions
George B. Dantzig
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TL;DR: This classic book looks at a wealth of examples and develops linear programming methods for their solutions and begins by introducing the basic theory of linear inequalities and describes the powerful simplex method used to solve them.
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Systematic Conservation Planning
C. R. Margules,Robert L. Pressey +1 more
- 13 Sep 2007
TL;DR: A more systematic approach to locating and designing reserves has been evolving and this approach will need to be implemented if a large proportion of today's biodiversity is to exist in a future of increasing numbers of people and their demands on natural resources.
Estimating site occupancy rates when detection probabilities are less than one
Darryl I. MacKenzie,James D. Nichols,Gideon B. Lachman,Sam Droege,J. Andrew Royle,Catherine A. Langtimm +5 more
TL;DR: In this paper, a model and likelihood-based method for estimating site occupancy rates when detection probabilities are 0.3 was proposed for American toads (Bufo americanus) and spring peepers (Pseudacris crucifer).