ArticlePeerJ2020
Exact integer linear programming solvers outperform simulated annealing for solving conservation planning problems.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Prioritizing reducing impacts on species to minimize residual reserves.Science advances · 2026Article
- Balancing land use for conservation, agriculture, and renewable energy.Nature communications · 2026Article
- Land tenure contributions to protected area growth under alternative conservation targets in the Australian monsoon tropics.Conservation biology : the journal of the Society for Conservation Biology · 2026Article
- Different Data for Different Goals: Exploring Trade-Offs and Synergies in the Use of Spatial Data Inputs to Optimize Conservation Action in Sagebrush Ecosystems.Ecology and evolution · 2025Article
- Advancing at-risk species recovery planning in an era of rapid ecological change with a transparent, flexible, and expert-engaged approach.Conservation biology : the journal of the Society for Conservation Biology · 2025Article
- Systematic conservation prioritization with the prioritizr R package.Conservation biology : the journal of the Society for Conservation Biology · 2025Article
- Article
- Multi-objective optimization can balance trade-offs among boreal caribou, biodiversity, and climate change objectives when conservation hotspots do not overlap.Scientific reports · 2022Article
- Trade-offs Between the Value of Ecosystem Services and Connectivity Among Protected Areas in the Upper Chattahoochee Watershed.Environmental management · 2022Article
- Narrowly distributed taxa are disproportionately informative for conservation planning.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
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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 exact integer linear programing (EILP) solvers. Using a case study in BC, Canada, we compare the cost-effectiveness and processing times of SA used in Marxan versus EILP using both commercial and open-source algorithms. Plans for expanding protected area systems based on EILP algorithms were 12-30% cheaper than plans using SA, due to EILP's ability to find optimal solutions as opposed to approximations. The best EILP solver we examined was on average 1,071 times faster than the SA algorithm tested. The performance advantages of EILP solvers were also observed when we aimed for spatially compact solutions by including a boundary penalty. One practical advantage of using EILP over SA is that the analysis does not require calibration, saving even more time. Given the performance of EILP 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.