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| Marxan | |
|---|---|
| Name | Marxan |
| Developer | University of Tasmania; The Nature Conservancy; World Wildlife Fund |
| Released | 1996 |
| Latest release version | Marxan with Zones (series) |
| Programming language | C, C++ |
| Operating system | Cross-platform |
| Genre | Conservation planning software |
| License | Open-source / academic |
Marxan
Marxan is a decision-support tool for systematic conservation planning used to identify networks of priority areas for biodiversity conservation, spatial planning, and resource management. It integrates spatial data, representation targets, cost layers, and socioeconomic constraints to produce optimized reserve designs and zoning proposals. Widely used by conservation organizations, government agencies, and research institutions, Marxan has influenced policy in marine and terrestrial contexts and has been paired with tools from United Nations Environment Programme, IUCN, and Convention on Biological Diversity initiatives.
Marxan implements a spatial optimization framework based on simulated annealing and site-selection heuristics to find near-optimal configurations of planning units that meet biodiversity targets while minimizing cost and fragmentation. The software accepts raster or vector planning units and supports additional features such as connectivity, boundary length modifiers, and penalty factors to address edge effects and representativeness. Users commonly employ Marxan alongside mapping and database platforms from Esri, QGIS, ArcGIS, and biodiversity data sources like GBIF, Protected Planet, and IUCN Red List of Threatened Species. Outputs inform conservation strategies linked to policy processes such as Aichi Biodiversity Targets and Post-2020 Global Biodiversity Framework negotiations.
Marxan was originally developed in the 1990s by researchers at the University of Tasmania and collaborators including The Nature Conservancy to operationalize systematic reserve design concepts articulated in academic work by figures associated with Island Biogeography and conservation theory. Early adopters included practitioners linked to World Wildlife Fund and national parks agencies in Australia, New Zealand, and the United States during major planning efforts such as the establishment of marine protected areas around the Great Barrier Reef and terrestrial reserves near Tasmania. Subsequent development produced Marxan with Zones and Marxan with Connectivity extensions, with contributions from researchers at University of Queensland, CSIRO, and international partners in Europe, Africa, and the Americas. Major deployments have been associated with international programs like Global Environment Facility funded projects and regional initiatives coordinated by BirdLife International and Conservation International.
The core algorithm uses simulated annealing complemented by iterative improvement heuristics to explore the combinatorial search space of planning-unit selections. Marxan models include objective functions that balance representation of conservation features (species, habitats, ecosystems) against cost layers and apply boundary length modifiers to favor compactness. Advanced modules implement spatially explicit connectivity metrics and incorporate Marxan with Zones logic to solve multi-zone allocation problems analogous to zoning ordinances used in municipal planning such as those administered by UN-Habitat. Integrations with integer linear programming and stochastic optimization provided by academic groups at Stanford University and University of Oxford have produced comparative studies. Implementations exist in compiled binaries, R wrappers maintained in academic repositories, and plugin interfaces for GIS platforms developed by teams at University of Washington and James Cook University.
Marxan has supported designation and evaluation of terrestrial reserves, marine protected area networks, fisheries closures, and multi-use zoning in regions ranging from the Caribbean and Coral Triangle to the Mediterranean Sea and Amazon Basin. Notable case studies include national systematic conservation planning in countries such as South Africa, Chile, Canada, and Philippines where outputs influenced laws or protected-area proclamations administered by ministries like Department of Environment and Natural Resources (Philippines). Projects funded by World Bank and Global Environment Facility have used Marxan to prioritize investment portfolios for biodiversity offsets and REDD+ planning in landscapes intersecting with projects led by UNEP-WCMC and FAO initiatives. Academic evaluations comparing Marxan outcomes with alternatives used datasets from GBIF, IUCN Red List, and long-term monitoring programs at sites like Prince Edward Islands and Galápagos Islands.
Typical inputs include planning-unit geometries, conservation feature layers (species occurrences, habitat maps, ecosystem types), cost surfaces (economic opportunity cost, enforcement cost), and target settings expressed as representation percentages or absolute amounts. Ancillary inputs may specify locked-in or locked-out units reflecting protected status under instruments such as Ramsar Convention designations or Natura 2000 sites. Parameters that strongly influence solutions include the number of runs, iterations per run, and boundary length modifier; calibration often relies on sensitivity analyses and expert elicitation involving stakeholders from agencies like National Oceanic and Atmospheric Administration or academic partners from University of California, Santa Barbara.
Marxan produces heuristic solutions that are computationally efficient for large planning problems but do not guarantee global optimality; results are typically presented as selection frequency maps and best solutions across many runs. Limitations include sensitivity to input quality, scale mismatches, spatial autocorrelation, and the challenge of integrating complex socioeconomic dynamics or dynamic species distributions driven by processes examined in studies from Intergovernmental Panel on Climate Change. Validation approaches include cross-validation with independent biodiversity surveys, comparison with integer programming solutions from groups at Imperial College London, and scenario testing in adaptive management cycles used by agencies such as Parks Canada.
Adoption of Marxan spans NGOs, government agencies, and academic research groups; governance of code and community development has involved consortia at University of Tasmania, The Nature Conservancy, and partners within the UNEP and IUCN networks. Training workshops, user forums, and collaborative projects are routinely organized by institutions including Conservation International, BirdLife International, and regional research centers at University of Cape Town and Pontificia Universidad Católica de Chile. The broader ecosystem includes complementary tools such as Zonation and prioritizr that emerged from research groups at Swedish University of Agricultural Sciences and University of Auckland.
Category:Conservation software