What is Marxan?

Marxan tools are designed to help users create cost-effective spatial plans that meet predefined conservation goals and targets while minimising constraints to achieve them. Marxan is aligned with the systematic conservation planning framework discussed in previous lessons.

Over the years, Marxan has grown from its standard application to consider more complex challenges like incorporating multiple zones, connectivity, and, probabilities (Figure 1). In addition, Marxan’s user community has also built companion tools (e.g., plug-ins and interfaces) to assist with planning projects. All Marxan tools are free and open access. They include extensive documentation and examples to help users learn to use them efficiently to find solutions to complex conservation issues. 

Marxan software has mostly been used to facilitate the design of conservation networks but has now evolved to be useful in other contexts. To date, it has supported decisions on a wide range of conservation planning problems at global, regional and local scales, including:

• Restoration activities in the Atlantic Forest, Brazil, in the Yucatán Peninsula in the Mexican Caribbean, in the Murray–Darling Basin in South Australia, and southwestern Alberta, Canada.
• Provision of ecosystem services in Central Coast ecoregion of California, United States, Telemark in southern Norway, and Vermont, United States.
• Understanding trade-offs between competing objectives in the Andes of Bolivia, and Central Kalimantan, Indonesia.
• Identifying management priorities in the Danube River Basin, Europe, and South Africa's grassland biome.
• Law enforcement activities in the Greater Virunga Landscape, in Central Africa, and the Patos Lagoon estuary along the Brazilian coast
• Design conservation networks to preserve environmental and cultural values (e.g., Heiner et al. or Martinez-Harms et al. 2021)
• Planning for conservation development objectives (e.g., Heiner et al. 2019)
• Reporting on the performance of existing reserve systems
• Developing multiple-use zoning plans for natural resource management
• Assess the feasibility and set of options to meet global conservation targets

The first Marxan software is primarily a product of Ian Ball’s PhD thesis (Ball 2000) that was supervised by and funded through Professor Hugh Possingham. The idea and problem formulation for Marxan was developed by Prof. Possingham to address urgent forest conservation planning problems in Australia.

In the initial stages, it was called SPEXAN, a combination of the words Spatially Explicit Annealing. At that time, the most substantial funding was from Environment Australia (EA). We owe much to Andrew Taplin for this initial support. 

Marxan was developed as a modified version of SPEXAN to meet the needs of the Great Barrier Reef Marine Planning Authority (GBRMPA) in their 2003-2004 rezoning plans. GBRMPA provided partial support for the modification. Marxan also was used by Adam Lewis and Suzanne Slegers to provide decision support for the GBR representative areas program. Along with The Nature Conservancy's ecoregional planning processes, these represent some of the largest applications of Marxan/SPEXAN. Finally, it evolved into Marxan which is a portmanteau acronym, fusing MARine, and SPEXAN, itself an acronym for SPatially EXplicit ANnealing.

The next lessons will now focus on how to use Marxan including definitions and practical application of the key concepts that underpin the software (Marxan 101), and more advanced concepts on how to use data to run Marxan (Advanced).

Ball, I.R. 2000. Mathematical applications for conservation ecology: the dynamics of tree hollows and the design of nature reserves. PhD Thesis, The University of Adelaide.

Ball, I.R. and Possingham, H.P. 2000. Marxan (V1.8.2): Marine Reserve Design Using Spatially Explicit Annealing, a Manual.

Ball, I. R., Possingham, H. P., & Watts, M. E. (2009). Marxan and relatives: Software for spatial conservation prioritization. In A. Moilanen, K. A. Wilson, & H. P. Possingham (Eds.), Spatial conservation prioritisation: Quantitative methods and computational tools (pp. 185–210). Oxford University Press.