Socio-cultural, Significance, Physical and chemical, Management and planning, Flora, Fauna, Economic
Medium-long term – The time required for Marxan to provide 100 good solutions ranges from minutes to days. It is usually the advanced features of Marxan (such as separation distance and minimum clump size) that can slow the analysis down significantly, especially with large numbers of planning units (Game and Grantham 2008).
Marxan is a suite of tools designed to help decision makers find good solutions to conservation planning problems. This includes free software that can be used to solve several types of planning problems and extensive documentation and examples describing a framework for approaching conservation planning (Marxan 2020).
Marxan provides decision support to a range of conservation planning problems, including:
the design of new reserve systems
reporting on the performance of existing reserve systems
developing multiple-use zoning plans for natural resource management (Marxan 2020).
These features provide users with decision support to achieve an efficient allocation of resources across a range of different uses, including:
Identify areas that efficiently meet targets for a range of biodiversity features for minimal cost
Use the principle of complementarity to select planning units which complement the conservation area network (the whole is more than the sum of its parts)
Meet spatial requirements such as compactness of a reserve system
Include data on ecological processes, threats, and condition
Identify trade-offs between conservation and socio-economic objectives
Generate a number of very good (near-optimal) solutions (Marxan 2020).
Marxan software has contributed to several major conservation projects including the rezoning of the Great Barrier Reef Marine Park (Bell et al 2009 and references within).
Marxan uses the well-accepted 'minimum-set' approach to identify spatial conservation priorities, 'minimum-set' and 'maximal coverage'. The objective of the minimum-set strategy is to achieve the conservation objectives while minimizing the resources expended or negative impacts on stakeholders.
Marxan provides many near-optimal solutions to a minimum-set problem, which are designed to be used as decision support and considered within a broader decision-making process involving a range of stakeholders (Watts et al 2017 and references within).
There are four main steps to running Marxan:
Setting up the input files
Setting the scenario parameters
Interpreting the results (Game and Grantham 2008).
Users should refer to the Marxan User Manual (Game and Grantham 2008) and the Marxan Good Practices Handbook (Ardron et al 2010). There are several freely-available user interfaces that can assist in running Marxan, for example C-plan, CLUZ (Conservation Land Use Zoning) and PANDA (Protected areas Network Design Application) (Game and Grantham 2008).
Criteria groupings of the method
Spatial data: planning units and conservation features. Planning unit data will always need to be defined and the selection of conservation criteria will be dependent on the type of reserve being implemented.
Four input files are required (without them Marxan will not run):
Input parameter (sets values for all the main parameters that control the way Marxan works)
Conservation feature (information about each of the conservation features being considered)
Planning unit (information about the planning units including cost and condition)
Planning unit versus conservation feature (the distribution of conservation features in each of the planning units) (Game and Granthan 2008).
Optional files include:
Boundary length (the length or ‘effective length’ of shared boundaries between planning units)
Block definition (can be used to set a series of default variable values for groups of conservation features) (Game and Grantham 2008).
Marxan requires experience with conservation planning and GIS software.
The Marxan software, Microsoft operating system, GIS software and access to spatial datasets.
In addition to advising which planning units make up an efficient reserve system, Marxan can also provide other outputs, such as:
Run (the repeat runs the output pertains to)
Value (the overall objective function value for the solution from that run, which is how Marxan chooses the ‘best’ solution out of you repeat runs)
Cost (total cost of the reserve system)
Planning Units (PUs) (number of planning units contained in the solution for that run)
Boundary (total boundary length of the reserve system)
Missing (the number of conservation features that did not achieve their targets in the final solution for that run)
Shortfall (the amount by which the targets for conservation features have not been met in the solution for that run)
Penalty (the penalty that was added to the objective function because the reserve system failed to meet the representation targets for all features) (Game and Grantham 2008).
Marxan can save up to eight different output files:
Solutions for each run
Best solution from all runs
Missing values for each run
Screen log file
Snapshot files (Game and Grantham 2008).
Input to other planning processes and interfaces.
Marine and terrestrial applications.
Land use planning.
Reserve design planning.
Resilience and disaster planning.
Spatial resource planning.
Criteria by category
Physical and chemical
Planning unit dimensions
Planning unit location
Management and planning
Planning unit cost
Planning unit location
Planning unit status (condition)
Marxan is useful to spatial planners for conservation and reserve design purposes. This technique may be useful for government agencies, natural resource managers and others.
Compatible with other software suites.
Outputs verified by sensitivity analysis.
Data can be displayed visually for ease of interpretation.
Overcomes the ‘minimum set problem’ of reserve planning.
Facilitates transparency and repeatability in planning.
Not intended to act as a stand-alone reserve design solution, depends on the stakeholder engagement, best practice ecological principles, scientifically defensible conservation goals and targets, and quality spatial datasets.
Requires considerable experimentation in order to produce defensible results.
Department of Environment and Science, Queensland (2020) Marxan, WetlandInfo website, accessed 1 February 2023. Available at: https://wetlandinfo.des.qld.gov.au/wetlands/resources/tools/assessment-search-tool/marxan/