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Marxan outputs
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An explanation of the outputs produced by MARXAN
Systematic conservation planning involves setting targets for a number of conservation features and identifying portfolios of planning units that, when combined, meet these targets. The real-world relevance of these portfolios can be further enhanced by including a range of political and socio-economic data into the planning process and by identifying patches of suitable units, which are more ecologically viable and easier to manage.
Moreover, simulated annealing is often more useful than optimisation techniques that have also been developed by mathematicians. This is because identifying a large number of near-optimal portfolios is often more useful than identifying one optimal solution, as this allows planners more flexibility when designing conservation networks. Marxan is a conservation planning program that uses simulated annealing to identify efficient portfolios and CLUZ acts as a user-friendly interface for this process. The following web pages explain more about simulated annealing and the Marxan outputs that can be produced using CLUZ:
We also recommend that you look at the Reserve design game, which has been developed by Wayne Rochester and Hugh Possingham at The University of Queensland. This game illustrates the concepts and methods of systematic reserve design, including the process of simulated annealing.
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However,
it is generally near-impossible to identify the best portfolio for meeting
all these requirements because there are so many different potential combinations
of planning units to investigate. For example, when dealing with 100 planning
units there are 2
or 1,260,000,000,000,000,000,000,000,000,000 different combinations that
would need to be assessed to identify the best portfolio. Fortunately,
mathematicians have developed a way around this problem that identifies
near-optimal portfolios by using a process called simulated annealing.
Section
1: Explaining how simulated annealing works
