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MODULE 3. Marxan ToolsMarxan is a free software program that provides decision support to teams ofconservation planners and local experts identifying efficient portfolios of planning areasthat combine to satisfy a number of ecological, social, and economic goals (Ball andPossingham, 2000). Marxan is used by over 1100 registered users from 600 organizations(government, academia and NGOs) in 95 countries as a decision support tool to consideroptions in terrestrial and marine reserve design. It is perhaps the most popular "siteselection optimization" software available and is widely published. It is a stand-aloneprogram that requires no other software to run, although GIS makes it simpler to preparethe data, generate the required input files, and view the results. For the latest informationabout Marxan, users should register and download the software athttp://www.uq.edu.au/marxanEarly conservation assessments depended on manual mapping to delineate sites and wereoften reliant on expert opinion to prioritize conservation areas. The large number, size,and diverse types of datasets describing the targets eventually required the use of a moresystematic and efficient site selection procedure. Marxan software is an optimizationprogram and provides decision support for teams of experts choosing between hundredsof biodiversity targets and thousands of candidate areas (planning units). It identifiesefficient portfolios of planning units and has a measure of flexibility that allows the teamsto adapt efficient solutions to real world situations. Using a transparent process that isdriven by quantitative goals, the analysis is repeatable and objective. Marxan results canillustrate a pattern of priority sites of low political or social pressure that can still satisfythe explicit biodiversity goals. It can alsoidentify a network of sites where resourcesnecessary to implement conservationstrategies or threat abatement are forecastto be lower (TNC, 2005).Planning units are parts of the land andseascape that are analyzed as the potentialbuilding blocks of an expanded system ofreserves or areas of conservation priority.They allow a comparison betweencandidate areas. Planning units can besystematic units such as hexagons; naturalareas like watersheds, administrativeboundaries; or arbitrary sub divisions ofthe landscape. They differ widely in sizebetween studies and within regions,dependant mostly on scale of analysis anddata resolution (TNC, 2005).TNC Protected Area Tools (PAT) Version 3.0The Nature Conservancy, August 200942
3.0 The MARXAN AlgorithmOne of the more popular algorithms implemented in Marxan is the ‘simulated annealing’site optimization algorithm. In order to design an optimal reserve network, each planningunit is examined for the values it contains. The features within one planning unit may bevaluable alone but may not be the best choice overall—depending on the distribution andreplication of the other features in the wider planning area.During the simulated annealing procedure, an initial portfolio of planning units isselected. Planning units are then added and removed in an attempt to improve theefficiency of the portfolio. Early in the procedure, changes in the portfolio that do notimprove efficiency can be made in order to allow the possibility of finding a moreefficient overall portfolio. The requirement to accept only those changes that improveefficiency becomes stricter as the algorithm progresses through a set of iterations. Notethat for any set of conservation targets and goals, there may be many efficient andrepresentative portfolios that meet all conservation goals, but most of these networkswould have a number of planning units in common. Many runs of the algorithm are usedto find the most efficient portfolio and to calculate a measure of irreplaceability (usedhere to indicate the number of times a particular unit is chosen). In some cases,conservation targets are only found in limited sites – areas of high irreplaceability that arealways chosen in any representative portfolio. Additionally, areas of high irreplaceabilityalso include planning units, whose exclusion would require a proportionally largerconservation area network to achieve the same level of representation, resulting in a lossof portfolio efficiency.The algorithm attempts to minimize portfoliototal ‘cost’ whilst meeting conservation goalsin a spatially compact network of sites. Thisset of objectives constitutes the ‘objective costfunction’ and is made up of three user-defined‘costs’:Total Cost = ∑Unit Cost + ∑SpeciesPenalties + ∑Boundary Lengthwhere ‘Total Cost’ is the objective (to beminimized), ‘Unit Cost’ is a cost assigned toeach planning unit, ‘Species Penalties’ arecosts imposed for failing to meet biodiversitytarget goals, and ‘Boundary Length’ is a costdetermined by the total outer boundary lengthof the portfolio.Attempts are made to minimize the totalportfolio cost by selecting the fewest planningunits with the lowest total unit cost needed toTNC Protected Area Tools (PAT) Version 3.0The Nature Conservancy, August 200943
Page 1 and 2: Protected Area Tools (PAT)TMfor Arc
Page 3 and 4: AcknowledgementsThe idea for the de
Page 5 and 6: Mandatory Requirements Needed to Ru
Page 7 and 8: Installing the Protected Area Tools
Page 9 and 10: The majority of the work that goes
Page 11 and 12: data to spatially represent the spe
Page 13 and 14: 2003, Araújo et al. 2002). In some
Page 15 and 16: 1.1 Setting up the ERS TableSuccess
Page 17 and 18: 1.1.1 Overlay FunctionAnother item
Page 19 and 20: features have been added to the map
Page 21 and 22: the output will automatically defau
Page 23 and 24: Once you click OK in the previous m
Page 25 and 26: unit or feature (i.e. polygon) must
Page 27 and 28: higher elevations and ridges moving
Page 29 and 30: 1.3.4.2 Marine ERS ModelsDeveloping
Page 31 and 32: MODULE 2. Relative Biodiversity Ind
Page 33 and 34: Normalized relative biodiversity in
Page 35 and 36: EXERCISE 2. Calculating the Relativ
Page 37 and 38: the targets (polygons with lots of
Page 39 and 40: Once this step is performed, you ca
Page 41: This selection routine will choose
Page 45 and 46: 6. All input features must have the
Page 47 and 48: grid of squares or lattice of hexag
Page 49 and 50: • To make "area" and "length" com
Page 51 and 52: Sug gested steps for preparing inpu
Page 53 and 54: 2. Planning Unit File (pu.dat)This
Page 55 and 56: ‘id2’ are set but no value for
Page 57 and 58: conservation target files are alrea
Page 59 and 60: An example of what an attribute tab
Page 61 and 62: values. These values will be used i
Page 63 and 64: Occurrence (OCC), Minimum Clump (CL
Page 65 and 66: 3.2.4 Combining Marxan Input FilesI
Page 67 and 68: 2. Click onto the Run Options tab p
Page 69 and 70: This is the folder where the output
Page 71 and 72: 3.4 Joining and Displaying Marxan O
Page 73 and 74: Figure 12. Results of joining the t
Page 75: McPherson, M, S. Schill, G. Raber,

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