Marxan was chosen because of its ability to provide multiple solutions (56%), its scientific rigor (53%), its ability to handle large amounts of data (53%), the provision of near‐optimal solutions (52%), and its reputation (45%). A few respondents (3%) also noted political pressure as a determining factor for choosing Marxan. Additional comments on the choice of Marxan included its reputation as the “industry standard”
for conservation planning, the ability to cluster solutions, and the ability for post‐hoc analysis. Most users consider Marxan’s ease of use to be moderate or moderately difficult (76%), with several comments that the generation of input files is the most challenging aspect.
Biodiversity conservation (88%) and establishment of protected areas (72%) were the most common planning objectives. Other objectives include ecosystem functioning (36%), research (35%), and sustainable fisheries (19%). Less common responses cover community development (7%), recreation and education (4% each), and aesthetic and spiritual objectives (3% each). The objectives set in the Marxan projects were primarily set by scientists/experts (97%), followed by decision‐makers (40%) and stakeholders (39%). Results were communicated to scientists/experts (91%), decision‐makers (77%), stakeholders (57%), and the general public (28%).
Most participants (66%) had not completed their Marxan projects, and were therefore unable to answer whether their project had resulted in a conservation gain. Twenty‐four percent indicated that their project had resulted in a conservation gain, whereas ten percent said that it had not. Comments suggest a mixed experience in the influence Marxan projects had on decisions. Some projects were designed as research projects that were not intended to provide recommendations. Some Marxan users found their results being integrated into conservation plans and protected area designs, whereas others’
recommendations were ignored, or projects cancelled upon realisation of the area required for meeting objectives.
The size of the study areas and planning units used varied widely (Table 1), with an average of about 1 million square kilometres. Hexagons and squares were almost equally popular for planning units (46% and 44%), followed by irregular units (35%).
Some participants also indicated using triangles.
Table A1.1: Study area details (n=61).
Study area size (km2)
Planning unit size (km2)
Minimum 40 0.0001
Maximum 30 000 000 10 000
Mean 955 503 260
Standard deviation 4 024 649 1502
Most users applied all of Marxan’s basic options, but less than half used advanced options for either all or some conservation features (Table 2). The majority of participants used the adaptive simulated annealing function (81%), followed by iterative improvement (44%) and simulated annealing while setting the initial temperature (22%).
The heuristic options were used, but were not as popular (ranging from 17% to 6%).
Table A1.2: Marxan options.
Basic options Used Not used
Boundary length modifier 89% 11%
Repeated runs 98% 0%
Planning Unit Cost 87% 13%
Boundary Cost 79% 18%
Planning Unit Status 87% 10%
Penalty factor for conservation features
(SPF) 88% 12%
Advanced options
Used for all targets
Used for some
targets Not used
Block definitions 13% 22% 65%
Cost threshold 25% 11% 64%
Separation distance (sepdistance) 5% 26% 68%
Separation number (sepnum) 4% 15% 82%
Minimum clump size (target2) 9% 30% 61%
Number of occurrences required
(targetocc) 25% 27% 47%
Minimum viable population size 11% 16% 73%
A variety of data inputs were used (Table 3). Other data inputs that were used include sites of fisheries importance, land cover classifications, soils data, spawning aggregations, landscape matrices, hydrological functions, marine landscape and benthic terrain models, and the output of population viability analyses. The majority of respondents used a variety of targets for features (65%) rather than uniform targets.
Targets were set based on the commonness or rarity of features, ecological values, estimates of minimum viable populations, proportions of original abundance, the results of gap analyses, and species‐area relationships.
Table A1.3: Data inputs.
Used Not used
Species distributions (models and/or surveys) 88% 12%
Habitat distributions (models and/or surveys) 81% 19%
Species Abundance (surveyed populations) 54% 46%
Species presence/absence (surveys) 47% 51%
Species presence only (sightings) 74% 25%
Species/habitat incomplete coverage 46% 50%
• If yes, were any estimated to cover less than 75% of the
study area? 50% 30%
Geophysical data (e.g., topography, salinity, currents) 64% 34%
Habitat classifications 82% 18%
Biogeographic zones 62% 38%
Economic data (e.g., cost of land, value of fisheries) 43% 57%
Land/marine human use data 62% 38%
Traditional (indigenous) ecological knowledge 17% 83%
Local Ecological Knowledge 40% 60%
Expert Scientific opinion 64% 36%
3D data converted into 2D (e.g., topography, bathymetry) 43% 55%
Temporal data 12% 85%
Connectivity 37% 61%
Genetic information 6% 94%
Ecosystem services/processes information (e.g., pollination,
carbon sequestration rates) 19% 81%
Others 18% 70%
The most common data weighting options were those of relative importance (48%), expert opinion (47%), and data reliability (40%). Other weightings included commonness of features, vulnerability or rareness of features, and the viability rankings for targets. Most participants assigned the same penalties to all features (54), with some applying different values (33%). When different penalty values were used, they were mostly based on the relative importance of features or the confidence in the datasets.
Other approaches include ratings according to relative vulnerability and rareness. Many Marxan users reported undertaking sensitivity analyses to determine what values to use as the penalties.
Planning unit area was the most commonly applied cost, followed by equal costs, and economic data (Table 4). Other costs applied primarily relate to threats to ecological integrity (including proxies such as proximity to urban areas, roads).
Table A1.4: Cost.
Used Not
used
Equal cost 55% 45%
Planning unit area 76% 24%
Economic data (e.g., cost of land, value of
fisheries) 51% 49%
Naturalness 44% 49%
Expert Scientific opinion 17% 77%
Local Knowledge 14% 83%
Traditional (indigenous) ecological knowledge 9% 85%
Others 50% 47%
Most participants did not undertake sensitivity analyses (60%). Those that did reported testing the effects of planning unit shapes and sizes, grouping different features, adjusting the boundary length modifier, applying a variety of costs, changing species penalty factors, locking features in or out, randomly deleting data, and changing targets and estimates of populations.
Both the selection frequency and the overall best run were the outputs most commonly used (83% and 77%), with fewer respondents using individual runs (27%). Comments suggest that there is a preference for using the selection frequency and overall best output together to complement one another.
A1-3CONCLUSIONS
Participants listed many strengths and weaknesses of Marxan (Table 5). The most commonly listed strength is the flexibility of Marxan in providing multiple solutions, whereas the most listed weakness is insufficient guidance for adjusting the required settings. Additional responses about which Marxan parameters are not explained well indicates that most survey respondents feel that most parameters could benefit from additional explanation and examples. In particular, responses suggest that a detailed description is needed of how various Marxan parameters interact.
Table A1.5: Strengths and weaknesses of Marxan as expressed by survey participants.
Strengths (n=53) Weaknesses (n=49) Flexibility / variety of options 62% Insufficient guidance for adjusting settings 43%
Handling of large amounts of data 30% Errors/bugs 39%
Near-optimal solutions 25% Preparing the input files 37%
Repeatability/transparency 19% Steep learning curve 16%
Selection frequencies output 17% Converting outputs to GIS 14%
Reputation 13% Interpreting/explaining results 14%
Speed 13% User interface 12%
Explicit targets 11% Determining input targets/features 10%
Inclusion of costs 11% how parameters interact 10%
Systematic approach 11% Defining cost parameter 8%
Ability to create graphical outputs 9% Inability to consider multiple zones 8%
Ease of use 9% Slow with complex/large problems 8%
Objective data analysis 9% Connectivity 6%
Ability to batch files 8% Data availability 6%
Ease of post-hoc analysis 8% Data management 6%
Forces evaluation of data inputs 6% Limits of planning units, features 6%
Ability to update inputs 4% Lack of a help tool 4%
Boundary length modifier 4% Manual 4%
Free tool 4% Verifying results 4%
Arc interface extensions 2% Black box 2%
Documentation 2% Data weighting 2%
Minimum area option 2% Difficult to batch 2%
User support 2% Screening out poor solutions 2%
Participants made several recommendations for issues and practicalities that should be addressed in a good practices handbook (Table 6). The most common need was seen to be assistance with setting of all Marxan parameters, including those that some users noted having difficulty with (i.e., that for them resulted in unexplained crashes) such as separation distance.17
17 Editors’ note: these widespread concerns related to the setting up and running of Marxan led to
the re‐writing of the Marxan User Manual, in a joint project between the University of Queensland and PacMARA.
Table A1.6: Suggestions for inclusions in a good practices handbook Setting Marxan parameters (e.g., SPF, BLM, cost, separation
distance) 37%
Setting up input files / tutorial 21%
Communication and interpretation of results 19%
Ensuring robust analyses / explaining inherent biases 16%
Provide practical examples 16%
Undertaking sensitivity analyses and which parameters to test 16%
Addressing data issues (e.g., quality, coverage, management,
etc.) 14%
Explanation of settings 14%
Rules of thumb for a starting point for inputs 14%
Determining realistic project goals 9%
Parameter interactions 9%
Using the annealing settings 9%
Why and when to use Marxan 9%
Explanation of errors and troubleshooting steps 7%
Known limits of Marxan (e.g., planning units, features) 7%
Engaging with stakeholders and interest through Marxan 5%
Improving the GUI 5%
Incorporating connectivity and other network design
considerations 5%
Incorporating stochasticity and temporal variability 5%
Assessing solution quality 2%
Function and focus of each algorithm 2%
How far from optimal is the best solution? 2%
Improving speed 2%
Incorporating vulnerability 2%