about Marxan. - Kawarthas, Naturally Connected

Marxan Analysis for Conservation System Design

Geoff ClarkSettled Landscapes Spatial AnalystSouthern Science and Information SectionOntario Ministry of Natural Resources

A.D. Latornell Symposium ‐ November 2010, Friday AM, F1F

A brief overview of Marxan decision support software its use in MNR’s NHS analysis processs

Outline

• What is Marxan?• What is systematic conservation planning?• Benefits of using systematic conservation planning• Brief Overview MNR’s NHS Approach Using Marxan• Case Study – SydenhamWatershed

• Input data sets• Simplified Example of Marxan Analysis• Case Study – Socio‐political constraints in Marxan• Case Study Results

• Optimized System• Summed Solution• A What‐If Scenario

What is Marxan?• Decision support tool for reserve system design, specifically

designed to solve what is known as the minimum set problem.

• Marxan is used in conjunction with other forms of knowledge as part of a systematic conservation planning process.

• Other forms of knowledge are essential to the refinement of Marxan inputs, the interpretation of Marxan outcomes and the final placement of conservation system boundaries.

Credit: Ardon, J.A., Possingham, H.P., and Klein, C.J. (eds). 2010. Marxan Good Practices Handbook, Version 2. Pacific Marine Analysis and Research Association, Victoria, BC, Canada. 165 pages. www.pacmara.org.

Systematic conservation planning

• Locate, design, and manage protected areas that represent the biodiversity of a region.

• Transparent process of selecting and designing a system to meet clear region‐wide conservation goals.

Credit: Ardon, J.A., Possingham, H.P., and Klein, C.J. (eds). 2010. Marxan Good Practices Handbook, Version 2. Pacific Marine Analysis and Research Association, Victoria, BC, Canada. 165 pages. www.pacmara.org.Credit: Mace, G.M., Possingham, H.P., and Leader‐Williams, N. 2006. Prioritizing choices in conservation. In McDonald, D.W. and Service, K. (eds). Key Topics in Conservation Biology. Blackwell Publishing, Malden, MA USA, 17‐34.

Photo: Hansueli Krapf, Creative Commons

Systematic conservation planning

• Ensure integrity of the broader ecosystem by meeting big‐picture, regional‐scale goals.

• Systematic conservation planning is a departure from ad‐hoc, site‐by‐site approaches that have been used in the past.

• Ad‐hoc approaches can lead to fragmented collections of sites which may not adequately represent the regional landscape.

Credit: Ardon, J.A., Possingham, H.P., and Klein, C.J. (eds). 2010. Marxan Good Practices Handbook, Version 2. Pacific Marine Analysis and Research Association, Victoria, BC, Canada. 165 pages. www.pacmara.org.

Photo: Luc Viatour

• Empowering – Opportunity to ask lots of what if questions and see outcomes across the landscape.

• Comprehensive – Quickly check through a large number of different areas across the landscape.

• Transparent and Defendable ‐Why an area was included or not within a system can be explained relative to the objectives the Planning Team said they want met.

• Easily Updatable – We can build on what has already been done without having to start over again. Stakeholders can quickly be shown how their system might need to be adjusted because of changes in legislation, policy, knowledge, information, land‐cover conditions, etc.

Benefits of using systematic conservation planning

Credit: Voros, Steve. Ministry of Natural Resources. Powerpoint “How Marxan Works”

About Marxan• Created by Ian Ball and Hugh Possingham at University of

Adelaide, Australia in 2000 as a modification of SPEXAN software.

• New versions continue to be developed at The Ecology Centre, University of Queensland with funding provided from a wide range of public and private organizations.

• Marxan is internationally recognized and well understood by the global conservation community.

• Uses a mathematical algorithm called Simulated Annealing to evaluate reserve system design options.

• Can be downloaded from: www.uq.edu.au/marxan/

1. Identify the landscape for the conservation reserve system analysis and who the stakeholders are.

Analysis area should be based on ecological boundaries not political ones.

MNR’s NHS Approach Using Marxan


Compiledata

2. Gather available information to support the values and issues identified by participants

Use the data gathered to summarize the current state of the landscape



1. Identify the landscape for the conservation reserve system analysis and who the stakeholders are.

Analysis area should be based on ecological boundaries not political ones.

Set targets for initial scenarios

Compiledata

3. Planning Team reviews current conditions across the landscape and identifies specific conservation targets for Marxan to achieve.



2. Gather available information to support the values and issues identified by participants

Use the data gathered to summarize the current state of the landscape


Compiledata

Model scenarios for comparison

4. Using input data and targets, multiple “learning”scenarios are developed for comparison using Marxan. (What if’s)



3. Planning Team reviews current conditions across the landscape and identifies specific conservation targets for Marxan to achieve.

Team reviews and discusses scenarios undertake further modelling


Compiledata


5. Planning Team compares the Learning Scenarios using maps, tables and graphs.

A Preferred Scenario is formulated by the Planning Team



4. Using input data and targets, multiple “learning”scenarios are developed for comparison using Marxan. (What if’s)

Marxan is run again using targets from Preferred Scenario



Compiledata


Preferred scenario chosen by team



5. Planning Team compares the Learning Scenarios using maps, tables and graphs.

A Preferred Scenario is formulated by the Planning Team

Participants review maps and finalize the result through consensus

Information can be used to support:- Priorities for stewardship projects- Land use planning and policy decisions- Development proposal assessments

Final mapping of priority features, areas and linkages



Compiledata Model scenarios

for comparison

Preferred scenario chosen by team



Marxan is run again using targets from Preferred Scenario

The study area for the analysis was based on the Sydenham watershed. The analysis was prepared for a 2 km buffer of the area.

Case Study: Determination of Study Area

Case Study: Initial Determination of Conservation Targets

• Analysis is based on input conservation targets– Ecological Functions– Hydrologic Functions– Biodiversity Representation– Socio‐political Constraints

• Targets tell Marxan how much should be included in final output conservation portfolio

• This case study: all targets set to 50%– Should identify/represent the “best half” of what is left on the

landscape

Conservation Targets – Ecological / Hydrologic / Biodiversity Rep.Target Set in Scenario

ID Name Area (ha) % of Total in

Watershed2 Wetland (Marsh) 5166 50 %12 Wooded Wetland (Swamp) 10441 50 %15 Prairie or Savannah 154 50 %100 Forest Soil Not Mapped 94 50 %130 Forest C-W 232 50 %140 Forest C-MW 20 50 %150 Forest C-I 6325 50 %160 Forest C-P-S 6793 50 %170 Forest C-VP 1 50 %230 Forest L-W 1011 50 %231 Forest L-W-S 18 50 %250 Forest L-I 1192 50 %251 Forest L-I-S 20 50 %260 Forest L-P 1696 50 %320 Forest S-R 32 50 %330 Forest S-W 662 50 %350 Forest S-I 2552 50 %351 Forest S-I-S 24 50 %360 Forest S-P 862 50 %430 Forest G-W 16 50 %600 Forest Organic 165 50 %700 Forest Aluvium 2317 50 %1000 Total Forest 24031 50 %1111 Deciduous Forest 10953 50 %1122 Mixed Forest 188 50 %1133 Coniferous Forest 480 50 %1144 Other Forest 12411 50 %3000 Riparian Other 0 0 %3001 Riparian Forest 3350 50 %3002 Riparian Marsh Fen Bog 158 50 %3003 Riparian Prairie Savannah 8 50 %3009 Riparian Open Water 994 50 %3012 Riparian Wooded Wetland 1624 50 %

Target Set in ScenarioID Name Area (ha) % of Total

in Watershed

3015 Riparian Coastal Swamp 3 50 %3016 Riparian Coastal Marsh 1182 50 %3044 Riparian Urban 0 0 %4001 Headwater Woodland 5257 50 %4002 Headwater Wetland 129 50 %4003 Headwater Prairie

Savannah76 50 %

4004 Headwater Coastal Swamp 27 50 %4005 Headwater Coastal Marsh 1720 50 %4012 Headwater Wooded

Wetland4550 50 %

4022 Coastal Wetland 4785 50 %4023 Coastal Wooded Wetland 137 50 %4032 Large River Systems 3502 50 %6002 Ovenbird Habitat 475 50 %7001 Forest Core 100m From

Edge3318 50 %

7002 Forest Core 200m From Edge

601 50 %

7050 Forest Patch 50 to < 100 ha 5305 50 %7100 Forest Patch 100 ha to <

200 ha2734 50 %

7200 Forest Patch Over 200 ha 939 50 %7300 WFZ Other 0 0 %7301 WFZ Woodlands 10947 50 %7303 WFZ Prairie Savannah 107 50 %7344 WFZ Urban 0 0 %8001 Largest Natural Patch per

Subwatershed2127 50 %

9001 Bird Stops Very Low 2432 50 %9002 Bird Stops Low 496 50 %9003 Bird Stops Medium 2406 50 %9004 Bird Stops High 2513 50 %9005 Bird Stops Very High 1842 50 %

Summarize to Planning Units

All input data layers are summarized into 5 hectare “planning units”that cover the entire study area.

A hexagon covers only 5 hectares of land but the ecological value it contains may add up to more than 5 hectares.

1: 40,000

1: 5,000

3.24 ha Swamp (ID 12)3.24 ha Forest on Alluvial soil (ID 600)3.24 ha Total Forest (ID 1000)3.24 ha Forest Other (ID 1144)1.64 ha Riparian Swamp (ID 3012)0.9 ha Riparian Urban (ID 3044)0.43 ha Forest Core 100m (ID 7001)3.24 ha Forest Patch 50-100ha (ID 7050)1.76 ha WFZ Urban (ID 7344)20.93 ha – TOTAL CONTRIBUTION

1.44 ha WFZ Urban (ID 3044)

Uses the sum of three COST inputs to determine most efficient combination of areas to include subject to the targets and constraints.

1. Land Unit Cost – such as:

simply the amount of area

land market values

+2. Land Unit Boundary Cost – controls how spread out across the landscape the

selected land units can be. The higher this cost, the more clumped together the areas selected will be.

+3. Cost of Not Meeting Targets – A penalty cost is incurred if a conservation target is

not represented in the selected system.

How Does Marxan Work?


Nine planning units each 1 X 1 km so Land Unit Boundary length for each is 1 and Land Unit Cost based on area for each is 1Km2 or simply 1

To clump areas together as much as possible, we’ll multiply the Land Unit Boundary length by 1.5

Number of possible combinations to test in order to find the cheapest one = 29 or 512

For Example

Ecoland Landscape

Three values have been identified, mouse, fish and butterfly, each with a cost of 10 if they are not found in a Land Unit

We’ll set a target that each should be represented at least once in the system


Let’s Look at 2 out of the 512 Possible Options

Total Land Unit Cost = 4

Option A

Boundary cost = 12 * 1.5 = 18

+ +

Target Cost = 10

= 32

Total Cost of Option A

Option B

Boundary cost = 8 * 1.5 = 12

+ +

Target Cost = 0

= 16

Total Cost of Option B

Total Land Unit Cost = 4


1. Excluded – can never be part of a system design• e.g. urban areas, approved

development zones

2. Conserved – must always be part of any system design• e.g. existing protected areas

3. Preferred – all costs being equal, include this land unit over one that is simply available• e.g. publicly owned lands

It’s also possible to reduce the number of options through constraints usually to account for existing or approved land use decisions :

Case Study – Socio‐Political Constraints

Case Study: Lowest Cost Solution

The outputs from Marxan (text files) are joined back to the hexagon shapefile for visualization.

Run 1Total Cost = 19

Summed Solution

2 2

2




Most Efficient or Least Cost Solution

Case Study: Summed Solution

The summed solution shows how many times each hexagon was selected out of the 100 runs.

Case Study: What If?Walpole Island is contributing a lot of natural environment to the NHS.

What would happen if Walpole Island was removed from the analysis (marked as excluded so Marxancouldn’t look there)…

Case Study: What If?In this scenario, Walpole Island was marked as excluded from analysis.

Marxan must work harder to find ecological features in the remaining land area to meet the desired targets.

This shows just how significant Walpole Island is to this watershed’s ecological integrity and biodiversity.

• Zoomed in we can see the 5ha hexagons which contain the information and areas for each feature

• Using GIS we can map the underlying features captured by the hexagons identified in the final scenario

• We can look at how well the boundaries from the GIS layer capture the actual features on the ground using available imagery

•By developing some rules we can identify areas with restoration potential where existing natural cover is fragmented in order to develop a connected system

•In this example GIS was used to identify areas between features that are within 300mandgaps/holes within features at least 300m wide

After Marxan work is complete, the hexagon results need to be mapped back to natural features on the landscape.

Thank you

Geoff ClarkSettled Landscapes Spatial AnalystSouthern Science and Information SectionOntario Ministry of Natural Resources

A.D. Latornell Symposium ‐ November 2010, Friday AM, F1F

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about Marxan. - Kawarthas, Naturally Connected