cutter et al-2007-systematic conservation planning module

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Systematic Conservation Planning Lab An Introduction to Decision Support Tools for Conservation Planning FWCB8452 © 2007 Peter Cutter, Jesse Kroese, and Steven Polasky Photo: Matt McGee

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Systematic Conservation Planning Lab An Introduction to Decision Support Tools for Conservation Planning

FWCB8452

© 2007

Peter Cutter, Jesse Kroese, and Steven Polasky

Pho

to:

Mat

t McG

ee

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Outline

• Introduction o Goals o Context and Data Sources o Software: ArcView, Marxan, and CLUZ

• ArcView and CLUZ Basics o ArcView Data Structure o CLUZ Data Structure o Opening ArcView o Navigating in ArcView

• Manual Planning o Spatial efficiency o Cost efficiency o Cost threshold o 10 % Targets

• Automated Planning Using MARXAN o Single Occurrence Target o Cost-efficiency o Cost-efficiency with % Targets

Appendices • The CLUZ User Interface • Data Sources and Notes • Reference Maps • Conservation Planning Glossary

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Introduction

Goals • To gain an appreciation of the complexity of multiple feature conservation

planning. • To gain practical experience with the most popular conservation planning

decision support computer software, MARXAN. • To gain, through first-hand experience, an understanding of the strengths

and weaknesses of common assumptions and trade-offs in conservation planning.

Context and Data Landscapes in Oregon have frequently been used as examples for illustrating conservation planning concepts (Arthur et al. 2004; Arthur et al. 2002; Calkin et al. 2002; Camm et al. 2002; Csuti et al. 1997; Polasky et al. 2001; Polasky et al. in review (2007); Polasky et al. 2005; Szentandrasi et al. 1995). This background provides a rich context for exploring the conservation planning process with real-world data and well-developed scenarios. Our planning landscape is the western 2/3 of Oregon, an area for which we have three key data layers to facilitate systematic conservation planning:

• an explicit system of spatial units with which to associate other data; • an accurate assessment of the occurrence of conservation features; and • accurate estimates and of land values.

We will be looking at the landscape through the lens of 289 hexagonal land units of approximately 650 km2 each. Although over 400 vertebrate species occur in this landscape, we will limit our exercise to a mere 7 of these that differ in terms of pattern and extent of occurrence. Hexagonal planning units and species occurrence data were compiled by the Oregon Natural Heritage Program as part of its statewide Gap Analysis project. Maps showing these data can be found in Appendix 3 of this manual.

Software In this lab, we will be using 3 software programs:

• ArcView GIS - a general and very popular GIS program, • MARXAN - a software application developed by Hugh Possingham and others at

the University of Queensland (MARXAN is now used in many conservation planning projects around the world); and

• CLUZ (Conservation and Land Use Zoning) software - an extension of ArcView that helps facilitate conservation planning decision-making and serves as a link between ArcView and MARXAN. CLUZ was written by Bob Smith at Kent University, UK.

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ArcView and CLUZ Basics ArcView Data Structure Before opening ArcView, it’s useful to have a basic idea of how it stores and handles data. An ArcView project file (with a suffix ‘.apr’) serves as a directory that calls on various data sources so that they can be viewed and manipulated in a single workspace. We will be working with an ArcView project (cluz-oregon.apr) that calls on the data we need for our planning project. A fundamental data element in most projects is a ‘shapefile’ (aka “theme” or “layer”) which consists of both “topology” (information about the geographic position and relationship of information) and tabular data associated with individual geographic data elements such as points, lines, or polygons. Additionally, some data elements are just tabular data.

Extensions provide specialized functionality to ArcView. CLUZ is such an extension—built for conservation planning. CLUZ Data Structure CLUZ uses three primary data elements (one geographic theme and two data tables) to keep track of the data in a conservation planning scenario. These are briefly reviewed below. Planning Unit Theme. This theme defines the individual geographic units that form the basis of planning. Information on the area, cost, and conservation status of each planning unit are stored here. The theme has topology defining 289 contiguous hexagons (our planning units) and an associated table with a row of information pertaining to each unit.

• User-friendly tools for conservation planning

• Link between ArcView and MARXAN

• Open source

CLUZ MARXAN • DOS-based program for

applying multivariate analysis algorithms to complex datasets

• Open source

ArcView

Extensible GIS Platform

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The data fields in the Planning Unit Table include:

Unit_ID. This is the unique identifier for each planning unit.

Area. This is the area of each unit. In our exercise, we will use values in km2.

Cost. This is the cost of the planning unit. In our exercise, this equals the cost of the unit in millions of dollars.

Status. This field describes the conservation status of each unit. CLUZ allows the following values: Conserved, Earmarked, Available or Excluded. Conserved units are those that are part of existing protected areas. Earmarked units are that have been selected by the user as belonging to a proposed conservation portfolio and Available units are those that have not been selected by the user to belong to the portfolio. Excluded units are those that definitely will not belong to any portfolio for ecological, economic or political reasons.

Protection. This is an additional field that has been added to store information on actual protection in Oregon. We will use this later in the lab.

Abundance Table. This table keeps track of whether (and to what extent) each conservation feature is represented within individual planning units. In the screenshot below, the “Unit_id” column on the left refers to the unique ID of individual planning units as described above and the other columns refer to the conservation features that are the subject of the planning exercise (species, in our example) and keep track of the total square kilometers of their distribution within each unit.

Because we are tracking feature occurrence by presence/absence within each unit, you will notice that our data appear as a consistent 648 km2 per planning unit in which a particular feature occurs. Target Table. This table keeps track of information pertaining to individual conservation features and the conservation targets we have assigned to them. It is where we can adjust settings related to the biology of each feature. Finally, it

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is where MARXAN keeps track of how well our current scenario is doing with regard to meeting our preset targets.

The data fields in the Target Table are described below.

ID. A unique identifier for each conservation feature we are working with.

Name. Names of the individual conservation features that correspond with their IDs.

Total. Total documented area (km2) of each conservation feature.

Target. The amount (in km2) of each conservation feature we would like to have under protection. In our example, we have set this to equal the area of one planning unit so that our goal is one unit of representation for each conservation feature.

Conserved. The amount of each feature actually protected. This field is automatically updated as we change the conservation landscape

Pc_target. The amount in percent of each feature actually protected.

Opening ArcView

Step 1: Launch ArcView by selecting Start menu Programs GIS ArcView

Step 2: Select open an existing project and click OK.

Step 3: Navigate to filepath C:data/fwcb8452/Oregon/cluz-oregon.apr

Click OK and a map should pop up.

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ArcView Workspace Initially, you are presented with two windows, the View Window and the Project

Window: View The VIEW is the primary interface when working in Arcview. It is where spatial data can be viewed or analyzed. It is possible to have more than one VIEW in a project, but in our case we just have one, entitled “CLUZ Oregon”.

Table of Contents

Checked themes are visible

THEMES (Data)

Raised themes are active

Map Pane

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On the left side of the VIEW window is the TABLE OF CONTENTS. All of the data layers, or THEMES, are stacked vertically in the TABLE OF CONTENTS. THEMES that are checked are visible in the VIEW, although checked layers lower in the stack can be obscured by checked layers higher in the stack. The order of THEMES in the stack can be easily changed by dragging them up or down.

Practice checking THEMES on and off and moving them up or down in the stack.

In addition to visualizing a THEME in the view by checking it, you may also view it’s tabular data. First, you must make it the active THEME by clicking it. Notice that it becomes raised to indicate that it is the active THEME. Next, click on the table button , which is located the top row of menu icons.

Practice by activating the “Planning Units” THEME, and opening its table. Note that many of ArcView’s other functions also require that a THEME be first activated.

Project Window The PROJECT WINDOW allows you to move between multiple views and tables, as well as other functions we won’t be using (e.g., Charts, Layouts and Scripts).

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Although the PROJECT WINDOW can be used to access tables, such as the Abundance Data and Target data tables, we will be accessing these through shortcut buttons provided by CLUZ .

Initializing the Cluz Set-up File Before we begin working with CLUZ, we need to tell ArcView where the CLUZ set-up file is located. From the CLUZ menu, select the first option, View and edit CLUZ set-up file. At the next prompt, choose Open existing setup file.

Navigate to c:\data\fwcb8452\oregon\ and double click on the file ‘setup.clz’.

From the CLUZ menu, select the first option, View and edit CLUZ set-up file again to view the filepaths of all of the key files that our CLUZ setup will be using.

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Examining the main CLUZ data Now that CLUZ is initialized, you should get comfortable with opening the key data tables it uses.

• To open the Planning Unit table, make the planning unit theme active and click the button on the toolbar. You can adjust the size of the window and use the scroll bars if necessary to view the part of the table that you are interested in. Use the ⌧ in the upper right corner of the table window to close it again. Tables and View can also be minimized just like most windows.

• To open the Abundance Table, click the button.

• To open the target table, click the button.

- - Getting distribution data into the abundance and target tables Imagine that you have just received the results of an intensive 3 years of fieldwork: a shapefile representing the distribution of Chinook salmon in Oregon. Your planning team has decided to use this information in your planning process so you have to get it into the data tables that CLUZ will use to keep track of all data. To do this:

Step 1: Open the main view (CLUZ-Oregon).

Step 2. We will first add a data layer representing the occurrence of Chinook salmon in this

part of Oregon. To do this, first click the add data button in the ArcView menu.

Step 3. Navigate to the following file path:

C:\data\fwcb8452\Oregon\spp\ And select the file: f5028-chinook_salmon.shp

You should see this new data layer appear at the top of the “Table of Contents” panel. Ativate this layer and have a look at the distribution of Chinook salmon in the planning landscape.

Step 3. On the CLUZ menu, select convert themes to abundance data

Step 4. From the list of options, select ‘F5028-chinook_salmon’ and then press OK.

Step 5. In the feature prefix prompt, type “f” (we’ll use “f” for fish but anything will do here as long as it starts with a letter) and then press OK.

Step 6. Next CLUZ asks you if you would like to convert the area values that will be assigned to this feature. Because our data are in meter coordinates and we will be discussing options in terms of kilometers, choose Yes to indicate that we do want to convert.

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Step 7. Now you are prompted to enter a divisor for our conversion. Since we are going from square meters to square kilometers, enter 1,000,000 here and press OK.

Step 8. Finally, you are asked to indicate our preferred decimal precision for the area calculations. Since we are working with a fairly broad brush, we’ll set this to zero and press OK.

CLUZ will now update both the Abundance and Target tables with the data we have

supplied. You can check this by clicking the and buttons and examining these tables. There should now be a seventh column labeled f5028 to represent Chinook salmon.

Step 9. Editing the target table entry.

As you can see in the target table, our new feature has an id number but no name. To give it a name that has some real-world meaning, we need to edit the target table. To do this, first open the target table. • Under the Table menu, click start editing.

• Select the text editing tool on the toolbar , click in ‘Name’ cell, and type in “Chinook salmon”.

• Update the other cells to match the values for other features. • Go back to the Table menu and select Stop Editing. • You will be prompted to save edits and click yes.

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Building a Map of Conservation Feature Richness It is often useful in a planning process to view the pattern of conservation feature richness across the landscape. CLUZ automates this process and will create a layer to display this information.

Step 1: Open the main view (CLUZ-Oregon).

Step 2. From the CLUZ menu, select count number of features in each planning unit.

A new layer should appear in the Table of Contents showing feature richness across the landscape. You may want to refer to this in subsequent exercises or outside of the lab so take a screenshot to refer to later.

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Exploring Options Manually with On-Screen Conservation Planning

Planning Challenge #1 Manual Planning: Single Occurrence Representation Now we need to get down to business with the process of planning. For starters, we will attempt to design a conservation landscape that protects at least one occurrence (one unit) of each species in a spatially efficient way. This means that a unit in which more than one conservation feature occurs will often be a better choice than one in which only one feature occurs. To begin putting together a conservation portfolio, follow the steps below.

Step 1. Navigate to the main map view.

Step 2. Click to open the Change Status interface.

Step 3. You may want to make things easier to access by adjusting the windows so that you can simultaneously view the main map and the Change Status interface. At this point, your screen should look something like this

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Step 5. Make sure “planning unit” theme is ‘active’ (e.g. click on the name of this theme so that there is a beveled box around it).

Step 4. Using the reference maps in this packet (or by viewing the species layers by toggling the layers in the Legend part of the screen), try to decide which units will contribute most effectively to the goal of having each species represented by at least one polygon.

Step 5. When you have identified one or more units that you think you may want in your conservation portfolio, use the button on the main ArcView menu to select those units. They should be highlighted in yellow when selected. Multiple units can be selected by holding down the Shift key and clicking additional units. When the desired units are highlighted, you can temporarily “earmark” them for potential inclusion in your portfolio. To do this, click Set as earmarked from the Change Status

interface and confirm the change by clicking the button. You can adjust the status of units back to “available” at any time by selecting them and changing their status as described here.

Step 5. The Change Status panel will tell you how many of your targets have been met but it will not show whether you have gone over the minimum representation target. To

check on this, click the target table button and check the “conserved” column.

A perfectly efficient solution should show 648 km2 protected for each feature.

Step 6. When you arrive at your best attempt at representation and minimal cost, Take a screenshot to refer to later.

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Planning Challenge #2: Manual Planning: Minimizing Cost In this challenge, we will attempt to design a conservation landscape that protects at least one occurrence (one unit) of each species in a cost effective way. We thus have 2 simultaneous goals: representing each species with at least one planning unit and minimizing our cost.

Step 1. Proceed using the same procedure as above but use the following method to periodically check the cost of your portfolio.

Step 2. Select the show portfolio cost and area details option from the CLUZ menu to check the area, cost and feature representation status of your current portfolio.

An efficient solution should show 648 km2 protected for each feature **and** be as low-cost as possible.

Step 3. When you arrive at your best attempt at representation and minimal cost, Take a screenshot of your map **and** the portfolio cost and area details to refer to later.

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Planning Challenge #3: Manual Planning: Planning Within a Fixed Cost Limit Imagine a situation in which you could spend as much as Oregon’s current protected area is worth to completely overhaul the existing system. You are not limited by the current protection regime—only by the cost of land which varies dramatically throughout the state. In this challenge, you have 354 million dollars to spend to try to achieve a representative, spatially efficient, and cost efficient conservation portfolio.

Step 1. Proceed using the same procedure as above.

Step 2. Periodically, select the show portfolio cost and area details option from the CLUZ menu to check the area, cost and feature representation status of your portfolio.

An efficient solution should show 648 km2 protected for each feature **and** not exceed 354 million dollars.

Step 3. When you arrive at your best attempt at representation within the cost threshold, Take a screenshot of your map **and** the portfolio cost and area details to refer to later.

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Planning Challenge #4: Manual Planning: Representing 10% of the known distribution of each feature. This challenge is similar to Challenge one except that instead of just representing 1 hexagonal unit of each species, your conservation portfolio goal is 10% representation of each species. This complexifies the problem a bit. We will first adjust the targets within the target table to reflect this and then will proceed to manually search for an efficient solution.

Step 1. Make the entire planning landscape available using the change status panel as you have done above.

Step 2. Adjust the target table to reflect your 10% targets. To do this:

• Open the target table using the button. • While the table window is active, make the table editable by selecting Table

Start Editing. • Select the ‘target’ column by clicking in the title cell at the top of that column.

• Now click the calculate button on the ArcView toolbar and the ‘Field Calculator’ window will open. You will notice that above the main formula box is the text ‘[Target =]’ indicating that whatever you put in the box will define the new value for the ‘Target’ column.

• Double click “Total” in the “Fields” list to make it appear in the formula window. • Double click ‘*’ in the ‘requests’ window to add it to the formula window. This

represents ‘multiply by’. • Type ‘0.1’ after the ‘*’ to indicate that we want this column to be 10% of the total. • Click OK. This will assign the quantity specified by our formula to each row (e.g.

each conservation feature.

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• Save your edits to this table by selecting Stop Editing and then answering YES when prompted to save edits.

Step 3. Proceed as above to systematically turning units to ‘earmarked’ status to try to satisfy the 10% target. You can check the status of the landscape at any time by

clicking the button.

Step 2. Periodically, select the show portfolio cost and area details option from the CLUZ menu to check the area, cost and feature representation status of your portfolio.

Your goal is to have all targets met as economically as possible.

Step 3. When you arrive at your most cost-efficient solution, Take a screenshot of your map **and** the portfolio cost and area details to refer to later.

Congratulations! You have reached the conclusion of the manual planning

challenges! - -

Planning with MARXAN

MARXAN Activity #1 Single Occurrence Representation Now that you have struggled with the challenges of constructing a conservation landscape manually, you will now call on the power of MARXAN to automate the process. For a great description of how MARXAN works, you should spend some time reviewing the pages at: http://www.mosaic-conservation.org/cluz/marxan_intro.html . The following steps will walk you through the process of using MARXAN to provide a solution to the same problem you were faced with in Challenge #1, e.g. designing a conservation landscape that protects at least one occurrence (one unit) of each species in a spatially efficient way.

Step 1. If necessary (if you are restarting ArcView for example), ‘reinitialize’ the CLUZ setup file. Do this by:

• selecting the first option on the CLUZ menu. At the next prompt, choose Open existing setup file.

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• Navigate to c:\data\fwcb8452\oregon\ and double click on the file ‘setup.clz’.

Step 2. Change the ‘cost’ column in the ‘planning unit’ theme so that all units have an equivalent cost (thus forcing MARXAN to minimize the number of units for the solution rather than the cost). To do this:

• Make the “planning unit” theme active by clicking on it’s name in the Table of contents pane.

• Open the data table associated with the “planning unit” theme by clicking the

table button in the ArcView toolbar. • Make the theme editable by selecting Theme Start Editing. • Select the ‘Cost’ column by clicking in the title cell at the top of that column.

• Now click the calculate button on the ArcView toolbar to open the “Field Calculator’ window.

• Enter the number ‘1’ in the formula box. • Click OK. This will assign the same simple cost to all planning units. • If necessary, change the ‘Target’ column in the ‘planning unit’ theme so that all

features have the same target of 645. To do this: o Select the ‘Target’ column by clicking in the title cell at the top of that

column.

o Now click the calculate button on the ArcView toolbar to open the “Field Calculator’ window.

o Enter the number ‘645’ in the formula box. o Click OK. This will assign the same target value to all planning units.

• Save your edits to this table by selecting Stop Editing and then answering YES when prompted to save edits.

Step 3. Ensure that you are starting with a “blank slate” on your landscape. This may mean changing cells that currently have “conserved” or “earmarked” status back to

“available”. Use the button to access the change status panel for this.

Step 4. Creating / Updating the MARXAN input files. Before we call on MARXAN to run, we need to make sure that a set of specially formatted files (that mirror the structure of the 3 special files explained above) are ready for the program to read. This can be accomplished by consecutively selecting each of the following from the CLUZ menu:

Create abundance.dat (after this option is selected, you will get a window confirming all the species you are working with. No need to select anything—just click OK here) Create target.dat Create unit.dat

You should get a confirmation message as each table is created. Step 5. Now we are ready to activate the MARXAN process. To do this, select RUN MARXAN

from the CLUZ menu and you will be presented with the following interface:

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Note: This interface actually constrains our input to only a fraction of the possible

MARXAN settings. This is fine as we actually want the default MARXAN settings at this point.

Step 6. Adjust the number of iterations and runs. For consistency, we will be using values of 100,000 iterations and 100 runs. You can type these values right into the appropriate boxes.

Step 7. Accept all other settings by hitting the Run button. This will trigger the following confirmation dialogue:

• Click Yes to indicate that these have been updated • You should see a DOS window getting really active as MARXAN cycles through

a lot of calculations. If all goes well, you should see 2 new data layers appear in the Table of Contents pane.

Note: for clarity, you may want to move the “Oregon_pu_bnd” layer above the MARXAN output layers so you can see the planning unit boundaries.

Step 8. Viewing results. There are a couple of ways to view the outputs of MARXAN. First, the two newly created layers give us a graphical view of what is going on:

• The multi-color layer added to the Table of Contents pane represents the frequency with which individual units have been selected in our 100 runs. This has been proposed as a measure of rreplaceability.

• The pink color layer represents MARXAN’s best solution for achieving the targets we have set.

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Clicking the button will bring up a summary table displaying how the current MARXAN output does with regard to achieving the set targets.

Step 9. Take a screenshot of your map to refer to later.

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MARXAN Activity #2 Cost-efficient Representation This activity will somewhat parallel the manual planning challenge #2.

Step 1. Change the ‘cost’ column in the ‘Planning units’ theme back to estimated real-world values. To do this:

• Make the “planning unit” theme active by clicking on its name in the Table of contents pane.

• Open the data table associated with the “planning unit” theme by clicking the

table button in the ArcView toolbar. • Make the theme editable by selecting Theme Start Editing. • Select the ‘Cost’ column by clicking in the title cell at the top of that column.

• Now click the calculate button on the ArcView toolbar to open the “Field Calculator’ window.

• Double click the ‘Cost_mil’ field in the ‘Fields’ window to add it to the formula window.

• Click OK. This will assign the estimated real-world values back to each planning unit.

• Save your edits to this table by selecting Stop Editing and then answering YES when prompted to save edits.

Step 2. Ensure that you are starting with a “blank slate” on your landscape. This may mean changing cells that currently have “conserved” or “earmarked” status back to

“available”. Use the button to access the change status panel for this.

Step 3. Update the MARXAN input files with the Create abundance.dat, Create target.dat, and Create unit.dat options on the CLUZ menu (as detailed above).

Step 4. Activate the MARXAN dialogue box by selecting RUN MARXAN from the CLUZ menu.

Step 5. Adjust the number of iterations (100000) and runs (100).

Step 6. Accept all other settings by hitting the Run button.

Step 7. Examine the MARXAN outputs by inspecting the output layers and clicking the button.

Step 8. Take a screenshot of your map to refer to later.

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MARXAN Activity #3 Cost-efficient Representation with 10% of Total Distribution Targets This activity will somewhat parallel the manual planning challenge #4.

Step 1. Adjust the target table to reflect your 10% targets as you did in Manual Planning Challenge #4. To do this:

• Open the target table using the button. • While the table window is active, make the table editable by selecting Table

Start Editing. • Select the ‘target’ column by clicking in the title cell at the top of that column.

• Now click the calculate button on the ArcView toolbar and the ‘Field Calculator’ window will open. You will notice that above the main formula box is the text ‘[Target =]’ indicating that whatever you put in the box will define the new value for the ‘Target’ column.

• Double click “Total” in the “Fields” list to make it appear in the formula window. • Double click ‘*’ in the ‘requests’ window to add it to the formula window. This

represents ‘multiply by’. • Type ‘0.1’ after the ‘*’ to indicate that we want this column to be 10% of the total. • Click OK. This will assign the quantity specified by our formula to each row (e.g.

each conservation feature. • Save your edits to this table by selecting Stop Editing and then answering YES

when prompted to save edits.

Step 2. Ensure that you are starting with a “blank slate” on your landscape. This may mean changing cells that currently have “conserved” or “earmarked” status back to

“available”. Use the button to access the change status panel for this.

Step 3. Update the MARXAN input files with the Create abundance.dat, Create target.dat, and Create unit.dat options on the CLUZ menu (as detailed above).

Step 4. Activate the MARXAN dialogue box by selecting RUN MARXAN from the CLUZ menu.

Step 5. Adjust the number of iterations (100000) and runs (100).

Step 6. Accept all other settings by hitting the Run button.

Step 7. Examine the MARXAN outputs by inspecting the output layers and clicking the button.

Step 8. Take a screenshot of your map to refer to later.

- -

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References

Arthur, J. L., J. D. Camm, R. G. Haight, C. A. Montgomery, and S. Polasky. 2004. Weighing conservation objectives: Maximum expected coverage versus endangered species protection. Ecological Applications 14:1936-1945.

Arthur, J. L., R. G. Haight, C. A. Montgomery, and S. Polasky. 2002. Analysis of the threshold and expected coverage approaches to the probabilistic reserve site selection problem. Environmental Modeling & Assessment 7:81-89.

Ball, I., and H. Possingham 2000. Marxan (v1.8.2): Marine reserve design using spatially explicit annealing. Great Barrier Reef Marine Park Authority.

Calkin, D. E., C. A. Montgomery, N. H. Schumaker, S. Polasky, J. L. Arthur, and D. J. Nalle. 2002. Developing a production possibility set of wildlife species persistence and timber harvest value. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere 32:1329-1342.

Camm, J. D., S. K. Norman, S. Polasky, and A. R. Solow. 2002. Nature reserve site selection to maximize expected species covered. Operations Research 50:946-955.

Csuti, B., S. Polasky, P. H. Williams, R. L. Pressey, J. D. Camm, M. Kershaw, A. R. Kiester, B. Downs, R. Hamilton, M. Huso, and K. Sahr. 1997. A comparison of reserve selection algorithms using data on terrestrial vertebrates in Oregon. Biological Conservation 80:83-97.

Polasky, S., J. D. Camm, and B. Garber-Yonts. 2001. Selecting biological reserves cost-effectively: an application to terrestrial vertebrate conservation in Oregon. Land Economics 77:68-78.

Polasky, S., E. Nelson, J. Camm, B. Csuti, P. Fackler, E. Lonsdorf, D. White, J. Arthur, B. Garber-Yonts, R. Haight, J. Kagan, C. Montgomery, T. Starfield, and C. Tobalske. in review (2007). Where to put things? Spatial Land Management to Sustain Biodiversity and Economic Production. Biological Conservation.

Polasky, S., E. Nelson, E. Lonsdorf, P. Fackler, and A. Starfield. 2005. Conserving species in a working landscape: Land use with biological and economic objectives. Ecological Applications 15:1387-1401.

Smith, R. 2000. Conservation Landuse Zoning Extension for ArcView GIS (CLUZ), Kent, UK.

Szentandrasi, S., S. Polasky, R. Berrens, and J. Leonard. 1995. Conserving Biological Diversity and the Conservation Reserve Program. Growth and Change 26:383-404.

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Appendix 1. The CLUZ user interface CLUZ and ArcView provide several handy buttons that allow us to view and manipulate relevant data. Here we highlight several of these that will be necessary for the work we will be doing.

Table button – opens the table associated with the active map layer in ArcView

Select tool – select this button and then click on a planning unit to select it. To select multiple units, click this and a summary of what features occur there

Abundance table button – opens the abundance table for viewing.

Target table button – opens the target table for viewing

MARXAN Result summary button – A snapshot of targets protected by a MARXAN solution

Planning Unit Status Editor – Opens a user interface that allows changing the protection status of planning units.

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Select rectangle This tool is identical to the one found in the View and allows the user to select rectangular blocks of units by left-clicking on the mouse, holding the mouse button down, moving the mouse icon to a new position and then releasing the mouse button. Several blocks of units can be selected at once by pressing down on the shift button on the keyboard whilst selecting a new block.

Select polygon This tool allows the user to select irregularly shaped blocks of units by digitising the boundary of a polygon of the required block. The user must left-click once to start digitising and then set the boundary of the polygon by continue to left click. Double-clicking on the left mouse button finishes the process and several blocks can be selected at once by pressing down on the shift button on the keyboard whilst selecting a new block.

Build query The build query tool allows planning units to be selected based on their attributes described in the planning unit theme table. The build query dialog box is exactly the same as that accessed from the Query Builder button in the View and allows the user to select new sets of planning units.

The status section of the Change Status panel allows you to specify the value you want to set by choosing the relevant radio button. Turning off the Allow changes in conserved and excluded status check box means that only the status of Earmarked and Available units can be changed. Turning on the check box allows the user to change units to Conserved or Excluded status and also allows the status of Conserved or Excluded units to be modified. This should only be done at the beginning of the planning process.

Best to Earmarked button – automatically assigns MARXAN best output to “Earmarked” Status.

Abundance tool – select this button and then click on a planning unit to

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show a summary of what features occur there.

Similarity tool – Choosing this tool and clicking on an Earmarked planning unit identifies Available units that are most similar to the Earmarked unit in terms of achieving the conservation feature targets. These available units would be the best to swap with the Earmarked units in the portfolio. This tool will also identify whether an Earmarked unit could be removed from the portfolio without affecting the number of conservation targets that have been met.

…And a handy set of automated functions available from the menu. A few of the functions we will use are described in greater detail below:

This function initiates a file that tells CLUZ where your keeping all of the files it needs to run.

This function with automatically update the target table to reflect changes in protection.

This function automates the process of bringing feature distribution into CLUZ and updating the necessary tables.

This will give you a snapshot of how you’re doing with regard to the cost and effectiveness of your current portfolio.

These functions are used to convert the tables that CLUZ uses to keep track of everything into tables that MARXAN can use to perform calculations.

This function will initiate a MARXAN run and prompt you for the necessary information.

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Appendix 2. Data Sources and Notes Species Data These maps were produced by the Oregon Natural Heritage Program as part of their statewide biodiversity gap analysis program. As you can see, the occurrence of a given species has been simplified so that it is expressed as either present or absent within large hexagonal units. Protection These data are adapted from a four category management system developed in Scott et al. 1993, Edwards et al. 1995, and Crist et al. 1995. In this case, categories 1 and 2 are combined and categories 3 and 4 are combined to yield the following two categories: Strong Protection. Includes:

Status 1: An area having permanent protection from conversion of natural land cover and a mandated management plan in operation to maintain a natural state within which disturbance events (of natural type, frequency, and intensity) are allowed to proceed without interference or are mimicked through management.

Status 2: An area having permanent protection from conversion of natural land cover and a mandated management plan in operation to maintain a primarily natural state, but which may receive use or management practices that degrade the quality of existing natural communities.

Weak/No Protection. Includes:

Status 3: An area having permanent protection from conversion of natural land cover for the majority of the area, but subject to extractive uses of either a broad, low-intensity type or localized intense type. It also confers protection to federally listed endangered and threatened species throughout the area.

Status 4: Lack of irrevocable easement or mandate to prevent conversion of natural habitat types to anthropogenic habitat types. Allows for intensive use throughout the tract. Also includes those tracts for which the existence of such restrictions or sufficient information to establish a higher status is unknown.

Cost Excerpted from: Polasky, S., J. D. Camm, and B. Garber-Yonts. 2001. Selecting biological

reserves cost-effectively: an application to terrestrial vertebrate conservation in Oregon. Land Economics 77:68-78. Details of the methods used to find the average land value figures for the 289 sites in this study are described in Garber-Yonts and Polasky (1998). For private land, computerized records of assessed market values were available for a majority of counties in Oregon. However, such records were not available for a number of counties in eastern Oregon, which limited our study area to roughly the western two-thirds of the state. For public land, no equivalent measure of market value exists. Instead, for the value of public land we estimated the potential net present value of resources generated on public land, using forest inventory and productivity data and livestock forage productivity. Public lands where commodity production is not allowed, such as wilderness areas and national parks, were assumed to have no opportunity costs. Urban land, de® ned as land within urban growth boundaries, which are required by Oregon land use laws for every city and town in Oregon, and tribally owned land were excluded from the analysis.

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Appendix 3. Reference Maps

Major Ecoregions

Elevation

Land Cost (Millions of dollars / unit)

Strong Protection

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Conservation Feature Richness

Snowy Plover

Northern Spotted Owl

common kingsnake

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wolverine

Columbia torrent salamander

Chinook salmon

shortnose sucker

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Reference Map of the Planning Landscape showing estimated cost (in millions of dollars) of each planning unit.

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Appendix 4. Conservation Planning Glossary Complementarity: The value of a prospective planning unit to a conservation

portfolio given the existing protected area system. Planning units containing conservation features that are not well protected will complement the existing protected area system well.

Conservation feature: A species, land cover type, or other entity that is considered during a conservation planning exercise.

Conservation portfolio: The array of protected and unprotected conservation features across a planning landscape. Software such as MARXAN allows planners to generate several portfolios for comparison.

GAP Analysis: An evaluation of a region’s protected area system with regard to its protection of conservation features.

Heuristic algorithm: An algorithm that finds a near optimal solution. Heuristic algorithms, such as the technique used by MARXAN, are necessary for problems in which finding the optimal solution is computationally intractable.

Irreplaceability: The relative conservation value a planning unit has in a conservation portfolio. Planning units that are selected repeatedly during an iterative algorithm have high irreplaceability. Planning units with the only occurance of a target will inevitably have high irreplaceability.

Maximal cover problem: conservation planning problem where the goal is to achieve as many conservation targets as possible subject to a finite cost constraint.

NP (nondeterministic polynomial) problems: those problems for which the number of operations required to find the best solution grows as a polynomial function of the size of the input. NP-hard problems: Intractable NP problems for which the computational

complexity of finding a single best solution defies even modern computer capabilities. For an excellent example of how quickly computational complexity adds up—even with seemingly simple problems, check out the classic “traveling salesman” problem at: http://students.ceid.upatras.gr/~papagel/project/tspprobl.htm

Planning units: The spatial units used during a conservation planning exercise. For our exercise, we are using hexagons 648 square meters in size.

Objective function: quantitative function collapsing costs and benefits into a single metric for comparing possible conservation scenarios.

Representation: The extent to which a conservation feature is protected with regard to the target for that feature. During a planning exercise it must be decided how many different instances of a conservation feature must be protected (i.e., the target) to provide adequate representation.

Set cover problem: conservation planning problem where the goal is to minimize the total cost while meeting or exceeding conservation targets.

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Simulated annealing: a stochastic approach to “mining” for optimal sets. The term gets it’s name from the annealing process in metallurgy in which a controlled temperature regime is used to optimize the strength of bonds that are formed as molten metal hardens.

Surrogate: Conservation features that serve to represent the conservation needs of a broader group. For example, in our exercise the wolverine (Gula gula) is used to represent wide ranging carnivore species that require large tracts of contiguous forest cover.

Systematic conservation planning: Systematic conservation planning seeks to identify conservation strategies that achieve conservation goals in ways that maximize spatial or economic efficiency (Sarker et al. 2006).

Target: a specific, measurable amount of a conservation feature of interest, often expressed as the area required to ensure feature persistence.

Vulnerability: The likelyhood that a planning unit will be converted to an incompatible land use in the future. Vulnerability is used to help prioritize land acquisition.