Marxan Tutorial Compress

8/17/2019 Marxan Tutorial Compress

1/114


2/114

• Protected areas are now required to be representative of biodiversity.

• Selection of protected areas in many places has historically beenopportunistic.

• Many reserves were originally designated for their scenic beauty, culturalsignificance, lack of economic value or to protect a few charismaticflagship species.

• These PAs do not adequately represent the diversity of ecosystems,leading to duplication in the protection of some habitats and species andinadequate protection of others.

• Selection techniques improved with the understanding that the range of biodiversity should be represented.

• These techniques often concentrate on areas rich in well-studied habitatsand species, and do not provide quantitative representation or repeatability.

Biodiversity Representation


3/114


4/114

• It is vital to include expert and stakeholder knowledge in the process,whilst allowing quantitative representation and repeatability.

• Software such as MARXAN has been designed to implementalgorithms that allow such methodologies.

• They can include many parameters believed to be important in biologically meaningful priority area design.

• These include multiple representations, patch size control andminimum and maximum separation distances.

• The techniques can also offer many alternative systems that can benegotiated whilst maintaining all goals.


5/114

• MARXAN delivers decision support for selecting networks of priority conservation sites.

• A region is divided into smaller areas known as planning units, to

allow comparison between the areas through quantification of theircharacteristics.

• The selection of any planning unit over another involves evaluating it

with regards to all the planning units in the area under consideration.

• One unit with several valuable features on its own may or may not bethe best choice overall, depending on the distribution and replication

of those features in other planning units.

Reserve Design using Spatially Explicit Annealing


6/114

Marxan Utilization Worldwide

• Marxan was developed to meet the decision support needs of the GreatBarrier Reef Marine Planning Authority (GBRMPA) in theirrepresentative areas program that has rezoned the GBR. Other examplesinclude:

• British Columbia (Canada)

• Galapagos Islands (Ecuador)

• Gulf of California (Mexico)

• Joint Nature Conservancy Council (UK)

• The Florida Keys National Marine Sanctuary (USA)

• Channel Islands National Marine Sanctuary (USA)• South Australia, University of Queensland

• Northern Gulf of Mexico (USA)

• Trough-Georgia Basin (USA/ Canada)

• North East Atlantic (USA / Canada)


7/114

• Marxan can offer decision support for teams of experts choosing between thousands of planning units and many biodiversity targets.

• It selects a portfolio of spatially cohesive units that meet a suite of

biodiversity goals whilst minimizing the ‘cost’.

• The cost of the portfolio consists of a weighted sum of planning unitcost, boundary length and penalties for not representing biodiversity

targets to their user defined goal.

• A portfolio consists of a network of planning units, some of which areclustered into potential sites, with others serving to connect isolatedareas of existing or intended conservation management.

Designing a Portfolio


8/114


9/114

The MARXAN algorithm:Objective Function

Or: Total Portfolio Cost =

(cost of selected sites) +

(penalty cost for not meeting conservation goals) +

(cost of spatial distribution of the selected sites).

The algorithm attempts to minimize the total cost of a portfolio:


10/114

• MARXAN uses an ‘simulated annealing’ optimization algorithm toselect a portfolio.

• The algorithm is based on iterative improvement with stochasticacceptance of bad moves.

• This allows the algorithm to choose less than optimal planning units

early in the process that may allow for better choices and overall portfolio later.

• As the program progresses, the criteria for a good selection gets

progressively stricter, until finally the portfolio is built.

Simulated Annealing


11/114

• Planning units can be any shape or size, but appropriate units should bedesigned according to the available target data and to best facilitateconservation efforts in the priority sites identified.

• Planning units can be natural, administrative or arbitrary sub divisions ofthe land and seascape.

• The units should be small enough to reflect differences betweenfragmented and non fragmented habitats or distributions, but large enough

to reflect quantitative differences between units.

• Data on distributions within very small units becomes presence / absenceinformation and does not reflect differences regarding the size of patches

or the co-existence of biodiversity elements or ‘targets’ between the units.

Planning Units


12/114


13/114

Measures can be incorporated by careful setup of targets and goals. For example:

• Representation : quantitative representation of all targets.

• Multiple sites : a minimum number of sites can be stipulated and/or a minimumseparation distance to lower stochastic occurrence risk.

• Connectivity : a maximum separation distance can be stipulated and sites thoughtto be connected can be split into separate sub-targets ensuring representationwithin all connected sites.

• Resistance or resilience indicators: (if map-able eg shaded, well mixed, wellflushed areas etc) if a high level of confidence is achieved, these can beincorporated as separate (fine filter) targets.

• Resistant or resilient sites: can be incorporated as separate targets.

Factors That Can Be Included In Portfolio Analysis


14/114


15/114

• Information on the number of times a planning unit is chosen in a priority area network, and the best network can be mapped using GIS.

• Planning units that are chosen more than 50% of the time can bethought of as being essential for efficiently meeting biodiversity goals.Areas with lower irreplaceability are not unimportant but are moreinterchangeable with other similar planning units.

• Many design scenarios can be explored, and flexible units can beremoved and alternatives found.

Outputs


16/114


17/114


18/114


19/114


20/114


21/114


22/114


23/114


24/114


25/114

Portfolio Selection

Marxan Inputs


26/114

1) Target abundance per planning unit

2) Goals

3) Cost per planning unit4) Planning unit boundary lengths (optional)

5) Biological constraints (optional)

6) Spatial clustering (optional)

7) Species penalty factors (optional but extremely important)

Marxan Inputs


27/114

1) Target Abundance per Planning Unit


28/114

30%

20%

2) Goals


29/114

• The cost is a relative value applied to planning units such thatsome may be more difficult or “expensive” to set aside than others.

• Marxan attempts to minimize the total ‘cost’ of the portfolio. Thisconsists of cost, boundary length and penalties for not representing

the targets to the goals.

Cost can represent:

• Actual or modeled cost of planning unit area

• Cost of lost opportunity (e.g. fishing yield etc)

• Threat

• Inverted resilience indicators

• Any other measure to minimize in the portfolio as a whole.

3) Planning Unit Cost


30/114


31/114

Measures can be introduced to assure the portfolio contains targetsthat have:

• a minimum target patch size

• minimum separation distance between patches (avoidance ofstochastic disasters)

• maximum separation distance (connectivity)

• minimum number of patches.

4) Biological Constraints


32/114

• The species penalty factor (SPF) sets the relative importance oftarget representation when selecting areas.

• A spf value should be chosen that allows an acceptable number oftargets to reach goal representation.

• Testing is required to calculate this value.

5) Species Penalty Factors


33/114

• Marxan facilitates the choice of a portfolio with increased spatialclustering of planning units (PUs).

• It can be set to minimize the boundary length of the portfolio, whichclusters the planning units together.

• This effect can be set to have a strong or a only slight effect.

• Clustering the sites can require an increase in the number of PUsnecessary to meet all representation goals, but is thought to increasemanageability of sites and likelihood of persistence of biodiversity

targets.

6) Spatial Clustering of Planning Units


34/114

Increasing PU Clustering

0

0.0001

0.0005

0.001

0.01

0.1


35/114


36/114

• Data compatibility: Data should ideally be of the samescale or resolution, of a similar age and of similar accuracy.

• Screening eg patch size, health, threat etc.

• Stratification biologically diverse parts of targets should bestratified.

• Target weighting: fine and coarse filter targets should beweighted carefully and have goals appropriate to the aims ofthe portfolio.

Pre MARXAN Target Data

Preparation


37/114


38/114

Dominica

Caribbean Ecoregional Assessment


39/114


40/114

Exercise 1: An introduction to MARXAN


41/114

This first exercise describes how to use marxan to design a portfoliousing case study data.

• The first section examines the target distribution and planning unitshapefiles in ArcView, and the marxan input files in notepad.

• The second section describes setting up a marxan run using input files

that have been prepared from this data, running the algorithm andmapping the results.

Exercise 1: An introduction to MARXAN


42/114

Follow the instructions A1-6 to copy MARXAN and the tutorial data

onto your computer and then view the data in ArcView.


43/114


44/114

Input Tables

Target Details File : spec.dat

Target Abundance: puvspr.dat


45/114

Boundary file : bound.dat

Planning unit file: pu.dat

Input Tables


46/114

Block definition file(optional but useful for setting

proportional goals)

Input Tables


47/114


48/114

B1 ) MARXAN is set up using the Inedit program openedfrom windows explorer


49/114


50/114

B3 )


51/114

B4 )


52/114

B5 )


53/114

B6 )


54/114


55/114

B8 )


56/114

B9 ) MARXAN is opened and run by executing themarxan.exe file.


57/114


58/114

B10 ) Examine the outputs:

Best - the units in the ‘best’ portfolio

Sum - summary of each run,including whether all goals have

been met.


59/114


60/114

Section C : Mapping MARXAN Results


61/114

Mapping Irreplaceability


62/114


63/114

C2 ) Join tables to the planning unit attribute table.


64/114

C3 ) Display the pu shapefile using graduated color.

Double click


65/114

C3 cont. ) Use the run1_irr field as the classification field.


66/114


67/114

Mapping the ‘best’ Portfolio


68/114


69/114

C4 cont. ) Use the run1_bst field as the classification field.


70/114


71/114

C5 ) Run the algorithm again using a boundary length

modifier (BLM) and view the results of increased clustering.

C6 ) Run the algorithm with the protected area planning units

locked into the portfolio. Compare the results to identifywhether the present PA system is efficient or meets allconservation goals.

C7 ) Run the algorithm for 200 runs and compare the ‘best’ portfolio cost with the best run of 100 runs. Have 200 runsidentified a more efficient portfolio?


72/114

Exercise 2

Creating Input Files using Tutorial Data


73/114


74/114

MARXAN Files:

1) Target Abundance File: puvspr.dat

2) Species File : spec_goals.dat

3) Planning Unit File: pu.dat

4) Boundary File: bound.dat

5) Block Definition File: block.dat (optional )


75/114

D) Target Abundance File: puvspr.dat(Planning Unit versus Conservation Feature File)


76/114

D2 ) A dbf table is created that contains the id’s of the PUsfrom the planning unit file.

(puvspr.dat)


77/114

D2-D4 ) This table is used by the CLUZ abundance ArcViewscript to produce an abundance table using the target

shapefiles and the planning unit shapefile.

(puvspr.dat)


78/114

(puvspr.dat)

D5 ) View the abundance dbf table.


79/114

D6 ) The CLUZ puvspr ArcView script is used to convert theabundance table into the MARXAN puvspr file format.

(puvspr.dat)


80/114

D7 ) Resulting puvspr_abun file.


81/114

E ) Species File : spec_goals.dat


82/114

Follow steps E1 to E3 to create the table containing a targetid column.


83/114

E4 ) Goals can be calculated using the abundance table

(spec.dat)


84/114

Follow steps E5 to E7 to complete the spec_goals file.

(spec.dat)


85/114

F ) Planning Unit File: pu.dat

) h l h f l d dbf bl


86/114

F1 ) The planning unit shapefile is exported to a dbf table.

(pu.dat)

F1 F4 ) Th bl b i l d i l d d


87/114

(pu.dat)

F1 – F4 ) The table can be manipulated in excel and saved asa csv file, then renamed to .dat.

F5 ) F ll F5 PU bl h id ifi ll PU


88/114

F5 ) Follow step F5 to create a PU table that identifies all PUswith over 50% of their area under a PA.


89/114

G) Boundary File: bound.dat

G1 G2 ) Th b d fil t i t A Vi i d t


90/114

G1-G2 ) The boundary file extension to ArcView is used tocreate the boundary file from the planning unit shapefileautomatically.


91/114

F ) Block Definition File: block.dat (optional )

F ll t H1 d H2 t t th bl k d fi iti fil


92/114

Follow steps H1 and H2 to create the block definition file:


93/114

Exercise 3 : Run marxan with new input files.


94/114

Follow steps J1 to J4 to run a series of marxan analyses with

varying parameters.

• Check that marxan will run successfully using the new files.

• View the effects of different parameters such as locking protected areas into the portfolio and increasing clustering.


95/114

Results


96/114

Effect of Increasing Boundary Length Modifier


97/114


98/114


99/114


100/114


101/114


102/114

BLM Number of Units in Best Number of TargetsPortfolio over Goal

0.001 593 16

0.01 579 16

0.03 567 18

0.06 550 18

0.1 547 17

0.5 674 22


103/114

0 50

100

150

200

250

300

350

% o

f G o a

l

M o i s t

E x t r u s i v e

R e e f C o l o n i z e d B e d r o c k M o i s t

A l l u v i a l

R e e f P a t c h R e e f i n d i v

R e e f L i n e a r R e e f

W e t l a n d F r e s h w a t e r W e t

e x t r u s i v e

L M w e t

e x t r u s i v e

D r y L i m e s t o n e

R e e f C o l o n i z e d P a v e m e n t W e t

a l l u v i a l D r y A l l u v i a l D r y E x t r u s i v e

M o i s t

I n t r u s i v e W e t

i n t r u s i v e

W e t l a n d T e r r e s t r i a l M a n g r o v e


W e t

u l t r a m

a f i c

M o i s t

u l t r a m

a f i c

M o i s t

s e d i m e n t a r y

R a i n e x t r u s i v e

L M w e t

i n t r u s i v e

W e t

l i m e s t o n e

L M r a i n i n t r u

s i v e

L M w e t

a l l u v i a l D r y I n t r u

s i v e


L M r a i n e x t r u s i v e

R e e f C o l o n i z e d P a v e m e n t w i t h C

W e t


D r y s e d i m e n t a r y

M o i s t

l i m e s t o n e

L M w e t

u l t r a m

a f i c

R e e f S c a t t e r e d C o r a l - R o c k R a i n a l l u v i a l

D r y u l t r a m

a f i c R a i n i n t r u

s i v e

L M w e t

l i m e s t o n e

Target

Proportion of Goal Held in Portfolio; 100 Runs BLM 0.00116 Targets Over Goal

Portfolio = 593 Units


104/114

0 50

100

150

200

250

300

350

%

G o a

l

M o i s t

E x t r u s i v e

M o i s t

u l t r a m

a f i c

W e t

e x t r u s i v e

R e e f

P a t c h R e e f i n d i v

M o i s t

A l l u v i a l W e t

a l l u v i a l

W e t l a n d F r e s h w a t e r

D r y E x t r u s i v e

R e e f

L i n e a r R e e f


R e e f

C o l o n i z e d B e d

r o c k

W e t l a n d T e r r e s t r i a l M a n g r o v e

R e e f

L i n e a r R e e f

W e t

i n t r u s i v e

W e t

u l t r a m

a f i c

L M w e t i n t r u s i v e

D r y A l l u v i a l

W e t

l i m e s t o n e

R e e f

C o l o n i z e d P a v e m e n t

M o i s t

I n t r u s i v e

R e e f

C o l o n i z e d P a v e m e n t w i t h C

L M w e t e x t r u s i v e

R e e f

S c a t t e r e d C o r a l - R

o c k

W e t

s e d i m e n

t a r y

M o i s t

s e d i m e

n t a r y

D r y I n t r u

s i v e

R a i n e x t r u

s i v e

R e e f

L i n e a r R e e f

L M r a i n e x t r u

s i v e


L M w e t a l l u v i a l

L M w e t u l t r a m

a f i c

M o i s t

l i m e s t o n e

L M r a i n i n t r u

s i v e

R a i n a l l u v i a l

R a i n i n t r u

s i v e

D r y u l t r a m

a f i c

L M w e t l i m e s

t o n e

Target

Proportion of Goal Held In Portfolio; 100 runs BLM 0.0116 Targets Over Goal



105/114

0 50

100

150

200

250

300

350

%

G o a

l


W e t

e x t r u s i v e

M o i s t A l l u v i a l

R e e f

C o l o n i z e d B e d r o c k

M o i s t

E x t r u s i v e


R e e f

C o l o n i z e d P a v

e m e n t

R e e f

L i n e a

r R e e f

R e e f


D r y E x t

r u s i v e

W e t l a n d T e r r e

s t r i a l

L M w e t e x t

r u s i v e

W e t

i n t r u s i v e

W e t

s e d i m e

n t a r y

M a n g r o v e

R e e f


w i t h C

W e t

u l t r a

m a f i c

W e t

l i m e s t o n e

M o i s t

I n t r u s i v e

R e e f

L i n e a

r R e e f

D r y I n t r u s i v e


L M w e t i n t r u s i v e

W e t a l l u v i a l

M o i s t

u l t r a m a f i c


M o i s t

l i m e s t o n e

R e e f

L i n e a

r R e e f


M o i s t



L M w e t u l t r a m a f i c

R e e f

S c a t t e r e d C o r a l - R o c k


L M r a i n i n t r u s i v e


R a i n i n t r u s i v e

D r y u l t r a m a f i c

L M w e t l i m e

s t o n e

Target

Proportion Of Goal Held in Portfolio; 100 runs, BLM 0.0318 Targets over Goal



106/114


107/114

0

50

100

150

200

250

300

350

%

G o a

l


W e t

e x t r u s i v e

M o i s t

E x t r u s i v e

R e e f




M a n g r o v e


R e e f


e m e n t

M o i s t

I n t r u s i v e

W e t

i n t r u s i v e

M o i s t

s e d i m e

n t a r y

D r y s e d i m e

n t a r y

R e e f


D r y E x t r

u s i v e

R e e f

L i n e a r R e e f

W e t

u l t r a

m a f i c


W e t

l i m e s t o n e

W e t l a n d T e r r e s t r i a l

M o i s t

u l t r a m a f i c

R e e f


L M w e t e x t r u s i v e

L M r a i n i n t r u s i v e


R e e f

C o l o n i z e d P a v e m e n t w i t h C

D r y I n t r

u s i v e

L M w e t i n t r

u s i v e

R e e f

L i n e a r R e e f


W e t

s e d i m e

n t a r y

M o i s t

l i m e s t o n e


R e e f

L i n e a r R e e f



R a i n i n t r u s i v e

L M w e t l i m e

s t o n e

L M w e t u l t r a m a f i c

Target

Proportion of Goal Held in Portfolio; 100 Runs BLM 0.117 Targets Over Goal



108/114

0

50

100

150

200

250

300

350

%

G o a

l

M o i s t

E x t r u s i v e

W e t

e x t r u s i v e


R e e f



W e t l a n d

F r e s h w a t e r



M a n g r o v e

R e e f

L i n e a

r R e e f

R e e f


w i t h

C

M o i s t

u l t r a m a f i c

R e e f

P a t c h

R e e f i n d i v

W e t l a n d

T e r r e s t r i a l

R e e f


e m e n t

L M w e t

e x t r u s i v e

W e t

i n t r u s i v e

D r y E x t

r u s i v e

R e e f



M o i s t

I n t r u s i v e

W e t

u l t r a

m a f i c

R e e f

L i n e a

r R e e f

D r y s e d i m

e n t a r y

W e t

s e d i m e

n t a r y

R e e f

L i n e a

r R e e f

M o i s t


L M w e t

u l t r a m a f i c

W e t

l i m e s t o n e

L M w e t

i n t r u s i v e


D r y I n t r u s i v e

M o i s t

l i m e s t o n e


L M w e t

l i m e s t o n e

L M r a

i n i n t r u s i v e

L M r a

i n e x t r u s i v e

R a i n

e x t r u s i v e

R a i n

i n t r u s i v e

Target

Proportion of Goal Held in Portfolio; 100 Runs, BLM 0.522 Targets Over Goal



109/114

Analysis of Highly Irreplaceable Areas


110/114


111/114


112/114


113/114

Additional information can be found in the MARXAN


114/114

Additional information can be found in the MARXANmanual which can be downloaded with the program from:

http://www.ecology.uq.edu.au/?page=20882&pid=

Please register as a user!

Marxan Tutorial Compress

Documents

Transcript of Marxan Tutorial Compress