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mean) and a color ramp. Before clicking OK, press the Classify button to see the MEANstatistic histogram summary. The mean value of the MEAN field will be displayed in theClassification Statistics box. This represents the mean risk value for all the habitatpolygons. This number could be used as a cut off point in determining a threshold forhabitats which may be more impacted because of the intensity and influence distances ofthe underlying risk element features. For example, if the mean value is 12.55, all planningunits/polygons that exceed this may be high-risk candidate units, or potentially impactedat higher risk levels based on the ERS model and the spatial extents of the risk elementfeatures. One could exclude these features in the map view by clicking on Data Exclusionin the Classification dialog box, and specifying this threshold limit as a selection formula(e.g. “zstat.MEAN” > 12.55).1.3.4 Creating Freshwater and Marine Environmental Risk SurfacesAs previously mentioned, ERS models can also be customized to represent risks tohabitats in freshwater and marine realms. Experts in these realms must identify what riskelements are impacting the health of these systems and rank them accordingly in theirintensity value and influence distance. For example, freshwater habitats may be impactedby features such as sewage outfall locations, dams, water abstraction, and invasivespecies. Marine habitats may be impacted by fishing pressures/practices, ports, marinas,and land- and marine-based pollution.1.3.4.1 Freshwater ERS ModelsA flow accumulation model can beused to measure the impact ofdefined risk elements onfreshwater biodiversity by creatinga grid of accumulated riskintensities that flow into each cellof a watershed. To do this, theuser must first create an ERSmodel using expert-definedfreshwater risk element features(as described above), and thenspecify the output grid model asthe input source grid for the flowaccumulation function (SeeFLOWACCUMULATION gridfunction in ArcGIS Help). Thisfunction requires the user tospecify a flow direction grid whichindicates the direction water flowson a cell-by-cell basis, starting atTNC Protected Area Tools (PAT) Version 3.026The Nature Conservancy, August 2009
higher elevations and ridges moving downward, much like water moves across a slopedsurface. In order to define a drainage network, the steepest down-slope flow pathbetween each cell is identified between its eight neighbor cells.Once the flow direction is established, the intensity values of the combined freshwaterrisk elements (e.g. agriculture, urban areas, and sewage outfalls) can be accumulated asthe model runs from the top (ridges) to the bottom (outlet) of each watershed. The finalaccumulated grid surface calculates accumulated risk (in intensity values) upstream fromany point (cell) in the watershed. Output cells with a high flow accumulation are areas ofconcentrated flow and may be used to identify stream channels and high risk areas.Output cells with a flow accumulation of zero are local topographic highs and may beused to identify ridges. This method also permits users to calculate the total risk intensityby watershed, thus quantifying the risk on a watershed level. If freshwater experts thinkthat the high accumulated values are too high for large rivers, the effect can be lessenedby subtracting a dilution factor to parts of the watershed. A zonal max function can beapplied to locate the maximum flow accumulation value for each planning unit or uniquewatershed, thus quantifying the maximum flow accumulation value on a per unit basis.Step flow process that uses a Digital Elevation Model (DEM) and Environmental Risk Surface (ERS) as aweight grid for accumulating intensity values when assessing risk to freshwater and marine habitats. Inareas of low or zero slope, creating a suitable flow direction grid may be a challenge. Users may want topreprocess the DEM by “burning in” stream networks using a variety of tools available on the web (i.e.ArcHydro, AGREE). Rivertools is an exceptional hydrology modeling software with powerful tools forcreating stream networks and ordered watersheds with high precision.ArcHydro http://www.crwr.utexas.edu/giswr/hydro/index.htmlAGREE http://www.ce.utexas.edu/prof/maidment/gishyd97/terrain/agree/agree.htmRivertools Software http://www.rivix.comTNC Protected Area Tools (PAT) Version 3.0The Nature Conservancy, August 200927
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: unit or feature (i.e. polygon) must
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 and 42: This selection routine will choose
Page 43 and 44: 3.0 The MARXAN AlgorithmOne of the
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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