spatially representing south west catchments council ... · datasets identified by the working...

Spatially representing South West Catchments Council

priorities for biosequestration plantations and high

biodiversity planting under climate change.

Simon Neville

Ecotones & Associates

May 2014

Bio-sequestration component of the SWCC Climate Change Project - CCF002

Acknowledgements

Leonie Offer (SWCC) organised the working group and workshops, and has provided project

management throughout. Dr Paul Raper (DAFWA) carried out new analysis and provided land

capability datasets in very quick time, as well as assistance with the salinity datasets and

agricultural components of the project. James Houston (Gaia Resources) was effective and

diligent in providing the original set of SWCC datasets to the project and in sourcing new

datasets identified by the working group. Mike Christensen (SWCC) provided comments on a

draft. The working group worked well to come to terms with the concepts and issues they

faced.

Cover image

Final Planting Options for SWCC.

Please reference this document as

Neville, S. (2014). Spatially representing South West Catchments Council priorities for

biosequestration plantations and high biodiversity planting under climate change. Consultant’s

report for South West Catchments Council. Ecotones & Associates, Denmark., WA.

Limitations of Use

Datasets, criteria for decision-making and climate change projections, exhibit characteristics and properties which

vary from place to place and can change with time. The preparation of this project report involved gathering and

assimilating existing datasets, the results of modelling and other information—including opinions—about these

characteristics and properties, in order to better understand priorities for plantation locations, and to carry out the

project Brief. The facts and opinions reported in this document have been obtained by conducting workshops,

collecting opinions and understandings from a range of stakeholder, and interpreting these using a number of multi-

criteria models. They are directly relevant only to the purposes for which the project were carried out, and are

believed to be reported accurately. The models used are intended to provide indicative results only, and are

dependent on input parameters. Any interpretation or recommendation given in this document is based on

judgement and experience, and not on greater knowledge of the facts that the reported investigations may imply.

The interpretations and recommendations are opinions provided for the sole use by the South West Catchments

Council, in accordance with a specific Brief. Ecotones does not represent that the information or interpretation

contained in this document address completely all issues relating to plantation establishment In the South West

Catchments Council Region. The responsibility of Ecotones is solely to its client, the South West Catchments Council.

It is not intended that this report be relied upon by any third party. Ecotones accept no liability to any third party.

Executive Summary i

Table of Contents

1. OBJECTIVES OF THE PROJECT ........................................................................................ 1

1.1 Project Objectives .......................................................................................................... 1

2. PROJECT METHOD ....................................................................................................... 3

2.1 Modelling Methodology (MCAS-S) ................................................................................ 4

2.1.1 MCAS Requirements & Workflow ....................................................................................... 5

2.2 Project Process .............................................................................................................. 6

2.3 Component Framework................................................................................................. 7

2.4 Component Model Diagrams ........................................................................................ 8

2.4.1 Component 1 – Landscapes that need to be protected from Carbon Plantings ................. 8

2.4.2 Component 2 – Locations for Low-Biodiversity Carbon Plantings ....................................... 9

2.4.3 Component 3 – Identifying Areas of High Biodiversity Value/Conservation Value ........... 10

2.4.4 Component 4 – Locations for carbon plantings to enhance habitat corridors and protect

high biodiversity areas .................................................................................................................... 12

3. COMPONENT DETAILS ................................................................................................ 13

3.1 Component 1 – Landscapes that need to be protected from Carbon Plantings ......... 14

3.1.1 High Capability Agricultural Land ..................................................................................... 15

3.1.2 Projected Yield Sustainability ............................................................................................ 16

3.1.3 High Quality Agricultural Land .......................................................................................... 18

3.1.4 Growing Season Rainfall % Change .................................................................................. 19

3.1.5 Protection Zones for PDWSA ............................................................................................. 20

3.1.6 Remnant Vegetation ......................................................................................................... 21

3.1.7 Component 1 Output - Landscapes that need to be protected from carbon plantings .... 22

3.2 Component 2 – Locations for Low-Biodiversity Carbon Plantings .............................. 23

3.2.1 Potential Salinity ............................................................................................................... 24

3.2.2 Potential Salinity Areas ..................................................................................................... 30

3.2.3 WRRC Catchments for Salinity and Biodiversity ................................................................ 31

3.2.4 Low Capability Agricultural Land ...................................................................................... 32

3.2.5 Areas with Projected Yield Declines .................................................................................. 33

3.2.6 Low Value Agricultural Land ............................................................................................. 33

3.2.7 Remnant Vegetation ......................................................................................................... 34

3.2.8 Component 2 Output – Locations for Low-Biodiversity Plantings ..................................... 34

3.3 Component 3 – Identifying Areas of High Biodiversity Value/Conservation Value .... 36

ii Ecotones & Associates

3.3.1 Rare or Threatened Vegetation Types .............................................................................. 37

3.3.2 Naturalness ....................................................................................................................... 44

3.3.3 Community Diversity ......................................................................................................... 47

3.3.4 High Value Biodiversity Areas ........................................................................................... 48

3.3.5 Proximity to Threatened Species ....................................................................................... 49

3.3.6 Climate Refugia ................................................................................................................. 51

3.3.7 Size - Areas > 2 ha ............................................................................................................. 53

3.3.8 Component 3 Output –Areas with High Biodiversity or Conservation Value .................... 54

3.4 Component 4 – Locations for carbon plantings to enhance habitat corridors and

protect high biodiversity areas .................................................................................... 56

3.4.1 Proximity to High Biodiversity/Conservation values (Component 3) ................................ 57

3.4.2 Proximity to known biodiversity assets ............................................................................. 58

3.4.3 Rivers and buffer zones ..................................................................................................... 62

3.4.4 Proximity to Priority Linkages ........................................................................................... 65

3.4.5 Potential for infill. ............................................................................................................. 67

3.4.6 Component 4 Output - Locations for carbon plantings to enhance habitat corridors and

protect high biodiversity areas........................................................................................................ 70

4. RESULTS AND OUTPUTS ............................................................................................. 71

4.1 Component Maps ........................................................................................................ 72

4.2 C5 - Combining Components. ...................................................................................... 76

5. COMBINING THE COMPONENTS FOR DECISION SUPPORT ........................................... 80

6. PROJECT DELIVERABLES ............................................................................................. 85

7. APPENDICES .............................................................................................................. 86

7.1 Appendix 1 - GIS Datasets available in MCAS-S Format .............................................. 86

7.2 Appendix 2 - GIS Datasets Used in the SWCC modelling ............................................. 91

7.3 Appendix 3 - South West Catchment Council’s Biosequestration Working Group -

Terms of Reference ..................................................................................................... 93

8. REFERENCES .............................................................................................................. 95

Executive Summary iii

List of Figures Figure 1: SWCC area boundary and major towns ............................................................................... 2

Figure 2:Overall Component Design & Outcomes ................................................................................ 7

Figure 3: Component 1 – Protection from Carbon Plantings ................................................................. 8

Figure 4: Component 2 – Locations for Low-Biodiversity Carbon Plantings ............................................ 9

Figure : Component 3 – Areas of High Biodiversity Value/Conservation Value ..................................... 11

Figure 6: Component 4 – Location of Biodiversity Plantings ............................................................... 12

Figure 7: Classification Figures in MCAS-S ........................................................................................ 13

Figure 8: Component 1 MCAS-S Diagram ......................................................................................... 14

Figure 9: High Capability Agricultural Land ...................................................................................... 15

Figure 10: Projected Yield Sustainability ........................................................................................... 17

Figure 11: High Quality Agricultural Land ......................................................................................... 18

Figure 12: Growing Season Rainfall % Change .................................................................................. 19

Figure 13: Protection Zones for PDWSA ........................................................................................... 20

Figure 14: Remnant Vegetation mask .............................................................................................. 21

Figure 15: Component 1: MCAS-S Output ......................................................................................... 22

Figure 16: Component 2 MCAS-S Diagram ....................................................................................... 23

Figure 17: SWCC Salinity Subcomponent – MCAS Diagram ................................................................ 24

Figure 18: Salinity Hazard ............................................................................................................... 25

Figure 19: Hydrozone salinity risk .................................................................................................... 26

Figure 20: Salinity Extent (in red) ..................................................................................................... 27

Figure 21: Potential Short Term Future Salinity ................................................................................. 28

Figure 22: Potential Medium Term Future Salinity ............................................................................ 29

Figure 23: Potential Salinity Areas ................................................................................................... 30

Figure 24: WRRC Catchments for Salinity and Biodiversity ................................................................. 31

Figure 25: Low Capability Agricultural Land...................................................................................... 32

Figure 26 Areas with Projected Yield Declines ................................................................................... 33

Figure 27: Low Value Agricultural Land ............................................................................................ 34

Figure 28: Component 2 Output – Locations for Low-Biodiversity Plantings ........................................ 35

Figure 29: MCAS-S Diagram for Component 3 .................................................................................. 36

Figure 30: Rare or Threatened Vegetation Types .............................................................................. 37

Figure 31: Granite environments ..................................................................................................... 38

Figure 32: Threatened ecological communities (TECs) ....................................................................... 39

Figure 33: Poorly Represented communities - % remaining in reserves ............................................... 40

Figure 34: The % that each vegetation association has been reduced by clearing ................................ 41

Figure 35: The representativeness and relative importance of each individual patch of vegetation ....... 42

Figure 36: Degree of Endemism ....................................................................................................... 43

Figure 37: Contiguous Area of Vegetation ........................................................................................ 44

Figure 38: Vegetation remaining at the local scale ............................................................................ 45

Figure 39: Landscape Fragmentation - number of patches of vegetation within 5km ........................... 46

Figure 40: Community Diversity – number of vegetation associations within 5km ............................... 47

Figure 41: Interim values - High Value Biodiversity Areas .................................................................. 48

Figure 42: Proximity to Threatened Flora ......................................................................................... 49

Figure 43: Proximity to Priority 1 Rare Flora ..................................................................................... 50

Figure 44: Projected Climate Refugia 2085 ....................................................................................... 52

Figure 45: Vegetation areas greater than 2ha in extent .................................................................... 53

Figure 46: Component 3 Output –Areas with High Biodiversity or Conservation Value ......................... 54

Figure 47: Areas defined as High Conservation Value (red) using the 15% threshold. ........................... 55

Figure 48: Component 4 - MCAS-S Diagram...................................................................................... 56

iv Ecotones & Associates

Figure 49: Proximity to High Biodiversity/Conservation values ........................................................... 57

Figure 50: Distance to Conservation Reserve .................................................................................... 58

Figure 51: Distance to EPP Wetlands ............................................................................................... 59

Figure 52: Distance to Ramsar Wetlands .......................................................................................... 60

Figure 53: Distance from Water features (Topographic estuaries, lakes, pool & watercourses) ............. 61

Figure 54: Rivers and buffer zones ................................................................................................... 62

Figure 55: Proximity to Major Watercourses .................................................................................... 63

Figure 56: Wild River Catchments .................................................................................................... 64

Figure 57: Proximity to Priority Linkages .......................................................................................... 65

Figure 58: Potential for Infill ........................................................................................................... 67

Figure 59: Percentage of local vegetation clearing ............................................................................ 68

Figure 60: Level of vegetation fragmentation ................................................................................... 69

Figure 61: MCAS-S Final Output – Component 4 ............................................................................... 70

Figure 62: Component 5 design ....................................................................................................... 71

Figure 63: Component 1 – Landscapes that need to be protected from Carbon Plantings ..................... 72

Figure 64: Component 2 – Locations for Low-Biodiversity Carbon Plantings ........................................ 73

Figure 65: Component 3 – Identified Areas of High Biodiversity Value/Conservation Value................... 74

Figure 66: Component 4 – Locations for carbon plantings to enhance habitat corridors and protect high

biodiversity areas ........................................................................................................................... 75

Figure 67: Component 5 – Combinations of C1, C2 & C4 .................................................................... 76

Figure 68: C5 – Locations for High-Biodiversity Planting .................................................................... 77

Figure 69: C5 – Locations for Low-Biodiversity Planting ..................................................................... 78

Figure 70: C5 – Locations for Any Planting ....................................................................................... 79

Figure 71: Outcome Hierarchy ......................................................................................................... 80

Figure 72: Decision Matrix - Priority Outcomes Mapped .................................................................... 83

Figure 73: Decision Matrix - Priority Outcome Descriptions ................................................................ 84

Figure 74: MCAS-S Files Provided .................................................................................................... 85

List of Tables Table 1: Decision Matrix - All Possible Combinations of Outcomes from Components 1, 2 & 4. ............. 81

Table 2: Decision Matric - Priority Options and Description ................................................................ 82

Executive Summary v

Executive Summary

South West Catchments Council (SWCC) received funding in 2013/14 as part of the Australian

Government’s Clean Energy Future program under the Land Sector Package (Stream 1). The

project will feed into the new regional NRM plan, South West Regional Natural Resource

Management Strategy 2012-2020 by incorporating current climate change information and

scenarios.

The analysis undertaken, and the maps produced by this project, provide the information

required to meet the requirements of the Australian Government to update Regional

Strategies to:

Identify where tree plantings could fit into the landscape without causing adverse

impacts.

Provide clarity to Carbon Farming Initiative (CFI) proponents when considering

whether their carbon emission abatement projects adhere to Regional NRM plans and

do not have unintended impacts by taking into consideration priority agricultural land,

hydrology and biodiversity.

The process for obtaining this information was to form a Technical Working Group and

undertake a facilitated process using a decision support tool (MCAS-S - Multi Criteria Analysis

Shell for Spatial Decision Support).

Simon Neville from Ecotones & Associates was contracted:

To assist SWCC in the development and delivery of bio-sequestration risk maps and

their associated spatial layer(s) and a decision-support matrix.

Provide a written report outlining the bio-sequestration risk maps, spatial layer(s) and

decision support matrix, and including any electronic files developed as part of this

contract, e.g. the decision-support matrix.

The project involved six stages:

1. Pre- Planning

2. Initial Workshop (26th Feb)

3. Component Planning

4. MCAS-S Model Setup

5. 2nd Workshop for Components (26th March)

6. Create Final datasets & GIS project; Report

The project deliverables were produced through an MCAS-S process which delivered four

major components (including map outputs):

Component 1 - What landscapes need to be protected from carbon plantings?

Component 2 - Where would SWCC encourage low biodiversity carbon plantings (e.g.

monocultures, tree-crops)?

Component 3 - High value biodiversity or conservation areas (intrinsic/internal values)

vi Ecotones & Associates

Component 4 - Where in the landscape does SWCC want carbon plantings to enhance

habitat corridors and protect high biodiversity areas?

Three of these components (1, 2 & 4) are derived from three ‘Key Questions’ developed in

Albany on 19th February 2014, at a meeting of the south west WA NRM climate change

officers. This organisation of components provides a clear framework for the deliverables

under the project objectives, and provides the basis for a consistent set of guiding principles

for CFI investment across NRM regions.

A large amount of data was processed in order to create the final outputs, which have been

combined together to operational maps for the SWCC Staff. The final map provides a set of

outcomes, based on the hierarchy of uses shown here.

The hierarchy indicates which uses take precedence and in what order. We have used this to

rank different options and create the final output, indicating the priority areas for both low-

biodiversity planting (e.g. plantations) and high-biodiversity planting. It also indicates areas

where planting is not a priority use.

Full Protection

High Priority High-Biodiversity Planting High Priority Low-Biodiversity Planting

Low Priority Protection

Low Priority High-Biodiversity Planting Low Priority Low-Biodiversity Planting

No Protection or No Planting

Executive Summary vii

Spatially representing the SWCC priorities for biosequestration plantations 1

1. OBJECTIVES OF THE PROJECT

1.1 Project Objectives

South West Catchments Council (SWCC) received funding in 2013/14 as part of the Australian

Government’s Clean Energy Future program under the Land Sector Package (Stream 1). The project will

feed into the new regional NRM plan, South West Regional Natural Resource Management Strategy

2012-2020 by incorporating current climate change information and scenarios.

This document provides the information required to meet the requirements of the Australian

Government to update Regional Strategies to:

Identify where tree plantings could fit into the landscape without causing adverse impacts.

Provide clarity to Carbon Farming Initiative (CFI) proponents when considering whether their

carbon emission abatement projects adhere to Regional NRM plans and do not have unintended

impacts by taking into consideration priority agricultural land, hydrology and biodiversity.

The process for obtaining this information was to form a Technical Working Group and undertake a

facilitated process using a decision support tool (MCAS-S). Spatial data layers were sourced through

State Agencies, CENRM and additional sources as required. Datasets such as those below were to be

sourced and considered by the Technical Working Group.

High quality agricultural land (using available Land Capability mapping?, Local Planning Schemes

– Agricultural land zonings)

Sustainable Agriculture Report Card results (condition and trends in salinity, soil erosion….)

Hydrology (underlying aquifers, ground-water dependent ecosystems, Ramsar sites and

wetlands)

Biodiversity – key refugia, regional linkages, priority remnant vegetation, threatened species and

community - known and potential locations, conservation reserves and land tenure.

Agroforestry – key species (to be identified by working group) and their physiological responses

to climate change

Project area definition

The project was to be run for the SWCC area. It was considered preferable that if possible the analysis

should extend beyond SWCC boundaries, however much of the data already held and some supplied

data was clipped to this boundary (shown in Figure 1).

2 Ecotones & Associates

Figure 1: SWCC area boundary and major towns

Dataset provision

Accessing of data for modelling and general project mapping requirements was to be undertaken by

Gaia Resources, currently responsible for SWCC GIS needs.


2. PROJECT METHOD

Simon Neville from Ecotones & Associates was contracted:

To assist SWCC in the development and delivery of bio sequestration risk maps and their

associated spatial layer(s) and a decision-support matrix.

Provide a written report outlining the bio sequestration risk maps, spatial layer(s) and decision

support matrix, and including any electronic files developed as part of this contract, e.g. the

decision-support matrix.

The main tasks for the consultants were as follows:

Informing SWCC about data needs and data manipulations;

Designing models within MCAS-S (or similar);

Facilitation of Working Group meetings (2 workshops);

Assisting Working Group in rating and weighting data layers;

Confirming agreement within Working Group on final scenario(s) and decision support tree; and

Presentation of final results to the SWCC Board (one meeting) and broader stakeholders (one

meeting).

This section presents the process followed and the structure of the modelling components used to

answer SWCC’s major objective - to identify where tree plantings could fit into the landscape without

causing adverse impacts.


2.1 Modelling Methodology (MCAS-S)

Integration of spatial data with spatial modelling, risk assessment frameworks and policy decision-

making has been carried out in very broad variety of ways for the last 30 years. Early work in spatial

environmental modelling was carried out for conservation assessment reserves in the 1980’s (Margules

and Usher, 1981; Margules and Nicholls, 1988; Margules, 1989). With the development of GIS

techniques, more complex tools were created, and by the 2000’s a very wide range of tools and

techniques were being used. For example: Ortigosa et al (2000) developed a program (VVF) to integrate

a range of suitability models into GIS; Heidtke and Auer (1993) created a GIS-Based Nonpoint Source

Nutrient Loading Model; Boteva et al (2004) used multi-criteria evaluation to determine conservation

significance of vegetation communities; Panitsa et al (2011) integrate species and habitat-based

approaches to conservation value assessment within GIS. The large range of approaches use both built-

in tools and customised tools for a very broad range of applications – from conservation value

investigations to modelling of nutrient risk (Neville et al 2008) to modelling of ecological risk (Bartolo et

al 2012). As part of these, GIS has been used as a base for a wide range of environmental models.

However the incorporation of attitudes and preferences into modelling requires more specific tools,

especially where the choices are, in effect, being made on the basis of judgements and opinions rather

than quantifiable data. This is often the case in NRM policy-making, and is the case in the current

situation: some of the grounds for spatial location will be based on “science”, others on opinions. It is

therefore necessary to use a modelling tool that fulfils two functions:

It must allow the use of varying qualities and types of data; and

It must allow the combination of criteria based on anything from hard science to judgements

based on political preference.

Multi-criteria analysis is one such framework, and with its incorporation into the package MCAS-S (Multi

Criteria Analysis Shell for Spatial Decision Support - ABARES, (2011)), it brings this framework to spatial

decision making, suitable for NRM bodies. MCAS-S is a spatial software shell which can display spatial

data but does not have full GIS functionality. This software is relatively easy to use and can easily be

provided to 3rd parties for their use and modification. In addition it allows rapid combination of spatial

datasets & criteria specification, and thus allows real-time development with interested parties/experts

etc. This modelling vehicle was chosen for the current study by SWCC

Usage of MCAS-S has been developing constantly since its development in the 2000 to allow the use of

Multi-Criteria analysis in a spatial context (ABARES, 2011). A key reason for using MCAS in the current

project is that is explicitly allows for the incorporation of different levels of information in the same

analysis. It does this through rendering all inputs into the same scale through a process of “fuzzification”

– converting criteria in fuzzy scales from 0 to 1 – in terms of satisfaction of the intended purpose. In

addition, its spatial presentation of the process suits the use of a working group with a range of

members, viewpoints and preferences as well as technical expertise. By involving the working group in

the process to develop the spatial criteria, SWCC not only benefit from the members experience and

expertise, but can gain the support of these members in accepting and promoting the outcomes of the

process.


2.1.1 MCAS Requirements & Workflow

A fundamental aspect of MCAS-S is that it renders the datasets used as grids. This provides very fast and

flexible processing of multiple datasets, but means that all input data has to be rendered as grids, and

this can result in the loss of detail (depending on the grid size used).

Data held within MCAS-S must conform in spatial extent and projection. Because of this the user of

MCAS-S therefore still requires GIS software for data preparation. While ArcGIS is the recommended

software for the conversion process, SWCC are understood to be moving to using QGIS, an open-source

platform. While QGIS does have raster processing capabilities, we would recommend that SWCC

maintain at least one ArcGIS licence with the necessary extension (Spatial Analyst) to maintain full raster

processing capabilities.

There were a variety of ways in which datasets were processed to make them suitable for MCAS-S. The

major components of the workflow are:

Identify the dataset required

o Identify the way in which it will be used – as continuous data or categorical data.

Pre-Processing - Undertake any necessary initial processing, such as

o Conversion from shapefile to raster.

o Re-classification.

o Euclidean distance for proximity features, or

o Calculations on fields (such as area to create rasters of area).

MCAS-S Processing

o Sample or re-sample the dataset to the standard resolution and location,

o Re-project the raster during re-sampling or export

o Export the raster to the appropriate MCAS Folder.

Output rasters were generally controlled in a series of simple toolbox tools for specific operations (such

as gridding shapefile). Settings for all MCAS-S analysis were:

Output coordinates [GDA_1994_MGA_Zone_50]

Processing extent [standard SWCC NRM region shapefile, and a single snap raster to ensure

exact coincidence of rasters in analysis]

Raster Analysis [cell size fixed at 200m, and mask set for the study area].

The use of a 200m grid cell allowed for high resolution data analysis at the whole of region scale, and

was finer than originally expected given potential processing constraints. The MCAS-S software can

handle larger grids, however these come with a penalty in terms of the time taken to display maps at

larger scales (ie close-up). Using a 200m grid cell size allows reasonably rapid real-time display of

changes in the process outputs (maps) brought about by the workshop group. However we note that the

smallest cell is still 4ha in size, which represents a potentially large area at the sub-regional scale. SWCC

will need to treat the results with caution when using them at a farm-scale.


2.2 Project Process

The project has involved six stages:

1. Pre- Planning

a. Develop a notional component structure

b. Test with Project manager

2. Initial Workshop (26th Feb)

a. Project Introduction

b. Present MCAS-S software and process to group

c. Outline Major Model Components

d. Workshop possible criteria for analysis process with group.

3. Component Planning

a. Develop component structure

b. Develop component diagrams (criteria)

c. Place criteria into structure

4. MCAS-S Model Setup

a. Source spatial datasets fit for use in each component

b. Convert datasets for MCAS-S

c. Create MCAS-S Components with initial (draft) classification and rating for criteria and

criteria weighting

d. Get assistance from Reference group members on some technical aspects & criteria

5. 2nd (Major) Workshop for Components (26th March)

a. Present Components 1, 2 & 4 to Reference Group

b. Confirm structures, remove unnecessary criteria

c. Choose classification and rating for criteria

d. Choose weighting for criteria

e. Test Components

f. Finalise Component 3

6. Create Final datasets & GIS project

a. Complete components & produce maps from these

b. Combine maps in ArcGIS

c. Use maps to identify planting options.

Note that in this process stakeholder and expert consultation was sought for Stages 3 and 4 as well as

the workshop stages 2 and 5. Component 3 was not covered in the workshop but consultation was

sought with DPAW staff before and after Workshop 2 regarding the structure and criteria.


2.3 Component Framework

The project deliverables were produced through an MCAS-S process which delivered four major

components (including map outputs):

Component 1 - What landscapes need to be protected from carbon plantings?

Component 2 - Where would SWCC encourage low biodiversity carbon plantings (e.g.

monocultures, tree-crops)?

Component 3 - High value biodiversity or conservation areas (intrinsic/internal values)

Component 4 - Where in the landscape do we want carbon plantings to enhance habitat

corridors and protect high biodiversity areas?

Three of these components (1, 2 & 4) are derived from three ‘Key Questions’ developed in Albany on

19th February 2014, at a meeting of the south west WA NRM climate change officers. [Component 3 is a

major sub-component of Component 4, and given its size and complexity requires a separate process.]

This organisation of components provides a clear framework for the deliverables under the project

objectives, and has the advantage of being informally endorsed by the other NRM groups in SW WA. It

therefore provides the basis for a consistent set of guiding principles for CFI investment across NRM

regions.1

The three main components will be combined to provide clear direction to SWCC on priorities and

preferences for planting, along the lines of the following flow diagram.

Figure 2 – Overall Component Design & Outcomes

The actual criteria used in each component (indicated in the figures that follow) were selected by the

Reference group in the two workshops and data sourced to fill them, usually on the recommendation of

Reference group members.

1 The framework is being adopted by SCNRM and NACC in their biosequestration planning process, for the same

reasons.

Component 4

High Biodiversity

Carbon Planting

Acceptable:

Low Biodiversity

Carbon Planting

Acceptable:

Any Carbon

Planting

Acceptable:

High Biodiversity

Carbon Planting

High OR Low

Biodiversity

Planting

Component 1

Protection from

Carbon Planting

Component 2

Low Biodiversity

Carbon Planting


2.4 Component Model Diagrams

2.4.1 Component 1 – Landscapes that need to be protected from Carbon

Plantings

The structure for this component is based around identifying and avoiding high-quality agricultural land,

any potential water resources that require protection from planting, and remnant vegetation. The

assessment of agricultural value is taken from land capability mapping based on the DAFWA

soil/landscape mapping developed over the last 20 years or so. Projected yield sustainability is a set of

crop yield projections based on rainfall projections for 2050. The component uses a climate stress

indicator (growing season rainfall) to identify areas that appear unlikely to remain productive under

climate change. The inclusion of protection zones for public declared water supply areas protects certain

water resources from inappropriate plantations. A significant amount of data was supplied especially for

this project by DAFWA staff, including agricultural land capability and projected yields.

Figure 3: Component 1 – Protection from Carbon Plantings

In this diagram and those that follow, the boxes represent the criteria and sub-criteria that contribute to

identifying the outcome. The green box is the outcome, the orange boxes indicate key input criteria; and

the grey boxes are contributing criteria. In one Component (3) there is a further set of yellow boxes

indicating further sub-divisions of contributing criteria or sub-criteria.

Landscapes that need to be protected from

carbon plantings

High quality agricultural land

High Capability agricultural land

Areas with projected yield sustainability

Public Declared Water Supply Areas -

Protection Zones

Climate Refuge Areas Maintained Growing

Season Rainfall

Remnant Vegetation


2.4.2 Component 2 – Locations for Low-Biodiversity Carbon Plantings

This component focuses low biodiversity planting (such as monocultures, traditional plantation forestry,

and low biodiversity carbon farming plantations) away from high-value agriculture, but into recovery

catchments. It also specifically targets areas close to potential salinity areas - areas that have identified

salinity risk but no expression as yet (salinity hazard). Salinity data comes from LandMonitor/DAFWA and

involves a combination of salinity hazard mapping from Land monitor satellite imagery analysis and

salinity risk from terrain analysis.

A number of the datasets used here are the same as in component 1, with the difference that the other

end of the scale of values is highlighted.

Figure 4: Component 2 – Locations for Low-Biodiversity Carbon Plantings

Areas where we would encourage low-

biodiversity carbon plantings

Potential Salinity Areas

Areas Close to Potential Short-term

Future Salinity

Areas Close to Potential Long-term

Future Salinity Water Resource &

Biodiversity Recovery Catchments

Low Value agricultural land

Low Agricultural Capability

Areas with projected yield declines

Cleared Land


2.4.3 Component 3 – Identifying Areas of High Biodiversity Value/Conservation

Value

For this component the initial workshop identified some criteria, but the lack of workshop input (partly

due to time, partly the unavailability of DPAW staff) meant that this component has been produced as a

desktop project with input from DPAW staff or other members of the reference group.

This component identifies intrinsic conservation/biodiversity values, and mirrors a similar approach used

to evaluate conservation value of remnant vegetation in the south west (Neville, 2009). A series of

criteria based on existing GIS data area used. The criteria are taken from basic conservation value

assessments, which emerged in the 1980’s (Margules & Usher (1981), Margules et al (1982), Austin

(1983), Margules and Nicholl (1988)).

These have been further developed and their relative importance quantified (Boteva et al (2004), Panitsa

et al (2011):

Diversity (30%)

Rarity (33%)

Naturalness (26%)

Area

Threat/replaceability (9%)

While this component has not been workshopped, we have been able to use this theoretical framework

to select, rate and weight the input criteria.

There is a significant difference between this identification of intrinsic values and other indicators of

conservation value, in that this component indicates conservation value even where no protection has

been given to an area, such as through reserve status. It recognises that not all areas of high value have

been accorded formal status, and that in a highly-fragmented landscape small areas can contain values

of uniqueness and representativeness.

Many of the datasets used were available (such as proximity to rare flora, granite areas, TEC/PECs and &

NCCARF Terrestrial Refugia value). However others had to be developed from a (2014) remnant

vegetation cover dataset from DAFWA and from the best available vegetation association data.

Endemism and % remaining in reserve datasets were supplied by staff from DPAW.


Figure 5 - Component 3 – Areas of High Biodiversity Value/Conservation Value2

2 Yellow boxes indicate sub-criteria

Areas of High Biodiversity Value / Conservation Value

High Value Biodiversity Areas

Areas > 2ha

Naturalness

Area of Contiguous Vegetation

Fragmentation

% Clearing

Rare or Threatened Vegetation Types

Threatened ecological communities (TECs)

Granite environments

Poorly Represented communities - %

remaining in reserves

Association reduction %

Patch Importance

Degree of Endemism

Community Diversity (Variety <5km)

Proximity to Threatened Species

Proximity to Threatened Flora

Proximity to Priority 1 Rare Flora

Climate Refugia NCCARF Terrestrial Refugia Value


2.4.4 Component 4 – Locations for carbon plantings to enhance habitat

corridors and protect high biodiversity areas

This final component uses a range of datasets to represent existing biodiversity assets, and includes the

Component 3 output as a high level input. Component 3 provides an assessment of intrinsic value of

areas from a biodiversity perspective – regardless of whether they have been previously identified as

having value or not. This component uses distance to such values as an important criterion for high-

biodiversity planting. The rest of the criteria are more focused on the significance of an area based on its

location in relation to existing identified assets, linkages, water, wetlands and remnant vegetation. Much

of this data was available as required, but some indicators (such as % clearing & fragmentation) were

derived from the remnant vegetation layer.

Figure 6: Component 4 – Location of Biodiversity Plantings

Areas where we want biodiversity plantings

Proximity to High Value Biodiversity Areas

[Component 3]

Proximity to known biodiversity assets

Reserves (Proximity)

Conservation Reserve (Proximity)

Crown Reserve (Proximity)

Wetlands (Proximity & value)

EPP Wetlands (Proximity)

RAMSAR Wetlands (Proximity)

Topographic estuaries, lakes, pool & watercourses

Priority Linkages (proximity)

Potential for infill

% Clearing

Areas with high fragmentation

Rivers & Buffer Zones

Proximity to Major Watercourses

Wild River Catchments

Dieback Assessment (proximity)

Cleared Land


3. COMPONENT DETAILS

The figures illustrating the MCAS-S components (like MCAS-S) use a number of conventions. Key

amongst these is the use of a Red-Blue colour ramp to indicate values. Depending on the number of

value classes selected, the ramp will be more or less complex, but in all cases, Red = high value, Blue =

low value, and Green = middle value.

Note that unless otherwise stated, the colours used in the maps of individual criteria use red as the

highest value and blue as the lowest:

Figure 7 – Classification Figures in MCAS-S


3.1 Component 1 – Landscapes that need to be protected from

Carbon Plantings

The MCAS-S diagram for this component is as follows:

Figure 8: Component 1 MCAS-S Diagram


3.1.1 High Capability Agricultural Land

Agricultural land capability is a key dataset, as there is no desire to see the best agricultural land taken

out of production. We did not have access to data on land values or agricultural productivity that was

either fine scale or recent3, and therefore looked to DAFWA to provide a surrogate. The chosen indicator

of agricultural land value is agricultural land capability, which has been derived by DAFWA from their

Soil-Landscape mapping datasets for 6 landuse types:

Broadscale Agriculture – grazing, dryland cropping and dryland cropping with minimum tillage

Intensive Agriculture – vines, perennial horticulture and annual horticulture.

Soils are classed for capability (classes 1 – 5; where 1 is best) and soil-landscape units coded based on

proportion of capable soils:

Code Legend

A1 >70% Class 1 or 2 (highest capability) A2 50-70% Class 1 or 2 B1 >70% Class 1, 2 or 3 B2 50-70% Class 1, 2, 3 C1 50-70% Class 4 or 5 C2 >70% Class 4 or 5

These six landuse types were combined in MCAS as categorical layers, where A1 had the highest (~1.0)

and C2 the lowest value (~0). The maximum value for each cell was extracted, i.e. the highest capability

value for any landuse, and this maximum used to indicate land capability for agriculture. Note that this

value is the base capability of the soils/landscape and does not account for water availability and other

non-soil factors like distance to market. Capability is mapped as follows:

Figure 9: High Capability Agricultural Land

3 Data is available from ABARE, however it is based on previous census and surveys (at least 7 years old) and is at a

very coarse scale.


3.1.2 Projected Yield Sustainability

The working group wished to include an indicator of potential effects of climate change on agricultural

potential in the model, and this layer provides an indication of agricultural reliability into the future (in

this case out to 2080) using projected change in potential yields.

Projected potential yield change estimates were generated by DAFWA in 2005 (Vernon and van Gool,

2006). Modelling was undertaken for major crops (wheat, oats, barley, lupins and canola) at 2050. The

temperature change scenario used was SRES A2, and the GCM was CSIRO Mark II. OzClim was used to

calculate surfaces that show the difference from the base climate (1961-90). The DAFWA results were

supplied in shapefiles which were gridded at 200m grids. The values used showed % change from 2005

yields (tonnes/ha).

Each projection dataset was split into 6 classes based on the projected % change:

1 - from -15.16 2 - from -10 3 - from -5 4 - from -2.5 5 - from 0 6 - from 2.5 (highest value)

There individual crop projections were combined using MCAS in a composite layer producing 7 classes.

The composite function was weighted according to the relative value of each crop (P Raper, pers.

Comm.) as follows, and generated from the sum of:

2 x 'Barley yldchng_pc' 2 x 'Canola yldchng_pc' 1 x 'Lupins yldchng_pc' 2 x 'Oats yldchng_pc' 3 x 'Wheat yldchng_pc'

The result was classed on an equal interval basis as shown. The figure following has had the existing

remnant vegetation areas masked out, but we note that much of the best performing areas are in fact

not agricultural.


Figure 10: Projected Yield Sustainability


3.1.3 High Quality Agricultural Land

The layer '*High Quality Agricultural Land' is a composite layer producing 3 classes; Low, Medium &

High, based on equal area classification.

The composite function is generated from the sum of:

2 x 'High Capability Agricultural Land'

1 x 'Projected Yield Sustainability'.

It therefore combines our existing understanding of land capability for agriculture with a second

criterion indicating reductions in agricultural value with climate change.

Figure 11: High Quality Agricultural Land


3.1.4 Growing Season Rainfall % Change

The group wished to include another indicator of potential climate affects in the model, and this layer

provides an indication of rainfall reliability into the future (in this case out to 2080) as this would affect

plantations.

The data is projected mean May-October Rainfall % Change (mm) by 2080. It comes from the GCM

CSIRO-Mk3.5, and the emission scenario is SRES marker scenario A2 (Global Warming Rate: moderate).

The layer 'Growing Season Percentage Change' is split into 5 classes:

5 - from -20.99712 (highest value) 4 - from -17.19226 3 - from -15.28983 2 - from -13.29681 1 - from -11.66616

All of these classes indicate a decline in growing season rainfall – the classification is directed to ensure

that carbon plantings are directed away from areas with higher projected rainfall reduction.

Figure 12: Growing Season Rainfall % Change


3.1.5 Protection Zones for PDWSA

Protection Zones for PDWSA (Public Drinking Water Source Areas) represent reservoir, bore or wellhead

locations buffered by a prescribed distance, where planting large scale plantations will impact on the

provision of water. These are relatively small areas in SWCC, with the two types of zone (Reservoir &

Wellhead protection) are given the same value.

The layer Protection Zones for PDWSA is split into 2 classes:

2 – Red – is a PDSWA

1 - White – is not a PDSWA:

Figure 13: Protection Zones for PDWSA


3.1.6 Remnant Vegetation

A basic policy of SWCC is that there will be no clearing of native vegetation for plantations of any sort.

Remnant vegetation is included in this component as an exclusion – no planting will occur on areas still

vegetated. This component therefore masks out all areas where vegetation still exists, as shown in the

figure below.

The dataset Native Vegetation Contiguous Area 2014 was originally compiled as part of the vegetation

theme of the National Land and Water Resource Audit (NLWRA). The dataset has been progressively

updated by the Department of Agriculture and Food post-NLWRA with assistance of the Department of

Environment and Conservation. This has been carried out using digital aerial photography (orthophotos)

acquired 1996 to 2013.

The remnant vegetation layer is classified into 2 classes:

2 – red – cleared 1 - white – remnant vegetation.

Figure 14: Remnant Vegetation mask


3.1.7 Component 1 Output - Landscapes that need to be protected from carbon

plantings

The output layer 'Landscapes that need to be protected from carbon plantings' is a composite layer

producing 3 classes; No Protection, Mid-Priority Protection and Full Protection.


3 x '*High Quality Agricultural Land' 1 x 'Growing Season Percentage Change' 1 x 'Protection Zones for PDWSA' 0.1 x 'Remnant Vegetation'

The result is classed into three zones on an equal areas basis.

Blue - areas without protection, Green - areas with Low Priority protection, and Red - areas with high priority (Full) protection.

Figure 15: Component 1: MCAS-S Output


3.2 Component 2 – Locations for Low-Biodiversity Carbon Plantings

The MCAS-S diagram for this component integrates the various criteria as follows:

Figure 16: Component 2 MCAS-S Diagram

There criteria are outlined below.


3.2.1 Potential Salinity

Planting of trees close to potential salinity areas is considered to be on effective measure to reduce the

impact of salinization. (The other is to provide for large-scale planting at the catchment scale to reduce

water-table rise). The short and long term future salinity layers are produced by a separate analysis. This

analysis uses three criteria:

Salinity Hazard (height above valley floor)

Hydrozone salinity risk

Salinity Extent

Figure 17: SWCC Salinity Subcomponent – MCAS Diagram


3.2.1.1 Salinity Hazard (height above valley floor)

This dataset is calculated from Land Monitor digital elevation models (DEMs) 25m resolution, and

identifies areas close to valley flow level as candidates for salinity dues to rising water tables. The

original grid has been re-classified so that cell values referring to hazard areas (values 1, 2, 3) are

converted to 1, all other values to 0. A process called ‘block statistics’ has been run at 8x8 cell scale to

sum all the potential salinity hazard cells within an 8x8 grid (200mx200m area) - to represent coarser

scale hazard (values 0 - 64). The summed values are charted below where blue = no hazard and red =

highest hazard.

Figure 18: Salinity Hazard


3.2.1.2 Hydrozone salinity risk

This dataset represents the timescale of development of dryland salinity in each hydrozone, and has

been produced as part of the DAFWA Report Card process. The risk assessment was based on the

likelihood and consequence of dryland salinity developing further in each hydrozone, and is shown

below as Green – Medium Term and Red – short term.

Figure 19: Hydrozone salinity risk


3.2.1.3 Salinity Extent

Salinity Extent data is sourced from the Landmonitor project and shows salt affected land, as well as land

that is potentially salt-affected, but where vegetation makes classification uncertain. The salt affected

classification represents areas affected by salt, not just surface expression (ie not just bare saltland).

Figure 20: Salinity Extent (in red)

3.2.1.4 Distance from Potential Short Term Future Salinity

The layer 'Potential Short Term Future Salinity' is generated with a multi-way mask function in MCAS-S.

The mask selects areas meeting the following criteria:

Layer 'salinity_xtnt' having a classified value between 1 and 4: i.e. not yet affected by salinity

Layer 'salinity_hzd' having a classified value of 5: i.e. high level hazard exists

Layer 'Time to Equilibrium' having a classified value of 5: i.e. time to equilibrium is shorter term

These areas can be described as being at risk of developing salinity but not yet expressing any

symptoms, and being in an area where such expressions will take place in the short term.

Layer 'salinity_xtnt' is a categorical layer built from 'salinity_xtnt' Class 1 for Out of Area Class 1 for Not Affected Class 4 for Vegetated, potentially salt-affected Class 5 for Salt Affected (highest value)


Layer 'salinity_hzd' is generated from primary data 'salinity_hzd' Split into 5 classes 1 - from 0 2 - from 12.8 3 - from 25.6 4 - from 38.4 5 - from 51.2 (highest value) Layer 'Time to Equilibrium' is a categorical layer built from 'sallin_urg' Class 3 for Medium Term Class 5 for Short Term (highest value)

Figure 21: Potential Short Term Future Salinity

The dataset above had the operation Euclidean distance performed on it to identify the distance of

every cell from these potential salinity areas. In the final component the distances used were very small

– up to 200m from any potential salinity cell.

3.2.1.5 Distance from Potential Long Term Future Salinity

Layer 'Potential Longer Term Future Salinity' is generated with a multi-way mask function. The mask

selects areas meeting the following criteria:

Layer 'salinity_xtnt' having a classified value between 1 and 4: Ie not yet affected by salinity


Layer 'salinity_hzd' having a classified value of 5: high level hazard exists

Layer 'Time to Equilibrium' having a classified value between 3 and 4: Time to Equilibrium is Medium term

These areas can be described as being at risk of developing salinity but not yet expressing any

symptoms, and being in an area where such expressions will take place in the longer (Medium) term.

The difference from Short Term salinity is in the Layer 'Time to Equilibrium' : Class 5 for Medium Term (highest value) Class 3 for Short term

Figure 22: Potential Medium Term Future Salinity


3.2.2 Potential Salinity Areas

Layer 'Potential Salinity Areas' is a composite layer producing 3 classes


1 x 'Distance from Potential Long Term Future Salinity' 2 x 'Distance from Potential Short Term Future Salinity' The result is classed according to this table: 1 - up to 0.6666667 2 - up to 1.333333 3 - above 1.333333 (highest value)

Figure 23: Potential Salinity Areas


3.2.3 WRRC Catchments for Salinity and Biodiversity

Water Resource and Biodiversity Recovery Catchments are important targets for revegetation, and so

are emphasised in this component. Both types of recovery catchment are included in the final

component, however Water Resource Recovery catchments are weighted more highly (1.0 vs 0.8) to

reflect the better fit of low-biodiversity plantings to their purpose.

Layer 'WRRC Catchments for Salinity and Biodiversity' is a categorical layer:

Class 4 for Biodiversity Recovery Catchment (yellow)

Class 5 for Water Resource Recovery Catchment. (red - highest value)

Figure 24: WRRC Catchments for Salinity and Biodiversity


3.2.4 Low Capability Agricultural Land

As in the previous component, land capability has been derived by DAFWA from their Soil-Landscape

mapping datasets for 6 landuse types. For this component the classes were allocated in reverse order:

the cells with the lowest capability were classified as highest value, as these would be the areas that the

group would direct low-biodiversity plantations to.

Red = Lowest capability Agricultural land. Other values indicated in the map key below.

Figure 25: Low Capability Agricultural Land


3.2.5 Areas with Projected Yield Declines

As in the previous component, this dataset comes from modelling conducted by DAFWA on projected

yield potential change into the future under a moderate climate scenario. In this component however

the classification system weights areas with high levels of decline as suitable targets for plantations.

Red = areas with highest project yield declines. Blue = areas with lowest projected yield declines.

Figure 26 Areas with Projected Yield Declines

3.2.6 Low Value Agricultural Land

Layer 'Low Value Agricultural Land' is a composite layer producing 5 classes from ‘High Value’ (class 1) to

‘Low Value’ (class 5).


2 x ' Low Agricultural Capability' 1 x 'Areas with Projected Yield Declines'

The result is classed as equal interval according to this table:

1 - up to 0.5632499 2 - up to 1.1265 3 - up to 1.68975 4 - up to 2.253 5 - above 2.253 (highest value)


Figure 27: Low Value Agricultural Land

3.2.7 Remnant Vegetation

Remnant vegetation is again included as an exclusion – no planting will occur on areas still vegetated.

This component therefore masks out all areas where vegetation has not been cleared (see Section 3.1.6

for further details).

3.2.8 Component 2 Output – Locations for Low-Biodiversity Plantings

Layer 'Areas to encourage low-biodiversity carbon Plantings' is a composite layer producing 3 classes.


3 x '*Low Value Agricultural Land' 1 x '*Potential Salinity Areas' 1 x 'cleared_2014' 1 x 'WRRC Catchments for Salinity and Biodiversity'

The result is classed according to an equal-area classification:

1 - up to 1.895257 – No Low-Biodiversity Plantings 2 - up to 2.3966 – Low Priority Low-Biodiversity Plantings 3 - above 2.3966 – High Priority Low-Biodiversity Plantings


These three classes become the direction from this Component. The final Component model is shown

below, where:

Blue - areas without protection, Green - areas with Low Priority protection, and Red - areas with high priority (Full) protection.

Figure 28: Component 2 Output – Locations for Low-Biodiversity Plantings


3.3 Component 3 – Identifying Areas of High Biodiversity

Value/Conservation Value

Figure 29: MCAS-S Diagram for Component 3

Component 3 is complex, and includes a number of intermediate sub-components, including rare or

threatened vegetation types, naturalness, high value biodiversity area, and proximity to threatened

species. The complex set of criteria is shown above in the component diagram.

The component can be used as an input to Component 4 or as a standalone indicator of

biodiversity/conservation value.


3.3.1 Rare or Threatened Vegetation Types

This sub-component is made up of six criteria:

Granite environments

Threatened ecological communities (TECs)

Poorly Represented communities - % remaining in reserves

Association reduction %

Patch Importance

Degree of Endemism

Figure 30: Rare or Threatened Vegetation Types


3.3.1.1 Granite environments

Granite environments provide unique environments, particularly in inland locations where vegetation

communities surrounding the bare surfaces are watered from runoff in locally restricted micro-climates.

The environments are shown in Red below:

Figure 31: Granite environments


3.3.1.2 Threatened ecological communities (TECs)

Threatened and Priority Ecological Community (TecPecs) are ecological communities throughout WA

that have been classified as "Critically Endangered", "Endangered", "Vulnerable", or as "Priority”.

Note that this dataset covers a very restricted set of communities which can benefit from buffering and

additional protection. The classification uses the following categories:

Class 5 for Critically Endangered, Endangered and Vulnerable (Red - highest value) Class 4 for Priority 1, 2 & 3. (Yellow)

Figure 32: Threatened ecological communities (TECs)


3.3.1.3 Poorly Represented communities - % remaining in reserves

A basic criterion for conservation biology is the extent to which a vegetation community is protected in

reserves. This dataset shows the percentage of each Vegetation Association (based on Beard’s

vegetation associations) which is currently protected within DEC Reserves (2012). The poorer the

representation the higher priority for conservation.

The classification uses five equal interval classes, where the lower values indicate the least amount in

reserves:

5 - from 0 – 20% (highest value) 4 - from 20 – 40% 3 - from 40 – 60% 2 - from 60 – 80% 1 - from 80 – 100%

Figure 33: Poorly Represented communities - % remaining in reserves


3.3.1.4 Association reduction %

This criterion uses the amount of reduction in each vegetation association since clearing as an indicator

of rarity of the remaining areas. The data is derived from Heddle and Beard vegetation classifications

(DEC) and the current vegetation remaining dataset (DAFWA). The original "System Association" (Beard)

or "Complex" (Heddle) polygon areas summarised for the pre-clearing datasets within the SWCC area,

and then intersected with remaining Vegetation dataset to create post-clearing (2014) datasets. The

reduction in area for each vegetation type was then calculated as a %. This was calculated for both

vegetation classifications, but the final dataset uses the Heddle classification where it exists (Heddle

does not cover the entire SWCC are, but is a far more detailed dataset).

Split into 5 classes:

1: 0 – 25% 2: 25 – 50% 3: 50 – 65% 4: 65 – 75% 5 : 75 – 100% (highest value)

Figure 34: The % that each vegetation association has been reduced by clearing


3.3.1.5 Patch Importance

This value indicates the % of its vegetation association that each individual polygon (patch or vegetation)

represents – this is an indicator of the representativeness and relative importance of the patch.

Derived from Heddle/Beard Datasets (DEC) & current vegetation remaining dataset (DAFWA) Each

individual patch (veg polygon) area was calculated and divided by the remaining area of its association

type to create a % value. This was calculated for both vegetation classifications, but the final dataset

uses the Heddle classification where it exists.

The layer is split into 5 classes, where 5 is the highest value; any patch representing over 50% of the

remaining area is in the highest class.

1: 0 – 10% 2: 10 – 20% 3: 20 – 40% 4: 40 – 50% 5 - >50% (highest value)

Figure 35: The representativeness and relative importance of each individual patch of vegetation


3.3.1.6 Degree of Endemism

Endemism is a measure of the number of species in a cell that are endemic (locally restricted). In this

case the data are not actual counts of endemic taxa, rather an index of endemism. Native Endemic

Species are defined as having a range of < 10,000 sq km. Note that this data is based on subsampled data

to correct for sampling effort.

Split into 5 classes (equal interval)

1 - from 22.42561 2 - from 55.62762 3 - from 88.82964 4 - from 122.0317 5 - from 155.2337 (highest value)

Figure 36: Degree of Endemism


3.3.2 Naturalness

Naturalness is based on three sub-criteria with equal weight:

The area of contiguous vegetation The % of clearing locally. The amount of vegetation fragmentation

3.3.2.1 Contiguous Area

Native Vegetation Contiguous Area comes from the DAFWA remnant vegetation data for 2014, digitised

from digital aerial photography (orthophotos) acquired 1996 to 2013.

The field "area_ha" field gridded at 200m cell size using calculated polygon area (ha). Note that roads cut

forest polygons into smaller contiguous blocks.

Split into 5 classes (values are multiples of a single grid cell – 2 ha):

1 - from 0.03381367 2 - from 8 3 - from 80 4 - from 800 5 - from 8000 (highest value)

Figure 37: Contiguous Area of Vegetation


3.3.2.2 Local Clearing

The first measure of landscape disturbance measures the amount of native vegetation within 2km (of

each cell) as a percentage.

The data was split into 5 classes using an equal interval scale, higher values indicate less clearing (a

higher % of vegetation remaining within 2km).

1 - from 1 2 - from 20.8 3 - from 40.6 4 - from 60.4 5 - from 80.2 (Highest Value)

Figure 38: Vegetation remaining at the local scale


3.3.2.3 Landscape Fragmentation

The second part of landscape disturbance calculates the number of patches of vegetation within 5km to

indicate the extent to which vegetation has been cut up (fragmented).

Focal Statistics carried out within 5km radius to calculate Variety (ie the number of different (sized)

patches of vegetation.)

Split into 5 classes using an equal area classification where lower numbers of patches is valued highest

5 - from 1 - 23 (highest value) 4 - from 24 - 54 3 - from 54 - 75 2 - from 75 - 106 1 - from 106

Figure 39: Landscape Fragmentation - number of patches of vegetation within 5km


3.3.3 Community Diversity

Community diversity is a standard indicator of conservation value on the grounds that more diverse

areas contain greater opportunities for species richness and complexity. The index used here is the

variety of Vegetation Associations within 5km (2014) Derived from Heddle/Beard Datasets (DAFWA/DEC)

Beard & Heddle Vegetation classifications were combined over the SWCC area, with Heddle taking

precedence. A grid was created from this combined shapefile and Focal Statistics carried out on this to

calculate Variety (number of different associations) within 5km radius. Note that due to the use of two

different datasets there is a discontinuity in values across the two (Heddle values higher due to the finer

resolution of the classification). This was considered preferable to using the Beard classification which is

very generalised.

Split into 5 classes using an equal interval classification:

1 - from 1 2 - from 6 3 - from 11 4 - from 16 5 - from 21 (Highest Value)

Figure 40: Community Diversity – number of vegetation associations within 5km


3.3.4 High Value Biodiversity Areas

Layer 'High Value Biodiversity Areas [Multiply]' is a composite layer producing 5 classes

The composite function is generated from the product of:

1 x 'Classified >2ha' 3 x 'Diversity - association <5k' 2 x 'Naturalness' 4 x 'Rare or Threatened Veg Types'

Using a multiplication function means that the areas receiving high values must score reasonably well

against all the criteria – a low value on any one criterion will drop a cell values. Areas scoring well as high

biodiversity areas therefore at least partially meet all the criteria involved.

The result is classed according to this table:

1 - up to 1.5 2 - up to 3 3 - up to 5 4 - up to 8 5 - above 8 (Highest Value)

Figure 41: Interim values - High Value Biodiversity Areas


3.3.5 Proximity to Threatened Species

This is a criterion which measures actual biodiversity values through two criteria:

Proximity to Threatened Flora

Proximity to Priority 1 Rare Flora.

These are considered the key measures (K Williams, DPAW, pers. Comm). Threatened flora are given a

higher weighting in the composite (3 vs 2). Rare fauna are not included in this measure due to significant

and systematic bias in recording (K Williams, DPAW, pers. Comm).

3.3.5.1 Proximity to Threatened Flora

Threatened Flora records have been extracted from DPAW’s Threatened (Declared Rare) and Priority

Flora database. Coding is based on State Assessment (ConsStatus). Point Records were converted to a

200m grid raster and Euclidean Distance function performed. Distance in metres.

Split into 5 classes – preference is given to areas in close proximity to the records, influence degrades

rapidly with distance

5 - from 0 (metres) (Highest Value) 4 - from 1000 3 - from 2500 2 - from 5000 1 - from 10000

Figure 42: Proximity to Threatened Flora


3.3.5.2 Proximity to Priority 1 Rare Flora.

Priority 1 Flora records have been extracted from DPAW’s Threatened (Declared Rare) and Priority Flora

database. Coding is based on State Assessment (ConsStatus). Point Records were converted to a 200m

grid raster and Euclidean Distance function performed. Distance in metres.

Split into 5 classes – preference is given to areas in close proximity to the records, influence degrades

rapidly with distance

5 - from 0 (metres) (Highest Value) 4 - from 1000 3 - from 2500 2 - from 5000 1 - from 10000

Figure 43: Proximity to Priority 1 Rare Flora


3.3.6 Climate Refugia

SWCC is currently engaged in introducing climate change into its planning, and this criterion is the best

available to identify potential climate impacts on species (ie biodiversity). The Biological Refugia under

Climate Change criterion is one output of a large project, funded by NCCARF, modelling potential

distributions of species into the future.4 This dataset shows projected refugia areas in 2085, being areas

with the smallest loss, and greatest gain, of species. This maps shows the areas with the most

immigrants and fewest emigrants summed over four major taxonomic groups, and a total of 1400

species.

The areas with high values (Class Five) are projected to be refugia in the sense of providing the best

chance for the retention of existing biodiversity, and the potential to provide possibilities for species

displaced by changing climate.

The detailed refugia are scaled from 1 (lowest priority) to 7 (highest priority).

Class 1 for 0 Class 1 for 1 Class 2 for 2 Class 3 for 3 Class 4 for 4 Class 5 for 5 & 6 (Highest Value)

4 NCCARF – National Climate Change Adaptation Research Facility. Reside et al. 2013, Climate change refugia for

terrestrial biodiversity: Defining areas that promote species persistence and ecosystem resilience in the face of global climate change.


Figure 44: Projected Climate Refugia 2085


3.3.7 Size - Areas > 2 ha

Because this component is intended to be used in Component 4 as indicating where high-biodiversity

values may be found, it was decided to include a criterion excluding any patch of remnant vegetation

under 2 ha. This was done on the basis that such areas lack the ability to maintain value over long

periods of time. Note that this excludes a large number of small patches, as shown below:

Areas in red are >2ha

Areas in blue <2ha.

Figure 45: Vegetation areas greater than 2ha in extent


3.3.8 Component 3 Output –Areas with High Biodiversity or

Conservation Value

The final layer ‘Areas of High Biodiversity/Conservation Value' is a composite layer producing 5 classes


6 x '* High Value Biodiversity Areas' 2 x '* Potential Climate Refugia' 1 x '* Proximity to Threatened Species'


1 - up to 1.5 2 - up to 2 3 - up to 4.2 4 - up to 6.176396 5 - above 6.176396.

Figure 46: Component 3 Output –Areas with High Biodiversity or Conservation Value


This output was further classified to identify a total of 15% of remaining vegetation as “High Value”,

shown in red in the following figure.

Highest Value conservation areas – red

Other remnant vegetation – blue.

Figure 47: Areas defined as High Conservation Value (red) using the 15% threshold.


3.4 Component 4 – Locations for carbon plantings to enhance habitat


The component contains five major sub-components shown in the MCAS_S diagram below:

Proximity to High Biodiversity/conservation values (Component 3)

Proximity to known biodiversity assets

Rivers and buffers zones

Proximity to Priority Linkages, and

Potential for infill.

All of these sub-components are locational – indicating identified assets that are considered important

to plant near. As in the case of components 1 & 2, it removes remnant vegetation from consideration.

Figure 48: Component 4 - MCAS-S Diagram


3.4.1 Proximity to High Biodiversity/Conservation values (Component 3)

The result from Component 3 has been split into two classes – high and low – using a classification that

identifies the highest 15% of all remaining vegetated areas. This area has been buffered to identify close

proximity to these highest value areas and the buffer is used in Component 4.

Split into 3 classes

3 - from 0 2 - from 1000 1 - from 2500

The classes used identify areas within 1km and 2.5km of High Value Biodiversity/Conservation

vegetation as being within the influence areas.

Figure 49: Proximity to High Biodiversity/Conservation values


3.4.2 Proximity to known biodiversity assets

3.4.2.1 Distance to Conservation Reserve

This criterion specifies areas in close proximity to all Crown Reserves specifically vested for conservation

purposes. It will have the effect of providing for planting around existing reserves. Distances in m.


3 - from 0 – 500m (Highest Value) 2 - from 500 – 1000m 1 - from 1000 m (Lowest value)

Figure 50: Distance to Conservation Reserve


3.4.2.2 EPP Wetlands (Proximity)

This criterion specifies areas in close proximity to all EPP Wetlands. It will have the effect of providing for

planting around these wetlands and providing additional protection to them. Distances in m.


3 - from 0 – 500m (Highest Value) 2 - from 500 – 1000m 1 - from 1000 m (Lowest value)

Figure 51: Distance to EPP Wetlands


3.4.2.3 RAMSAR Wetlands (Proximity)

This criterion specifies areas in close proximity to all RAMSAR wetlands. It will have the effect of

providing for planting around these wetlands and providing additional protection to them. Distances in

m.


3 - from 0 – 500m (Highest Value) 2 - from 500 – 1000m 1 - Over 1000 m (Lowest value)

Figure 52: Distance to Ramsar Wetlands


3.4.2.4 Distance from Water features (Topographic estuaries, lakes, pool & watercourses)

This criterion specifies areas in close proximity to all water features - estuaries, lakes, pool and identified

watercourses. It will have the effect of enhancing planting around these wetlands and providing

additional protection to them. Distances in m.


3 - from 0 – 200m (Highest Values) 2 - from 200 – 500m 1 - Over 500

Figure 53: Distance from Water features (Topographic estuaries, lakes, pool & watercourses)


3.4.3 Rivers and buffer zones

The sub-component identifies areas in close proximity to major rivers and streams, as well as the Wild

Rivers catchments in the SWCC area. Major rivers in these catchment score highest.

Figure 54: Rivers and buffer zones


3.4.3.1 Proximity to Major Watercourses

Proximity to major watercourses is considered an important criterion – not only does fringing vegetation

play an important role in improving water quality, but the provision of riverine vegetation provides for

corridors and greatly improves in-stream habitat quality.

Major watercourses are classified as all major watercourses (Levels 1-5) in the topographic dataset

Hydrography_Features_SWCC. The classification limits the influence of the criterion to less than 600m

from the watercourse.


3 - from 0 – 500m (Highest Value) 2 - from 500 – 600m 1 - Over 600

Figure 55: Proximity to Major Watercourses


3.4.3.2 Wild River Catchments

The Hydrographic Subcatchments dataset was classified to identify recognised 'Wild River" catchments

in the SWCC area: Doggerup Creek and the Deep River; as well as the Shannon River basin which was

reserved for its outstanding naturalness and contains a small amount of clearing. These are all

catchments where the existing values would be improved with revegetation.

Figure 56: Wild River Catchments


3.4.4 Proximity to Priority Linkages

A key aim of conservation planting is to assist in reconnecting conservation assets in the landscape. Two

major components inform this aim:

Revegetation along key linkages, and

Revegetation in areas where there is good potential to reconnect existing fragmented landscapes.

The priority linkages used are the SWCC linkage - Distance from South West Catchment Council

Preliminary Ecological Linkages Axis Lines. This is an expanded version of the South West Regional

Ecological Linkages (SWREL) Axis Lines. Gridded at 200m cells and distance calculated by Euclidean

Distance (m).

Layer 'Proximity to Priority Linkages' is generated from primary data 'swcc_dist'


4 - from 0 – 250m (Highest Value)

3 - from 250 – 500m

2 - from 500 – 1000m

1 - Over 1000m

Figure 57: Proximity to Priority Linkages


3.4.5 Potential for infill.

Potential for infill identifies areas that have potential for strategic plantings to increase existing values

and improve landscape connectivity. This uses two criterion from Component 3 (% Clearing & Landscape

Fragmentation (number of patches)) – but values them in different ways. It is aimed at identifying areas

where higher levels of clearing are associated with large numbers of patches – indicating that planting

can be used for connect patches.

Figure 58: Potential for Infill


3.4.5.1 % Clearing

Native Vegetation - % uncleared within 2km (2014) measures the amount of native vegetation within

2km (of each cell) as a percentage.

In this case it is split into 3 classes

3 - from 1 – 30% (Highest Value) 2 - from 30 – 60% 1 - from 60 – 100%

Figure 59: Percentage of local vegetation clearing


3.4.5.2 Areas with high fragmentation

The second part of potential for infill identifies areas with high levels of fragmentation. The dataset

counts the number of patches of vegetation within 5km to indicate the extent to which vegetation has

been cut up (fragmented).

This is split into 10 classes

1 - from 1 2 - from 25 3 - from 50 4 - from 75 5 - from 100 6 - from 125 7 - from 150 8 - from 200 9 - from 250 10 - from 300 (Highest Value)

Figure 60: Level of vegetation fragmentation


3.4.6 Component 4 Output - Locations for carbon plantings to enhance habitat


Layer 'Areas where we want Biodiversity Plantings (All Criteria Multiply)' is a composite layer producing 3

classes – No, Low and High-Priority High-Biodiversity Plantings.

The composite function is generated from the product of:

1 x '* Rivers & Buffer Zones' 2 x '*Areas Close to Component 3 Biodiversity/Conservation Areas Final' 1 x '*Potential for Infill' 3 x '*Proximity to known Biodiversity Assets' 2 x '*Proximity to Priority Linkages' 1 x 'cleared_2014'


1 - up to 0.02005758– No High-Biodiversity Plantings, (blue) 2 - up to 0.04011515 - Low Priority High-Biodiversity Plantings (green) 3 - above 0.04011515 - High Priority High-Biodiversity Plantings (red)

Figure 61: MCAS-S Final Output – Component 4


4. RESULTS AND OUTPUTS

This section presents the results of the Components in two separate ways

Component Maps – showing the results of the components as high, low or no priority planting or

protection areas.

This first set of basemaps does not account for competing demands (ie from other components).

Combined Components – The maps are produced by combining the output of Component 1 with

Components 2 & 4, as presented below.

These maps show where acceptable plantings would occur in the light of areas to be protected from

plantings. Note that this still produces three separate maps which need to be interpreted

collectively.

Note that the maps in this section have been coloured using the same colour scheme as the previous

MCAS-S outputs.

Figure 62: Component 5 design

The resolution of conflict, and the provision of easily-interpreted recommendations requires the

combination of these separate outcomes (for the three types of planting) into a single Outcomes map.

Such an outcome requires a hierarchy of uses which indicates which outcomes have precedence when

they overlap. This is carried out in Section 5.

Component 4

High Biodiversity

Carbon Planting

Acceptable:

Low Biodiversity

Carbon Planting

Acceptable:

Any Carbon

Planting

Acceptable:

High Biodiversity

Carbon Planting

High OR Low

Biodiversity

Planting

Component 1

Protection from

Carbon Planting

Component 2

Low Biodiversity

Carbon Planting


4.1 Component Maps

Figure 63: Component 1 – Landscapes that need to be protected from Carbon Plantings


Figure 64: Component 2 – Locations for Low-Biodiversity Carbon Plantings


Figure 65: Component 3 – Identified Areas of High Biodiversity Value/Conservation Value


Figure 66: Component 4 – Locations for carbon plantings to enhance habitat corridors and protect high biodiversity areas


4.2 C5 - Combining Components.

We have developed an MCAS Component to combine the outputs from Components 1, 2 & 4. This

has allowed the production of maps of locations for the two major classes of planting (or both) in

the context of the restrictions on planting from Component 1.

Figure 67: Component 5 – Combinations of C1, C2 & C4

The outputs from Component 5 indicate, individually, locations for the three types of planting that

exist: High Biodiversity, Low-Biodiversity (plantations), and the third area which is either – i.e. the

area would be suited to either type. Within the workshop it was proposed that these areas actually

represent the highest priority areas for carbon planting.


Figure 68: C5 – Locations for High-Biodiversity Planting


Figure 69: C5 – Locations for Low-Biodiversity Planting


Figure 70: C5 – Locations for Any Planting


5. COMBINING THE COMPONENTS FOR DECISION

SUPPORT

The results maps presented in the previous section provide multiple options for any one cell, and so

do not give clear direction to SWCC staff. In order to provide this clearer direction, we have

combined the results for the three components (1, 2 & 4) in a single map. 5

Producing this map requires the adoption of a hierarchy of outcomes to select a preferred outcome

from multiple options for each cell. For example, if a cell was indicated as being Low Priority for

High-Biodiversity Planting, and High Priority for Low-Biodiversity planting and Low Priority for

Protection, which usage should be preferred? The hierarchy provides the answer.

The hierarchy of outcomes is based on discussion in the working group about the issues generally

surrounding plantations and carbon plantations in particular. It is shown in the figure below.

Figure 71: Outcome Hierarchy

This hierarchy provides a resolution for each conflict in the matrix of possible outcomes, listed in

Table 1 below. The highest ranking outcome is indicated with green shading, and in some cases may

be 2 cells where planting outcomes are equally ranked.

5 This final map has been created in ArcGIS by making a grid of each component output, and multiplying the

grids together to create a final grid with every different combination of component outputs indicated by a unique cell value.

Full Protection

High Priority High-Biodiversity Planting

High Priority Low-Biodiversity Planting

Low Priority Protection

Low Priority High-Biodiversity Planting

Low Priority Low-Biodiversity Planting

No Protection or No Planting


Grid Value

Outcome for High Biodiversity Planting

Outcome for Low Biodiversity Planting

Outcome for Protection

14 No High-Biodiversity Planting No Low Biodiversity Planting No Protection

26 Low Priority HBD Planting No Low Biodiversity Planting No Protection

28 No High-Biodiversity Planting Low Priority Low BD Planting No Protection

34 High Priority HBD Planting No Low Biodiversity Planting No Protection

42 No High-Biodiversity Planting No Low Biodiversity Planting Low Priority Protection

52 Low Priority HBD Planting Low Priority Low BD Planting No Protection

56 No High-Biodiversity Planting High Priority Low LBD Planting No Protection

68 High Priority HBD Planting Low Priority Low BD Planting No Protection

70 No High-Biodiversity Planting No Low Biodiversity Planting Full Protection

78 Low Priority HBD Planting No Low Biodiversity Planting Low Priority Protection

84 No High-Biodiversity Planting Low Priority Low BD Planting Low Priority Protection

102 High Priority HBD Planting No Low Biodiversity Planting Low Priority Protection

104 Low Priority HBD Planting High Priority Low LBD Planting No Protection

130 Low Priority HBD Planting No Low Biodiversity Planting Full Protection

136 High Priority HBD Planting High Priority Low LBD Planting No Protection

140 No High-Biodiversity Planting Low Priority Low BD Planting Full Protection

156 Low Priority HBD Planting Low Priority Low BD Planting Low Priority Protection

168 No High-Biodiversity Planting High Priority Low LBD Planting Low Priority Protection

170 High Priority HBD Planting No Low Biodiversity Planting Full Protection

204 High Priority HBD Planting Low Priority Low BD Planting Low Priority Protection

260 Low Priority HBD Planting Low Priority Low BD Planting Full Protection

280 No High-Biodiversity Planting High Priority Low LBD Planting Full Protection

312 Low Priority HBD Planting High Priority Low LBD Planting Low Priority Protection

340 High Priority HBD Planting Low Priority Low BD Planting Full Protection

408 High Priority HBD Planting High Priority Low LBD Planting Low Priority Protection

520 Low Priority HBD Planting High Priority Low LBD Planting Full Protection

680 High Priority HBD Planting High Priority Low LBD Planting Full Protection

Green Shading indicates priority outcome.

Table 1: Decision Matrix - All Possible Combinations of Outcomes from Components 1, 2 & 4.

Each possible outcome leads to a single resolution, as shown in Table 2. We have kept the option of

listing and mapping these with the attached description, which indicates the alternative options for

the cell.


Value Outcome Details

28 Low-Biodiversity planting Low priority Low-Biodiversity Planting

42 No Planting Low Priority Protection - no Planting priorities

52 High-Biodiversity planting

Low Priority High-Biodiversity Planting overriding Low Priority Low-

Biodiversity Planting

56 Low-Biodiversity planting High Priority Low-Biodiversity Planting


High Priority High-Biodiversity Planting overriding Low Priority Low-


70 No Planting Full Protection - no Planting priorities

78 No Planting

Low Priority Protection overriding Low Priority Low-Biodiversity

Planting

84 No Planting

Low Priority Protection overriding Low Priority High-Biodiversity

Planting


High Priority High-Biodiversity Planting overriding Low-Priority

Protection

104 Low-Biodiversity planting

High Priority Low-Biodiversity Planting overriding Low Priority High-


130 No Planting Full Protection overriding Low Priority Low-Biodiversity Planting

136 Any Planting High Priority Plantings - EITHER High or Low-Biodiversity Plantings

140 No Planting Full Protection overriding Low Priority Low-Biodiversity Planting

156 No Planting

Low Priority Protection overriding Low Priority High and Low-

Biodiversity Plantings


High Priority Low-Biodiversity Planting overriding Low Priority

Protection

170 No Planting Full Protection overriding High Priority High-Biodiversity Plantings


High Priority High-Biodiversity Planting overriding Low-Priority

Protection and Low Priority High-Biodiversity Planting

260 No Planting

Full Protection overriding Low Priority High AND Low-Biodiversity

Planting

280 No Planting Full Protection overriding High Priority Low-Biodiversity Plantings


High Priority Low-Biodiversity Planting overriding Low Priority High-

Biodiversity Planting and Low Priority Protection

340 No Planting

Full Protection overriding High Priority High-Biodiversity and Low

Priority Low-Biodiversity Plantings

408 Any Planting

High Priority for High AND Low-Biodiversity Planting overriding Low

Priority Protection

520 No Planting

Full Protection overriding Low Priority High-Biodiversity and High

Priority Low-Biodiversity Planting

680 No Planting

Full Protection overriding High Priority High-Biodiversity AND High

Priority Low-Biodiversity Plantings

Table 2: Decision Matric - Priority Options and Description

The mapping of these provides the best options for each cell (as shown in Figure 72: Decision Matrix

- Priority Outcomes Mapped). This represents the final recommendations arising out of the entire

process.


Figure 72: Decision Matrix - Priority Outcomes Mapped


Figure 73: Decision Matrix - Priority Outcome Descriptions


6. PROJECT DELIVERABLES

The project deliverables are as follows:

Project Report

This document.

Project Presentations

As produced for the project and presented to the Working Group and SWCC Board

ArcGIS map documents & processed data.

Three ArcGIS map documents are provided, two that were used for project data processing, and a

single final map document which contains the datasets and maps used for the project outputs in

Sections 4 and 5.

SWCC Datasets 1.mxd

SWCC Dataset 3.mxd

SWCC Plantation Direction.mxd

These map documents include a series of simple ArcGIS tools that were used for data processing

and can be used in the future by SWCC.

MCAS-S Models

All models used in the project are provided in a single MCAS-S folder:

Figure 74: MCAS-S Files Provided

MCAS-S processed datasets for SWCC

All datasets processed to MCAS-S standards are included in the MCAS-S folder. These are listed in

Appendix 7.


7. APPENDICES

7.1 Appendix 1 - GIS Datasets available in MCAS-S Format

Agricultural

Barley - Projected Yield Change % 2005 - 2050. SRES A2

Canola - Projected Yield Change % 2005 - 2050. SRES A2

Lupins - Projected Yield Change % 2005 - 2050. SRES A2

Oats - Projected Yield Change % 2005 - 2050. SRES A2

Wheat - Projected Yield Change % 2005 - 2050. SRES A2

Report card

Soil Acidification Condition

Soil Acidification Trend

Soil Carbon Abundance

Soil Carbon Trend

Soil compaction hazard

Soil compaction Trend

Water erosion hazard

Water erosion Trend

Water repellence condition

Water repellence Trend

Wind erosion hazard

Wind erosion Trend

Boundaries

Conservation Reserves

Distance from Conservation Reserves

Crown Reserves by Class

Distance to Crown Reserves

DEC Managed Lands and Waters (ISO 19139)

IBRA Subregions

LGA boundaries

Mining Tenements by Type

UNVESTED Crown Reserves by Class

Cadastre

Property Area

Catchments

Water Resource Recovery Catchments

Water Resource Recovery Catchments - % cleared land in catchment

Water Resource & Biodiversity Recovery Catchments

Public Drinking Water Source Areas (type)

Protection Zones for PDWSA (Public Drinking Water Source Areas)



Climate

NCCARF Biological Refugia under Climate Change

CSIRO Mk3.5 Projected climatic parameters – Scenario A2 for 2080

Mean Temp Autumn Change (Degrees) by 2080 from Current

Mean Temp Spring Change (Degrees) by 2080 from Current

Mean Temp Summer Change (Degrees) by 2080 from Current

Mean Temp Winter Change (Degrees) by 2080 from Current

Mean Temp Year Change (Degrees) by 2080 from Current

Mean Temp SUMMER Change (Degrees) by 2080 from Current

Mean Temp YEAR Change (Degrees) by 2080 from Current

Mean Rainfall AUTUMN Change (mm) by 2080 from Current

Mean AUTUMN Rainfall % Change (mm) by 2080 from Current

Projected AUTUMN Rainfall (mm) by 2080

Projected MAY-OCTOBER Rainfall (mm) by 2080

Mean MAY - OCTOBER Rainfall % Change (mm) by 2080 from Current

Mean SPRING Rainfall % Change (mm) by 2080 from Current

Mean SPRING Rainfall Change (mm) by 2080 from Current

Projected SPRING Rainfall (mm) by 2080

Mean SUMMER Rainfall % Change (mm) by 2080 from Current

Mean Rainfall SUMMER Change (mm) by 2080 from Current

Projected SUMMER Rainfall (mm) by 2080

Mean Rainfall WINTER Change (mm) by 2080 from Current

Projected WINTER Rainfall (mm) by 2080

Mean WINTER Rainfall % Change (mm) by 2080 from Current

Projected ANNUAL Rainfall (mm) by 2080

Mean ANNUAL Rainfall Change (mm) by 2080 from Current

Mean ANNUAL Rainfall % Change (mm) by 2080 from Current

CSIRO Mk3.5 Modelled climatic parameters – Scenario A2 for 2080 – downscaled using kriging

Mean Temp Autumn Change (Degrees) by 2080 from Current

Mean Temp Spring Change (Degrees) by 2080 from Current

Mean Temp Summer Change (Degrees) by 2080 from Current

Mean Temp Winter Change (Degrees) by 2080 from Current

Mean Temp Year Change (Degrees) by 2080 from Current

Mean Temp SUMMER Change (Degrees) by 2080 from Current

Mean Temp YEAR Change (Degrees) by 2080 from Current

Mean Rainfall AUTUMN Change (mm) by 2080 from Current

Mean AUTUMN Rainfall % Change (mm) by 2080 from Current

Projected AUTUMN Rainfall (mm) by 2080



Mean SPRING Rainfall % Change (mm) by 2080 from Current

Mean SPRING Rainfall Change (mm) by 2080 from Current

Projected SPRING Rainfall (mm) by 2080

Mean SUMMER Rainfall % Change (mm) by 2080 from Current

Mean Rainfall SUMMER Change (mm) by 2080 from Current

Projected SUMMER Rainfall (mm) by 2080

Mean Rainfall WINTER Change (mm) by 2080 from Current

Projected WINTER Rainfall (mm) by 2080

Mean WINTER Rainfall % Change (mm) by 2080 from Current



Mean ANNUAL Rainfall Change (mm) by 2080 from Current

Mean ANNUAL Rainfall % Change (mm) by 2080 from Current

Coastal & Offshore

Commonwealth Marine Reserves

State Marine Parks

Cultural

Aboriginal Heritage Sites - Site Access

Aboriginal Heritage Sites - Site Status

Dieback

Distance to Dieback Occurrence (Points)

Dieback Occurrence (polygons)

Flora & Fauna

Index of Native Endemic Species - Distribution < 10000 sq km

Priority 1 (Rare) Flora

Distance to Priority 1 Rare Flora

Distance to Threatened Flora

Distance to Rare, Threatened or specially protected Fauna.

Distance to Priority Threatened Fauna.

Threatened Flora.

Distance to Threatened (Declared Rare) Flora.

Groundwater

Proclaimed Groundwater Areas

Hydrography

Geomorphic Wetlands - Classification, Swan Coastal Plain

Register areas for Lakes EPP, 1992

Distance from EPP Wetlands

ELPW (Estuaries, Lakes, Pools & Watercourses)

Distance from ELPW

Ramsar Wetlands

Distance from Ramsar Wetlands

Water Polygons - TOPOGRAPHIC DATA DICTIONARY

Streams

Distance from ALL watercourses

Distance from MAJOR watercourses

Land Capability

Land Capability for Annual Horticulture

Land Capability for Perennial Horticulture

Land Capability for Vines

Land Capability for Dry Cropping

Land Capability for Dry Cropping Minimum Tillage

Land Capability for Grazing

Land Capability for E. Globulus


Linkages

Distance from South West Regional Ecological Linkages Axis Lines

Distance from South West Catchment Council Preliminary Ecological Linkages Axis Lines

Salinity


Salinity Extent

Hydrozone salinity risk 2012

Modelled in MCAS

Future Salinity (Short term)

Future Salinity (Medium term)

Distance from Future Salinity (Short term)

Distance from Future Salinity (Medium term)

Soils & Landforms

Granite Morphology - TOPOGRAPHIC DATA DICTIONARY

Distance from Granite Morphology - TOPOGRAPHIC DATA DICTIONARY

TEC & PECs

Threatened and Priority Ecological Community (TecPecs)

Threatened and Priority Ecological Community (Distance to ALL TecPecs)

Threatened and Priority Ecological Community (Distance to "Critically Endangered" TecPecs)

Threatened and Priority Ecological Community (Distance to "Endangered" TecPecs)

Threatened and Priority Ecological Community (Distance to "Priority 1" TecPecs)



Threatened and Priority Ecological Community (Distance to "Vulnerable" TecPecs)

Vegetation

2012

Cleared Areas 2012 (not covered by Native Vegetation 2012)

Native Vegetation 2012 - Distance from

Native Vegetation Extent 2012

Native Vegetation Contiguous Area 2012

Fragmentation 2012

Native Vegetation - % within 2km (2012)

Native Vegetation - number of patches within 1km (2012)


2014


Native Vegetation 2014 - Distance from



Fragmentation 2014





Vegetation Associations

% of each Vegetation Association within DEC Reserves (2014)

% of each Vegetation Association remaining (2014)

Vegetation Association - Reduction in area (%) to 2014

Variety of Vegetation Associations within 2km (2014)


Vegetation Patch - % of remaining association area - 2014


7.2 Appendix 2 - GIS Datasets Used in the SWCC modelling

Agricultural

Barley - Projected Yield Change % 2005 - 2050. SRES A2

Canola - Projected Yield Change % 2005 - 2050. SRES A2

Lupins - Projected Yield Change % 2005 - 2050. SRES A2

Oats - Projected Yield Change % 2005 - 2050. SRES A2

Wheat - Projected Yield Change % 2005 - 2050. SRES A2

Boundaries

Distance from Conservation Reserves

Distance to Crown Reserves

Catchments

Water Resource & Biodiversity Recovery Catchments

Protection Zones for PDWSA (Public Drinking Water Source Areas)


Climate

NCCARF Biological Refugia under Climate Change




Cultural

Aboriginal Heritage Sites - Site Access

Aboriginal Heritage Sites - Site Status

Dieback

Distance to Dieback Occurrence (Points)

Flora & Fauna

Index of Native Endemic Species - Distribution < 10000 sq km

Priority 1 (Rare) Flora

Distance to Priority 1 Rare Flora

Distance to Threatened Flora

Threatened Flora.

Distance to Threatened (Declared Rare) Flora.

Hydrography

Distance from EPP Wetlands

Distance from ELPW (Estuaries, Lakes, Pools & Watercourses)

Distance from Ramsar Wetlands

Water Polygons - TOPOGRAPHIC DATA DICTIONARY

Streams

Distance from MAJOR watercourses


Land Capability

Land Capability for Annual Horticulture

Land Capability for Perennial Horticulture

Land Capability for Vines

Land Capability for Dry Cropping

Land Capability for Dry Cropping Minimum Tillage

Land Capability for Grazing

Linkages

Distance from South West Catchment Council Preliminary Ecological Linkages Axis Lines

Salinity


Salinity Extent

Hydrozone salinity risk 2012

Modelled in MCAS

Future Salinity (Short term)

Future Salinity (Medium term)

Distance from Future Salinity (Short term)

Distance from Future Salinity (Medium term)

Soils & Landforms

Distance from Granite Morphology - TOPOGRAPHIC DATA DICTIONARY

TEC & PECs

Threatened and Priority Ecological Community (TecPecs)

Vegetation

2014




Fragmentation 2014



Vegetation Associations

% of each Vegetation Association within DEC Reserves (2014)

% of each Vegetation Association remaining (2014)

Vegetation Association - Reduction in area (%) to 2014


Vegetation Patch - % of remaining association area - 2014


7.3 Appendix 3 - South West Catchment Council’s

Biosequestration Working Group - Terms of Reference

Purpose

To facilitate the development of SWCC’s biosequestration and biodiversity planting policy and

criteria.

The role of the Biosequestration Working Group is to identify and define the criteria that implement

this policy, and that were used to provide the relevant data required for the computer based model

that produces the maps.

The Working Group is made up people experienced in areas relevant to this assessment, including

Forestry, Agriculture, Carbon Industry, Conservation Management, Water, Natural Resource

Management, Local and State Government.

This Biosequestration mapping is a component of the NRM Planning for Climate Change Stream 1

project.

SWCC has engaged the services of Simon Neville-Ecotones to collate existing regional scale datasets,

to facilitate the Working Group process, and to determine where CFI plantings would have a

positive or negative impact in the landscape.

The final product will be in the form of a series of maps, a decision support tree and an outline of

the methodology used.

The currently identifying areas where plantings will have;

Biodiversity co-benefits

Agricultural benefits (offering alternatives in poor agricultural production)

Positive impacts on salinity affected land

No negative impact on water tables

No impact upon public drinking water provision.

Membership

The Technical Working Group Membership to include:

Steve Blyth (SWCC Board and Nursery Manager)

Richard Moore (Australia Forest Growers Association)

Paul Raper (Dept. of Agriculture and Food WA)

Jamie Bowyer (Dept. of Agriculture and Food WA)

Kim Williams (Dept. of Parks and Wildlife)

James Duggie (Office of Climate Change)

Mark Sewell (Executive Officer, Warren Catchment Council)

Mick Quartermaine (Blackwood Basin Group)


Renata Zelinova (WALGA)

Dan Wildy (Rural Fares)

Dale Miles (Greening Australia)

Ian Dumbrell (FPC)

Cathie Derrington (Dept. of Water)

Steve Ewings (SWCC Sustainable Agriculture Program Manager)

Mike Christensen (SWCC Environment Program Manager

Leonie Offer (SWCC Climate Change Project Manager)

Roles and responsibilities

The Working Group:

Provides specific technical advice on the criteria, their ratings, and the weightings of the

criteria in the assessment.

Fosters collaboration with all parties

Builds and strengthens partnerships

Facilitates timely feedback to the service provider

Ensures successful delivery of the product

The consultant will endeavour to achieve consensus on all decision however SWCC reserves

the right to resolve any disputes arising out discussions.


8. REFERENCES

ABARES (2011). Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S): User Guide. Australian

Bureau of Agricultural and Resource Economics and Sciences, Canberra. www.abares.gov.au

Aguilera, P.A., A. Fernández, R. Fernández, R. Rumí & A. Salmerón, (2011). Bayesian networks in

environmental modelling, Environmental Modelling & Software 26 (2011) 1376-1388

Aitkenhead, M.J. & I.H. Aalders, (2009). Predicting land cover using GIS, Bayesian and evolutionary algorithm

methods. Journal of Environmental Management 90, 236-250

Boteva, D, Griffith, G. and Dimopoulos, P. (2004). Evaluation and mapping of the conservation significance of

habitats using GIS: an example from Crete, Greece. Journal for Nature Conservation 12, 237—250

Dlamini, W.M. (2010) A bayesian belief network analysis of factors influencing wildfire occurrence in

Swaziland. Environmental Modelling & Software 25:199-208

Froend, R. & R. Loomes (2004), Approach to Determination of Ecological Water Requirements of

Groundwater Dependent Ecosystems in Western Australia. A report to the Department of

Environment, 2004-12

Froend, R. & R Loomes (2006) Determination of Ecological Water Requirements for Wetland and Terrestrial

Vegetation – Southern Blackwood and Eastern Scott Coastal Plain. CEM report no. 2005-07, ECU

Joondalup, March 2006

Glendining NS and Pollino CA. (2012). Development of Bayesian Network decision support tools to support

river rehabilitation works in the Lower Snowy River. Hum Ecol Risk Assessment 18(1):92–114

Gobbi, M., Riservato, E., Bragalanti, N., Lencioni,V. (2012) An expert-based approach to invertebrate

conservation: Identification of priority areas in central-eastern Alps. Journal for Nature Conservation

20 (2012) 274– 279

Hart, BT and Pollino, CA (2009). Bayesian modelling for risk-based environmental water allocation, Waterlines

report, National Water Commission, Canberra

Lombard, A.T., Cowling M.C., Vlok J.H.J. & Fabricius C (2010). Designing Conservation Corridors in Production

Landscapes: Assessment Methods, Implementation Issues, and Lessons Learned. Ecology and Society

15(3): 7. [online] URL: http://www.ecologyandsociety.org/vol15/iss3/art7/

Margules, C. (1989) Introduction to some Australian developments in Conservation Evaluation. Biological

Conservation 50 1-11.

Margules, C. and Usher, M.B. (1981) Criteria used in assessing wildlife conservation potential: A Review.

Biological Conservation 21 79-109.

Margules, C.R. and Nicholls, A.O. (1988) Selecting Networks of Reserves to Maximise Biological Diversity.

Biological Conservation 43 63-76.

Margules, C.R., Higgs, A.J. and Rafe, R.W. (1982). Modern Biogeographic Theory: Are there any Lessons for

Nature reserve Design? Biological Conservation 24 115-128

http://www.abares.gov.au/

http://www.ecologyandsociety.org/vol15/iss3/art7/


McAbee, K., S. Albeke & N.P. Nibbelink (2008) Improving Imperiled Species Management through Spatially-

Explicit Decision Tools. Proceedings of the 6th Southern Forestry and Natural Resources GIS

Conference (2008)

McNeill J, MacEwan R and Crawford D (2006), Using GIS and a land use impact model to assess risk of soil

erosion in West Gippsland, Applied GIS 2:19.1–19.16.

Neville, S.D. (2009). Assessment of Conservation Value, Fitz-Stirling Area: Modelling report. Report to

Gondwana Link, Ecotones & Associates, William Bay WA.

Ortigos, GR, De Leo, GA and Gatto M. (2000). VVF: integrating modelling and GIS in a software tool for habitat

suitability assessment. Environmental Modelling & Software 15 (2000) 1–12

Panitsa. M, Koutsias, N, Tsiripidis, I, Zotos A. & , Dimopoulos, P. (2011). Species-based versus habitat-based

evaluation for conservation status assessment of habitat types in the East Aegean islands (Greece).

Journal for Nature Conservation 19 , 269– 275

Pollino, CA, Thomas CR and Hart BT. (2012) Introduction to Models and Risk Assessment. Hum Ecol Risk

Assessment, 18: 13–15, 2012

Reside, AE, VanDerWal, J, Phillips, B, Shoo, LP, Rosauer, DF, Anderson, BJ, Welbergen, J, Moritz, C, Ferrier, S,

Harwood, TD, Williams, KJ, Mackey, B, Hugh, S, Williams, SE 2013 Climate change refugia for

terrestrial biodiversity: Defining areas that promote species persistence and ecosystem resilience in

the face of global climate change, National Climate Change Adaptation Research Facility, Gold Coast.

Smith CS, Howes AL, Price B, and McAlpine CA (2007), ‘Using a Bayesian belief network to predict suitable

habitat of an endangered mammal—The Julia Creek dunnart (Sminthopsis douglasi)’, Biological

Conservation 139:333–347.

Sommer, B. and Froend, R. (2010). Gnangara mound ecohydrological study. Final Report to the Western

Australian Government, Department of Water. Report No. CEM2010-20. Joondalup: Centre for

Ecosystem Management, Edith Cowan University.

Sposito, V., Benke, K., Pelizaro, C. & Wyatt, R. (2009) - Application of GIS-based computer modelling to

planning for adaption to climate change in rural areas, Applied GIS, 5(3), 1-25

Swetnam, R.D., J. O. Mountford, A. C. Armstrong, D. J. G. Gowing, N.J. Brown, S. J. Manchester & J. R. Treweek

(1998). Spatial relationships between site hydrology and the occurrence of grassland of conservation

importance: a risk assessment with GIS. Journal of Environmental Management 54, 189–203.

Voinov, A. & F. Bousquet (2010). Modelling with stakeholders. Environmental Modelling & Software 25, 1268-

1281

Vernon, L and D. van Gool, (2006). Potential impacts of climate change on agricultural land use suitability

(Canola). Resource Management Technical Report 303, Department of Agriculture, WA.)

spatially representing south west catchments council ... · datasets identified by the working...

Documents