Spatially representing South West Catchments Council ... · datasets identified by the working...
Transcript of 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.
Spatially representing the SWCC priorities for biosequestration plantations 3
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.
4 Ecotones & Associates
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.
Spatially representing the SWCC priorities for biosequestration plantations 5
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.
6 Ecotones & Associates
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.
Spatially representing the SWCC priorities for biosequestration plantations 7
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
8 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 9
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
10 Ecotones & Associates
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.
Spatially representing the SWCC priorities for biosequestration plantations 11
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
12 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 13
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
14 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 15
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.
16 Ecotones & Associates
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.
Spatially representing the SWCC priorities for biosequestration plantations 17
Figure 10: Projected Yield Sustainability
18 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 19
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
20 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 21
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
22 Ecotones & Associates
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.
The composite function is generated from the sum of:
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
Spatially representing the SWCC priorities for biosequestration plantations 23
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.
24 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 25
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
26 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 27
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)
28 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 29
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
30 Ecotones & Associates
3.2.2 Potential Salinity Areas
Layer 'Potential Salinity Areas' is a composite layer producing 3 classes
The composite function is generated from the sum of:
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
Spatially representing the SWCC priorities for biosequestration plantations 31
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
32 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 33
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).
The composite function is generated from the sum of:
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)
34 Ecotones & Associates
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.
The composite function is generated from the sum of:
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
Spatially representing the SWCC priorities for biosequestration plantations 35
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
36 Ecotones & Associates
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.
Spatially representing the SWCC priorities for biosequestration plantations 37
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
38 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 39
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)
40 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 41
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
42 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 43
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
44 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 45
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
46 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 47
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
48 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 49
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
50 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 51
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.
52 Ecotones & Associates
Figure 44: Projected Climate Refugia 2085
Spatially representing the SWCC priorities for biosequestration plantations 53
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
54 Ecotones & Associates
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
The composite function is generated from the sum of:
6 x '* High Value Biodiversity Areas' 2 x '* Potential Climate Refugia' 1 x '* Proximity to Threatened Species'
The result is classed according to this table:
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
Spatially representing the SWCC priorities for biosequestration plantations 55
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.
56 Ecotones & Associates
3.4 Component 4 – Locations for carbon plantings to enhance habitat
corridors and protect high biodiversity areas
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
Spatially representing the SWCC priorities for biosequestration plantations 57
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
58 Ecotones & Associates
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.
Split into 3 classes
3 - from 0 – 500m (Highest Value) 2 - from 500 – 1000m 1 - from 1000 m (Lowest value)
Figure 50: Distance to Conservation Reserve
Spatially representing the SWCC priorities for biosequestration plantations 59
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.
Split into 3 classes
3 - from 0 – 500m (Highest Value) 2 - from 500 – 1000m 1 - from 1000 m (Lowest value)
Figure 51: Distance to EPP Wetlands
60 Ecotones & Associates
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.
Split into 3 classes
3 - from 0 – 500m (Highest Value) 2 - from 500 – 1000m 1 - Over 1000 m (Lowest value)
Figure 52: Distance to Ramsar Wetlands
Spatially representing the SWCC priorities for biosequestration plantations 61
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.
Split into 3 classes
3 - from 0 – 200m (Highest Values) 2 - from 200 – 500m 1 - Over 500
Figure 53: Distance from Water features (Topographic estuaries, lakes, pool & watercourses)
62 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 63
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.
Split into 3 classes
3 - from 0 – 500m (Highest Value) 2 - from 500 – 600m 1 - Over 600
Figure 55: Proximity to Major Watercourses
64 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 65
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'
Split into 4 classes
4 - from 0 – 250m (Highest Value)
3 - from 250 – 500m
2 - from 500 – 1000m
1 - Over 1000m
Figure 57: Proximity to Priority Linkages
66 Ecotones & Associates
Spatially representing the SWCC priorities for biosequestration plantations 67
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
68 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 69
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
70 Ecotones & Associates
3.4.6 Component 4 Output - Locations for carbon plantings to enhance habitat
corridors and protect high biodiversity areas
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'
The result is classed according to this table:
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
Spatially representing the SWCC priorities for biosequestration plantations 71
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
72 Ecotones & Associates
4.1 Component Maps
Figure 63: Component 1 – Landscapes that need to be protected from Carbon Plantings
Spatially representing the SWCC priorities for biosequestration plantations 73
Figure 64: Component 2 – Locations for Low-Biodiversity Carbon Plantings
74 Ecotones & Associates
Figure 65: Component 3 – Identified Areas of High Biodiversity Value/Conservation Value
Spatially representing the SWCC priorities for biosequestration plantations 75
Figure 66: Component 4 – Locations for carbon plantings to enhance habitat corridors and protect high biodiversity areas
76 Ecotones & Associates
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.
Spatially representing the SWCC priorities for biosequestration plantations 77
Figure 68: C5 – Locations for High-Biodiversity Planting
78 Ecotones & Associates
Figure 69: C5 – Locations for Low-Biodiversity Planting
Spatially representing the SWCC priorities for biosequestration plantations 79
Figure 70: C5 – Locations for Any Planting
80 Ecotones & Associates
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
Spatially representing the SWCC priorities for biosequestration plantations 81
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.
82 Ecotones & Associates
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
68 High-Biodiversity planting
High Priority High-Biodiversity Planting overriding Low Priority Low-
Biodiversity Planting
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
102 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-
Biodiversity Planting
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
168 Low-Biodiversity planting
High Priority Low-Biodiversity Planting overriding Low Priority
Protection
170 No Planting Full Protection overriding High Priority High-Biodiversity Plantings
204 High-Biodiversity planting
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
312 Low-Biodiversity planting
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.
Spatially representing the SWCC priorities for biosequestration plantations 83
Figure 72: Decision Matrix - Priority Outcomes Mapped
84 Ecotones & Associates
Figure 73: Decision Matrix - Priority Outcome Descriptions
Spatially representing the SWCC priorities for biosequestration plantations 85
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.
86 Ecotones & Associates
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)
Wild River Catchments
Spatially representing the SWCC priorities for biosequestration plantations 87
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
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
88 Ecotones & Associates
Projected ANNUAL Rainfall (mm) by 2080
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
Spatially representing the SWCC priorities for biosequestration plantations 89
Linkages
Distance from South West Regional Ecological Linkages Axis Lines
Distance from South West Catchment Council Preliminary Ecological Linkages Axis Lines
Salinity
Salinity Hazard (height above valley floor)
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 "Priority 2" TecPecs)
Threatened and Priority Ecological Community (Distance to "Priority 3" 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)
Native Vegetation - number of patches within 5km (2012)
2014
Cleared Areas 2014 (not covered by Native Vegetation 2014)
Native Vegetation 2014 - Distance from
Native Vegetation Extent 2014
Native Vegetation Contiguous Area 2014
Fragmentation 2014
Native Vegetation - % within 2km (2014)
Native Vegetation - number of patches within 1km (2014)
Native Vegetation - number of patches within 5km (2014)
90 Ecotones & Associates
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)
Variety of Vegetation Associations within 5km (2014)
Vegetation Patch - % of remaining association area - 2014
Spatially representing the SWCC priorities for biosequestration plantations 91
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)
Wild River Catchments
Climate
NCCARF Biological Refugia under Climate Change
Projected MAY-OCTOBER Rainfall (mm) by 2080
Mean MAY - OCTOBER Rainfall % Change (mm) by 2080 from Current
Projected ANNUAL Rainfall (mm) by 2080
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
92 Ecotones & Associates
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 Hazard (height above valley floor)
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
Cleared Areas 2014 (not covered by Native Vegetation 2014)
Native Vegetation Extent 2014
Native Vegetation Contiguous Area 2014
Fragmentation 2014
Native Vegetation - % within 2km (2014)
Native Vegetation - number of patches within 5km (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 5km (2014)
Vegetation Patch - % of remaining association area - 2014
Spatially representing the SWCC priorities for biosequestration plantations 93
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)
94 Ecotones & Associates
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.
Spatially representing the SWCC priorities for biosequestration plantations 95
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
96 Ecotones & Associates
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.)