Landscapes Toolkit: an integrated modelling framework to assist stakeholders in exploring options...

RESEARCH ARTICLE

Landscapes Toolkit: an integrated modelling frameworkto assist stakeholders in exploring options for sustainablelandscape development

Iris C. Bohnet • Peter C. Roebeling • Kristen J. Williams • Dean Holzworth •

Martijn E. van Grieken • Petina L. Pert • Frederieke J. Kroon •

David A. Westcott • Jon Brodie

Received: 2 June 2010 / Accepted: 26 July 2011 / Published online: 6 August 2011

� Springer Science+Business Media B.V. 2011

Abstract At present, stakeholders wishing to

develop land use and management change scenarios

at the landscape scale and to assess their correspond-

ing impacts on water quality, biodiversity and

economic performance, must examine the output of

a suite of separate models. The process is not simple

and presents a considerable deterrent to making such

comparisons and impedes the development of more

sustainable, multifunctional landscapes. To remedy

this problem, we developed the Landscapes Toolkit,

an integrated modelling framework that assists natural

resource managers, policy-makers, planners and local

communities explore options for sustainable land-

scape development. The Landscapes Toolkit links

spatially-explicit disciplinary models, to enable inte-

grated assessment of the water quality, biodiversity

and economic outcomes of stakeholder-defined land

use and management change scenarios. We use the

Tully–Murray catchment in the Great Barrier Reef

region of Australia as a case study to illustrate the

development and application of the Landscapes

Toolkit. Results show that the Landscapes Toolkit

strikes a satisfactory balance between the inclusion of

component models that sufficiently capture the rich-

ness of some key aspects of social-ecological system

processes and the need for stakeholders to understand

and compare the results of the different models.

The latter is a prerequisite to making more informed

decisions about sustainable landscape development.

The flexibility of being able to add additional

models and to update existing models is a particular

strength of the Landscapes Toolkit design. Hence, the

I. C. Bohnet (&) � P. L. Pert

CSIRO Ecosystem Sciences, PO Box 12139, Earlville BC,

Cairns, QLD 4870, Australia

e-mail: [email protected]

P. C. Roebeling

Department of Environment, CESAM, University of

Aveiro, 3810-193 Aveiro, Portugal

K. J. Williams

CSIRO Ecosystem Sciences, PO Box 284, Canberra,

ACT 2601, Australia

D. Holzworth

CSIRO Ecosystem Sciences, PO Box 102, Toowoomba

4350, QLD, Australia

M. E. van Grieken

CSIRO Ecosystem Sciences, EcoSciences Precinct,

GPO Box 2583, Brisbane, QLD 4001, Australia

F. J. Kroon � D. A. Westcott

CSIRO Ecosystem Sciences, PO Box 780, Atherton,

QLD 4883, Australia

J. Brodie

Catchment to Reef Research Group, James Cook

University, Townsville, QLD 4811, Australia

123

Landscape Ecol (2011) 26:1179–1198

DOI 10.1007/s10980-011-9640-0

Landscapes Toolkit offers a promising modelling

framework for supporting social learning and adaptive

management through participatory scenario develop-

ment and evaluation as well as being a tool to guide

planning and policy discussions at the landscape

scale.

Keywords Scenario analysis � Decision-support-

system � Participatory planning � Integrated

assessment � Great Barrier Reef region � Land use

planning � Landscape ecology � Water quality �Economic � Biodiversity

Introduction

Sustainable landscape development requires deci-

sion-making that acknowledges the complex ecolog-

ical, economic and social interactions that occur in

landscapes (e.g. Potschin and Haines-Young 2006;

Naveh 2007; Opdam 2007; Musacchio 2009; Bohnet

2010; Pearson and Gorman 2010). This is also the

case when decisions are required about the potential

impacts of changes in land use and management (e.g.

Termorshuizen et al. 2007; Bohnet 2008; McAlpine

et al. 2010). In both situations, scientific knowledge

should inform stakeholders in their decision-making

regarding what should be protected, sustained and/or

developed (e.g. Forester 1999; Naveh 2000; Nowotny

et al. 2002; Hirsch Hadorn et al. 2006). Unfortu-

nately, this is not a straightforward process. Estab-

lishing links between sustainable development,

landscape planning and landscape ecology as well

as between theory and practice is therefore critical

and of major importance for landscape ecology to

become more effective (Wu 2006; Opdam 2010). To

achieve this, many scholars have advocated the active

participation of stakeholders from the beginning of

the planning process (e.g. Luz 2000; Buchecker et al.

2003; Bohnet and Smith 2007; Valencia-Sandoval

et al. 2010). Stakeholder participation is particularly

relevant when dealing with problems that fall into the

‘‘post-normal science’’ paradigm, where facts are

uncertain, values are in dispute, stakes are high and

decisions urgent (Funtowicz and Ravetz 1994;

Costanza 2003; Groß and Hoffmann-Riem 2005). In

these cases, which are ideally addressed by using a

transdisciplinary problem-oriented approach, the dia-

logue has to be extended to all those who have a stake

in the issue (Fry et al. 2007). Moreover, stakeholders

should be able to make informed and balanced

decisions about the future based not only on their

values but also on scientific knowledge (e.g. Beunen

and Opdam 2011). Therefore, a reliable scientific

assessment of alternative future landscape develop-

ments (e.g. Hulse et al. 2002; Santelmann et al. 2004)

should complement the participatory planning pro-

cess to inform stakeholder decision-making. By

taking this type of approach to sustainable landscape

development landscape ecology becomes more rele-

vant and can make a valuable contribution to

landscape planning through opportunities provided

for social learning and adaptive management.

A wide range of projective, predictive and explor-

ative approaches have been developed to assess the

impacts of future land use and management change

(e.g. Stoorvogel et al. 2004; Letcher et al. 2006;

Verburg et al. 2006; Castella 2009). As a result, a vast

number of operational tools have been developed to

inform stakeholder decision-making (e.g. Bouman

et al. 2000; Stoorvogel and Antle 2001; Veldkamp

and Lambin 2001; Verburg and Veldkamp 2001;

Huigen 2004; Merritt et al. 2004; Nassauer and Corry

2004; Santelmann et al. 2004; Castella et al. 2005;

Roetter et al. 2005). Whereas projective and predic-

tive approaches forecast likely land use and manage-

ment change scenarios based on past trends, they are

generally non-participatory. In contrast, explorative

approaches are, to varying degrees, participatory in

that they develop land use and management change

scenarios in collaboration with stakeholders. How-

ever, many explorative tools that support the inte-

grated assessment of scenarios at the landscape scale

have not been designed to flexibly assess key system

components such as water quality, biodiversity and

economic performance for ready interpretation and

discussion by stakeholders. Consequently, if stake-

holders wish to assess the impacts of land use change

scenarios on key system components, they must

consult different experts for independently derived

predictions and interpretations for the relevant sce-

narios. This process is not straightforward and

represents a major deterrent, particularly when time

is limited.

The objective of this research was to develop a full

version of the Landscapes Toolkit, an integrated

1180 Landscape Ecol (2011) 26:1179–1198

123

modelling framework that builds on the philosophy of

explorative studies to support the exploration of

options for sustainable landscape development by

stakeholders. The full version of the Landscapes

Toolkit follows the earlier demonstrator version

(Roebeling et al. 2005) which was developed as

proof of concept. The Landscapes Toolkit links

disciplinary models to allow for spatially-explicit

analysis of the impacts of changes in land use and

management on water quality, biodiversity and

economic performance. It has been developed

together with stakeholders to facilitate social learning

and adaptive management through participatory

planning processes which apply the Landscapes

Toolkit interactively. The Landscapes Toolkit is

therefore relevant for landscape ecologists who are

working towards sustainable landscape development

by taking a transdisciplinary problem-oriented

approach to landscape management and aiming to

create multifunctional landscapes. Questions that

might be posed in the Landscapes Toolkit include:

‘What are the likely water quality, biodiversity and

socio-economic impacts of a particular land use and

management change scenario?’ and ‘To what degree

do these impacts differ from the current situation?’

Answers to such questions will assist stakeholders to

discuss trade-offs between different future landscape

developments and to make more informed decisions

about the future.

In the following sections, we describe the Land-

scapes Toolkit, the framework and its component

models. We then illustrate the application of the

Landscapes Toolkit to the Tully–Murray catchment

in the Great Barrier Reef region, Australia. Finally,

we discuss the benefits and contributions of the

Landscapes Toolkit to the discipline of landscape

ecology and offer some recommendations for those

interested in using the Landscapes Toolkit in future

work.

The Landscapes Toolkit

The Landscapes Toolkit integrates a common data-

base and disciplinary component models in a spa-

tially-explicit framework that allows for the

comparative-static assessment of stakeholder-defined

land use and management change scenarios. This

means that the Landscapes Toolkit (in its current

version) is neither dynamic nor includes feedbacks

between the component models. In the following

sections we describe (1) the datasets and how they are

managed in the Landscapes Toolkit, (2) the compo-

nent models and, finally, (3) a description of the

software itself.

Datasets and their management

Identification and harmonization of spatial and non-

spatial datasets is a priority for any land use planning

project and is the first step in customising the

Landscapes Toolkit for a particular study region.

The principal datasets required for use in the

Landscapes Toolkit are land use, catchment and

sub-catchment boundaries, a digital elevation model

(DEM), drainage lines, transport networks and veg-

etation cover. Additional data sets, such as land

suitability and protected areas, are included to

support scenario development and/or the definition

of parameters relevant to each model.

Component models

A limited number of disciplinary component models

currently linked in the Landscapes Toolkit assess

water quality, terrestrial biodiversity and regional

economics using a range of indicators. These three

models were chosen based on the need to improve

water quality, enhance biodiversity and maintain

economic performance in the Great Barrier Reef

region and the idea to assess the triple-bottom-line.

The following sub-sections provide a brief overview

of each of the models, their data inputs and outputs as

well as model uncertainties.

Water quality model

To assess the water quality impacts from land use and

management change scenarios, we use the catchment

model SedNet/ANNEX (sediment river network

model/annual network nutrient export) (McKergow

et al. 2005a, b; Wilkinson et al. 2009) to calculate

end-of-catchment sediment and nutrient loads.

SedNet/ANNEX calculates mean annual supply and

loss, within-catchment deposition and subsequent

downstream delivery of sediments and nutrients,

through the construction of sediment and nutrient

budgets for river networks. A sediment/nutrient

Landscape Ecol (2011) 26:1179–1198 1181

123

budget is an account of the most important sources

and sinks for eroded material and nutrients (Wilkin-

son et al. 2004). Sources of sediment include

hillslope, paddock, gully, drain, and riverbank ero-

sion. Sinks include floodplain, river bed and reservoir

deposition. Sources of nutrients include particulate

nutrients originating from erosion, dissolved nutrient

run-off delivered from diffuse sources such as

fertilised agricultural land and point source nutrient

pollution such as sewage treatment plants. Sinks for

nutrients are associated with sediment deposition,

denitrification, biological uptake and phosphorus

adsorption–desorption. Total sediment/nutrient deliv-

ery at the river mouth is the net result of the above

processes in upstream internal watersheds and con-

necting gullies, streams and rivers.

SedNet/ANNEX requires a digital elevation model

(DEM) and flow regionalisation parameters to derive

a stream network and sub-catchments. To calculate

sediment loads from hillslope erosion, SedNet

requires cover factors for each land use in the

catchment. In addition, a riparian vegetation layer

and gully/drain erosion layer is required to account

for riverbank and gully erosion, respectively. To

calculate dissolved and particulate nutrient loads, the

ANNEX part of the model requires parameters for

Event Mean Concentration run-off data for all

nutrients, land uses and management practices—

these data are based on agricultural production

system simulation models, namely APSIM (Keating

et al. 2003), LUCTOR (Hengsdijk et al. 1998) and

PASTOR (Bouman et al. 1998). Analysis of partic-

ulate nutrients requires, in addition, soil analysis data

as these parameters are modelled from the sediment

loads combined with the nutrient content of the soil.

Uncertainty and validation analysis of the SedNet/

ANNEX model has not been fully completed but a

number of studies have examined components of the

uncertainty while other studies have compared model

outputs to sediment and nutrient loads estimated from

monitoring data and load algorithms. Wilkinson et al.

(2009) observed that sediment yields (predictions)

from SedNet were generally within a factor of two of

measured yields at both moderate catchment scales

(16,000 km2) and small scales (a few hundred km2)

in SE Australia. In the Fitzroy catchment (in eastern

Queensland but south of our Tully–Murray study

area) sediment yield predictions from SedNet

matched a rating curve method (based on measured

TSS concentrations) well with a very good fit at

larger catchment scales (*100,000 km2) and less fit

at smaller scales (*20,000 km2) (Fentie et al. 2005).

Parameter and data uncertainties in SedNet have also

been examined by Henderson and Bui (2005) with

emphasis on how to deal with the uncertainties in

management situations. The problems of predicting

nitrogen and phosphorus loads using SedNet/ANNEX

given limited soil analytical data for N and P content

were highlighted by Sherman et al. (2007) with

predicted PN and PP loads out by factors of two while

predictions for DIN were almost identical to the

measured loads. In their estimates of total loads of

SS, nutrients and pesticides by river to the GBR using

both estimates from SedNet/ANNEX and measured

loads from monitoring data Brodie et al. (2009b) used

a five point scale to rank confidence in the estimates

based on temporal and spatial variability, data

limitations model parametisation issues and load

algorithm choice. In general confidence was highest

for SS and DIN while lowest for PP, PN and

pesticides. We have used a similar scale in our

uncertainty assessments in Table 2.

Terrestrial biodiversity model

To assess the biodiversity impacts from land use and

management change scenarios we developed a

terrestrial biodiversity model (TBM). The TBM uses

a simple form of systematic conservation assessment

(sensu Sarkar et al. 2006; Ferrier and Drielsma 2010)

to estimate the effective area of habitats. Three linked

indicators assess (1) overall landscape condition for

vegetation and for birds, (2) gaps in the representa-

tion of different ecosystems within protected areas

and (3) overall extent of threatened ecosystems.

Indices of habitat condition were related to land use

using hypothetical vegetation state-transition models

(Drielsma and Ferrier 2006) and field survey data

(Westcott, unpublished data), to assess the effective

area of habitat for vegetation and birds, respectively.

Gaps in the representation of different ecosystems

within protected areas were determined from effec-

tive areas (incorporating habitat condition modifiers)

of pre-clearing and remnant vegetation mapping

(Neldner et al. 2005; Kemp et al. 2007) using 15%

of pre-clearing extents as the minimum target for

conservation; an established policy in Australia

(NRMMC 2005); and the species–area relationship

1182 Landscape Ecol (2011) 26:1179–1198

123

to distribute the target so that rate types were

weighted proportionally more than common types

(Faith et al. 2008; Williams et al. 2009). To estimate

the overall extent of threatened ecosystems (Queens-

land Herbarium 2007) we assigned a pre-clearing

vegetation type (Kemp et al. 2007) to mapped areas

of regrowth (Accad et al. 2010) and re-vegetation

plantings (Sydes, unpublished data). These assigned

values combined with their remnant extents and

condition provided the indicator for the overall extent

of threatened ecosystems for each of the scenarios.

Input data required for the TBM to run scenarios

are initial and future condition scores for each land

use (which may be derived using field survey data,

state-transition models and expert opinion), maps of

pre-clearing and remnant vegetation types, protected

area boundaries, and formal lists of threatened veg-

etation types. For the Tully–Murray case study, initial

vegetation condition scores (varying 0-removed to

1-pristine) were inferred by expert opinion from a

classification of land use (Bureau of Rural Sciences

2006) for the region (DNRW 2007) that was grouped

into vegetation state classes (Thackway and Lesslie

2006) and assigned mid-class condition values. Land

uses ranked within vegetation state classes were then

assigned a continuous score within the class range.

Future vegetation condition scores were derived from

initial condition scores using hypothetical state-tran-

sition functions (Drielsma and Ferrier 2006) with

parameters varying by broad classes of vegetation and

in accordance with expert opinion (Catterall, pers.

comm.; Kemp, pers. comm.). Experts separately

scored different land uses as suitable habit for birds

in the range 0 and 1 (Westcott, unpublished data).

Regional economics model

In order to assess the long-term regional economic

implications for the different scenarios, we utilise the

environmental and economic spatial investment pri-

oritisation (EESIP) model (Roebeling et al. 2009;

Van Grieken et al. 2011). EESIP is a spatial

environmental-economic modelling approach that

integrates agricultural production system simulation

models (APSIM, LUCTOR and PASTOR; Bouman

et al. 1998; Hengsdijk et al. 1998; Keating et al.

2003) and a catchment water quality model (SedNet/

ANNEX; McKergow et al. 2005a, b; Wilkinson et al.

2009) into a spatial environmental-economic land-use

model and, consequently, relates management prac-

tice and water pollutant delivery to land use location,

transport infrastructure and distance to markets.

EESIP aims to identify the land use and management

practice arrangements that improve water quality

most cost-effectively, by exploring the trade-offs

between environmental and economic impacts related

to land use and management practice change.

Within the Landscapes Toolkit the optimization

routine is deactivated and EESIP is used to assess the

regional economic implications of the given (stake-

holder defined land use and management change)

scenarios. This means that farm behaviour is not

included in the EESIP model. Regional economic

indicators generated by EESIP include agricultural

inputs (e.g. fertilizer, pesticide and labour) per crop,

agricultural production per crop, gross margin per

crop and regional agricultural income. Regional

estimates are obtained through aggregation over all

land uses and management practices, while regional

agricultural income is given by the total agricultural

production value (based on final products) less

corresponding fixed and variable production, trans-

port and processing costs. Input data required to run

the scenarios consist of soil type maps, input and

output prices, land use and management practice

input–output data (from APSIM, LUCTOR and

PASTOR), fixed and variable transport costs, and

land use location specific distance to market. EESIP

indicator value estimates at the crop and regional

level are consistent with other studies in the area (see

Roebeling et al. 2009), thereby noting that model

uncertainty is relatively low (as EESIP builds on

existing and extensively tested production system

simulation models) while, in particular, economic

data uncertainty can be relatively large (e.g. input and

output price variations and developments).

The Landscapes Toolkit software

Model integration

The Landscapes Toolkit has been written in an

extensible manner, allowing easy integration of

external models. Rather than requiring models to be

modified to run in the Landscapes Toolkit, the

approach taken was to write connector components

to translate and pass the Landscapes Toolkit

Landscape Ecol (2011) 26:1179–1198 1183

123

configurations into input files that the models can

understand (Fig. 1). These connectors implement the

TIME (The Invisible Modelling Environment) inter-

face (Rahman et al. 2004), effectively making TIME

the integration platform (TIME is a software devel-

opment framework for creating, testing and deliver-

ing environmental simulation models). A specific

connector was written for each of the models

described in the previous section. The SedNet/

ANNEX connector takes the ESRI Shapefiles (ESRI

1998) and other configuration data that the user has

specified in the user interface, and translates it into a

form that the model can use. It then calls the external

model and collects the outputs, transforming them

into a form that the user interface can display to the

user. The EESIP and TBM connectors work in a

similar manner, but instead of communicating

directly with the model they pass control to Microsoft

Excel (Fig. 1). This demonstrates the flexibility of the

approach taken, allowing very different (spatially-

explicit) model types to be integrated.

User interface

The user interface of the Landscapes Toolkit focuses

on the creation and evaluation of stakeholder-defined

spatially-explicit land use and management change

scenarios. Scenarios are created through changes to

the current land use map. Land use and management

change/allocation options are predefined in the Land-

scapes Toolkit and include land use and management

changes in selected areas in the map, slope, buffers,

and agricultural land suitability (see Table 1). Each

scenario (as well as the current situation) has config-

urations for the three models—SedNET/ANNEX,

EESIP and the TBM.

Once all models for all scenarios are run, various

outputs from the models can be summarised in a chart

(Table 2). Sufficient flexibility has been incorporated

to allow stakeholders to evaluate multiple scenario

outcomes simultaneously. For example, stakeholders

may wish to compare the impacts on water quality

from all scenarios first, and then compare the

economic and/or biodiversity implications for the

region in a second step. In addition, disciplinary

model results are presented on a common scale and in

a summarised format (following Santelmann et al.

2004; Roebeling et al. 2005), allowing stakeholders

to easily grasp the overall impacts of the scenarios

and to help illustrate that one scenario may not score

well for all indicators assessed. This allows stake-

holders to discuss trade-offs between scenarios and to

define and negotiate future priorities for the area

under study.

Fig. 1 Schematic

representation of the

Landscapes Toolkit

depicting the separation

between the user interface,

TIME connectors and

external models

1184 Landscape Ecol (2011) 26:1179–1198

123

Application of the Landscapes Toolkit

to the Tully–Murray catchment

The Tully–Murray catchment study area

The Tully–Murray catchment (2,910 km2) is located

in Far North Queensland, Australia, is one of 35

basins discharging into the Great Barrier Reef (GBR)

World Heritage Area (Fig. 2) and has a resident

population of 11,230 people (Larson 2007). Under

the Reef Water Quality Protection Plan (Anon.

2003), the Tully–Murray catchment was identified

as a high risk catchment due to its potential for

erosion and pollutant transport to the receiving waters

(Baker 2003). The dominant land uses in the catch-

ment are nature conservation, sugarcane production,

grazing, horticulture, and plantation forestry (Fig. 3).

World Heritage-listed rainforest occupies approxi-

mately 58% of the catchment in the higher elevation

and upper reaches of the rivers and creeks, whereas

cleared cultivated land and remnant patches of

rainforest are found on the alluvial plains, and

wetlands and estuaries near the sandy coast.

We used the 2004 version of 1:100,000 mapping

of land use for the Tully–Murray catchment of

Queensland (Pitt et al. 2007; Witte et al. 2006) and

updated the map based on changes in land use in

2008. This updated land use map represents the

current situation as the reference/starting point for

developing the stakeholder-driven future scenarios in

the Landscapes Toolkit interface (Fig. 3).

Scenario development and definition

We used a participatory research process with the

local community, based on a social-ecological

framework for sustainable landscape planning

Table 1 Summary of the main land use changes and associated GIS-allocation rules for each of the future (2025) scenarios for the

Tully–Murray catchment

2025 Scenario Main land use change GIS-allocation rules (replicable

criterion) on which land use change

is based

Scenario 1: Improving water

quality in the sugarcane

landscape

Slopes [20% and used for agricultural purposes—

regrowth

All slopes [ 20%

Establishment of continuous riparian buffers Single riparian buffers where native

vegetation is missing

Land unsuitable for sugarcane and banana production—

horticulture, grazing, regrowth

Land suitability mapping for

agricultural crops in the local area

Conversion from sugarcane to grazing in Kennedy valley Change from one land use to another

in spatially explicit location

Scenario II: Tropical fruit and

food bowl

Slopes [20% and used for agricultural purposes—

regrowth

All slopes [20%

Establishment of continuous riparian buffers Double riparian buffers where native



small crops, horticulture, grazing, regrowth



Land under constrained sugarcane and banana production

is given preference by small crops and horticulture

Scenario III: Cassowary coast Slopes [20% and used for agricultural purposes—

regrowth

All slopes [20%

Establishment of continuous riparian buffers Triple riparian buffers where native



grazing, regrowth



Land under constrained sugarcane and banana production

is given preference by grazing

Landscape Ecol (2011) 26:1179–1198 1185

123

(Bohnet and Smith 2007), to develop future visions

for the Tully–Murray catchment in 2025 (Bohnet

2010).

The participatory planning process engaged a wide

range of local stakeholders (including farmers, land

based industries, conservation groups, Traditional

Table 2 Output summary of indicators of the three disciplinary component models including description of indicators and their

validity/uncertainty

Model Indicator Description of indicator Validity/

uncertainty of

indicator

Water quality Suspended sediment (thousands of

tonnes/year)

Annual load at the end of the river system

i.e. at entry to marine waters

5

Bedload sediment (thousands of

tonnes/year)



3

Total phosphorus (t/year) Annual load at the end of the river system


2

Filterable reactive phosphorus

(t/year)



3

Dissolved organic phosphorus

(t/year)



3

Particulate phosphorus (t/year) Annual load at the end of the river system


1

Total nitrogen (t/year) Annual load at the end of the river system


3

Dissolved Inorganic Nitrogen

(t/year)



5

Dissolved Organic Nitrogen

(t/year)



3

Particulate nitrogen (t/year) Annual load at the end of the river system


1

Terrestrial

biodiversity

Overall Terrestrial Biodiversity

(ha/%)

Effective area intact habitat potentially utilised by

indigenous terrestrial biodiversity

3

Terrestrial Birds (ha/%) Area suitable habitat potentially utilised by native birds 3

Threatened Ecosystems

(ha/%)

Effective area extant ecosystems defined as threatened

(\30% remains intact)

4

Under-represented Ecosystems

(ha/%)

Effective area extant ecosystems available to meet

conservation reserve target of 15% for each type

4

Regional

economics

Total regional income

(million AU$/year)

Aggregate annual agricultural gross margin (i.e. crop

production value minus crop production costs)

4

Total regional labour

(million hours/year)

Aggregate annual agricultural labour requirements

(i.e. labour requirements in crop production)

4

Crop production

Sugarcane (t/year) Annual cane production 5

Grazing (t/year) Annual beef production 4

Forestry (m3/year) Annual timber production 3

Bananas (t/year) Annual banana production 4

Horticulture (t/year) Annual horticulture production 2

Small crops (t/year) Annual small crops production 2

Validity/uncertainty scale: 1 low confidence/high uncertainty and 5 high confidence/low uncertainty. For the water quality model

loads are based on Brodie et al. (2009b) and interpreted for the Tully–Murray system. For the regional economics model the

aggregate annual agricultural gross margin is based on the relative importance of the different crops in the catchment and represents

the weighted sum of crop-specific validity values

1186 Landscape Ecol (2011) 26:1179–1198

123

Owners, Indigenous people, schools and government

representatives) and comprised three linked stages.

Stage 1 consisted of a series of qualitative semi-

structured interviews with a wide range of commu-

nity members. Stage 2 involved three community

workshops (Bohnet and Kinjun 2009), five school

projects (Bohnet et al. 2010) and several local

presentations and discussions about the future for

the Tully–Murray catchment in which a diversity of

community and science experts participated (Bohnet

2010). Stage 3 involved further local presentations

and discussions about the spatially-explicit future

scenarios developed for the Tully–Murray catchment

and the trade-offs between the future scenarios and

the current situation (Bohnet 2010). In total, over 150

individuals participated in the research. Several

stakeholders participated in all three stages of

the research, whereas some participated only in

Fig. 2 Location of the

Tully–Murray catchment in

Far North Queensland in

relation to other basins

discharging their waters

into the Great Barrier Reef

(Source Great Barrier Reef

Marine Park Authority)

Landscape Ecol (2011) 26:1179–1198 1187

123

Stage 1 and/or Stage 2 and 3 depending on their

interests and needs.

Ideally, the Landscapes Toolkit is used in Stage 2

of the participatory research to interactively develop

future scenarios with stakeholders, and in Stage 3 to

collaboratively evaluate the impacts of the scenarios

on water quality, economics and biodiversity. In this

way, the Landscapes Toolkit can support stakeholder

discussions about trade-offs between future scenarios.

Through this staged process and use of the Landscapes

Toolkit, we aim to support social and sustainability

learning amongst stakeholders (e.g. Pahl-Wostl 2006;

Tabara and Pahl-Wostl 2007) and to enable more

informed decision-making about the future. However,

since the functionalities of the Landscapes Toolkit

were developed and refined based on the findings of

the participatory research carried out in this project a

fully functioning version of the Landscapes Toolkit

could not be used during Stage 2 and 3. This was

appreciated by the participants who could see that the

Landscapes Toolkit was truly developed in parallel to

and based on the participatory research and not by

scientists who present a completed model based only

on their ideas.

The three spatially-explicit future scenarios: (1)

Improving water quality (in the sugarcane landscape),

(2) Tropical food and fruit bowl, and (3) Cassowary

coast, were derived from the participatory research

(Bohnet 2010; Table 1). We developed these future

scenarios in the Landscapes Toolkit, using the

updated land use map (current situation) of the

catchment as the template (Fig. 3). Hence, land use

Fig. 3 Current land uses

in the Tully–Murray

catchment including

protected areas

1188 Landscape Ecol (2011) 26:1179–1198

123

change routines (GIS-allocation rules), such as ripar-

ian buffers, land use changes based on land suitability

or slope (see Table 1 column 3 for more detail), had

to be incorporated in the Landscapes Toolkit. These

rules reflect a set of common and proposed land use

and management changes by community members,

thus allowing for the interactive development of

future scenarios. The scenario maps are therefore not

simple digital maps of land use and management

change in the Tully–Murray basin; they represent the

landscape outcomes of very different human priori-

ties for agricultural lands and assumptions about the

future. They are not predictions of the future, but

examples of what could happen in the future based on

stakeholder priorities, preferences or assumptions

and, hence, may be very different from present and

future realities (Nassauer and Corry 2004).

Scenario 1—Improving water quality

in the sugarcane landscape

The main goal in Scenario 1 is to improve water

quality while maintaining and enhancing the charac-

ter of the agricultural landscape. Based on this main

goal, land use and management changes in this

scenario include: introduction of planted riparian

buffer zones and conversion from sugarcane land to

grazing in the Kennedy valley (Table 1). Increased

riparian vegetation is believed to improve water

quality through a number of mechanisms, including

trapping suspended sediments in overland flow,

promoting denitrification and biological uptake of

dissolved nutrients, and reducing stream bank erosion

through physical stabilisation (McKergow et al.

2004a, b). Currently nutrient concentrations in many

streams in the Tully–Murray catchment downstream

of sugarcane and banana lands regularly exceed

greatly ANZECC and Queensland water quality

guidelines for nitrate and phosphate (Bainbridge

et al. 2009). In coastal waters off the rivers in this

region Great Barrier Reef Marine Park Authority

water quality guidelines for chlorophyll a (a nutrient

proxy indicator) and suspended solids and clarity

(Secchi depth) are generally exceeded (Brodie et al.

2007; De’ath and Fabricius 2008, 2010) linked to

river discharge of sediments and nutrients (Brodie

et al. 2009a). In addition, planted and actively

managed riparian vegetation, in contrast to regrowth

(left to grow back to forest without active

management), contributes to the goal of improving

biodiversity in a shorter timeframe (Catterall et al.

2007). Further land use changes include conversions

from marginal land under sugarcane and banana

production to more suitable agricultural land uses

based on a range of environmental factors (Smith

et al. 2004). To prevent soil erosion, agricultural land

on slopes steeper than 20% is replaced by regrowth

(Table 1). Land management changes include the

introduction of new best management practices

(BMPs) in sugarcane (‘Six easy steps’; Schroeder

et al. 2007) and banana (Armour and Daniels 2001;

Armour et al. 2007) production, aimed at better

targeting fertiliser application to actual plant require-

ments—thus leading to improved water quality

through a reduction in dissolved inorganic nitrogen

(DIN) loss. For acceptable water quality i.e. meeting

guidelines for chlorophyll a in Tully–Murray coastal

waters a 80% reduction in DIN discharge from the

rivers is required (Wooldridge et al. 2006; Brodie

et al. 2009a). Reef Plan requires a 50% reduction in N

and P loads across the GBR by 2013 (Brodie et al.

2011) but targets for individual rivers such as the

Tully or Murray are yet to be set. Many of the

management practices in sugar and banana cultiva-

tion mentioned above will lead to substantial reduc-

tions in, for example, DIN loads and connection

between improved fertiliser management and DIN

loads and chlorophyll a guidelines offshore have been

explored by Brodie et al. (2009a).

Scenario 2—Tropical fruit and food bowl

The main assumption in Scenario 2 is that agriculture

in Australia is moving north due to drought and

limited water supply in southern Australia, and that

by 2025 the Tully–Murray catchment will be the

tropical fruit and food bowl of Australia. Changes in

this scenario include conversions from land under

constrained sugarcane and banana production to a

wide range of small crops, such as cucurbits,

brassicas, potatoes, and horticulture (Smith et al.

2004). To prevent run-off from agricultural land,

broad riparian buffer zones are planted. As in

Scenario 1, agricultural land on slopes steeper than

20% is replaced by regrowth (Table 1). New BMPs

for sugarcane (‘nitrogen replacement approach’;

Thorburn 2004; Thorburn et al. 2005) and bananas

Landscape Ecol (2011) 26:1179–1198 1189

123

(Armour and Daniels 2001; Armour et al. 2007) are

applied. The nitrogen replacement approach manages

nitrogen application by estimating the amount used or

lost from the previous crop and calculating a

replacement amount for the new crop. This leads to

a considerable improvement in water quality through

a sizeable reduction in N application and DIN loss.

Scenario 3—Cassowary coast

The main goal of the ‘Cassowary coast’ scenario is to

create more wildlife habitats in the agricultural

landscape, particularly for endangered species such

as the Southern Cassowary (Casuarius casuarius

johnsonii). Habitat loss, fragmentation and the impacts

of human occupation in the study area have led to a

large decrease in cassowary numbers (Latch 2007),

and the changes in land use and management practices

in this scenario are aimed at reducing this threat.

Extensive riparian buffer zones are planted not only to

prevent run-off from agricultural land, but also to

establish a habitat network and corridors throughout

the agricultural landscape. Marginal land under sug-

arcane and banana production is converted to grazing

with low stocking rates, and to regrowth where grazing

is not suitable (Smith et al. 2004). As in Scenario 1 and

2, agricultural land on slopes steeper than 20% is

replaced by regrowth—adding to the stock of semi-

natural habitats by 2025 (Aide et al. 2000; Guariguata

and Ostertag 2001; Kanowski et al. 2003; Table 1).

Scenario results from component models

Water quality

In the scenarios considered, changes in land use and

management practice led to minor reductions in

sediment load export (Table 3). This is not surpris-

ing; most agricultural activities in the catchment take

place on relatively flat land in the Tully–Murray

floodplain (see Bohnet et al. 2008 for another Far

North Queensland example). Small changes between

the scenarios can be detected in total phosphorus

loads exported to the coast (Table 3). As expected,

the greatest phosphorus reductions are in Scenario 3

due to the considerable reductions in the sugarcane

and banana production area. Phosphorus loads

increase in Scenario 2 when compared with the

current situation, due to the increased area under

small crops. Considerable changes between scenarios

are found for nitrogen loads exported to the coast

(Table 3), thereby noting that the reductions in total

nitrogen loads are due entirely to the reductions in

DIN loads. Some of the load reduction predictions for

DIN are not robust (high uncertainty) due to our lack

of knowledge about management practices for small

crops and riparian buffers trapping DIN. Therefore,

the model results for Scenarios 2 and 3 need to be

interpreted with caution: the effects of riparian

buffers may be over-represented and the limited

knowledge about small crops in Scenario 2 weakens

Table 3 Results for the current situation and three future scenarios for the Tully–Murray catchment applying the water quality

model SedNet/ANNEX (Sediment River Network model/Annual Network Nutrient Export)

Exports from network (in t/year) Current

situation

Scenario 1

(Improving water

quality)

Scenario 2

(Tropical food

and fruit bowl)

Scenario 3

(Cassowary

coast)

Suspended sediment (SS) thousands of tonnes 27.9 26.8 26.9 26.3

Bedload sediment (BS) thousands of tonnes 8.9 8.9 8.9 8.9

Total phosphorus (TP) 92.9 88 95.9 81.8

Filterable reactive phosphorus (FRP) 48.8 45.7 53.3 42.7

Dissolved organic phosphorus (DOP) 29.3 28 28.2 25

Particulate phosphorus (PP) 14.8 14.3 14.4 14.1

Total nitrogen (TN) 1685 1312 1066 792.7

Dissolved Inorganic Nitrogen (DIN) 1118 770.5 530.8 299.7

Dissolved Organic Nitrogen (DON) 498.3 477.6 471.3 432.3

Particulate nitrogen (PN) 68.9 63.4 64.1 60.7

1190 Landscape Ecol (2011) 26:1179–1198

123

confidence in the outcomes of that scenario. Although

the reduction in DIN loads contribute considerably to

water quality improvement towards sustainable loads,

none of the scenarios achieve the required [80%

reduction in nitrate concentrations to end-of-river

load (Wooldridge et al. 2006; Brodie et al. 2009a, b).

Terrestrial biodiversity

The effective area of habitat available for terrestrial

biodiversity including birds, threatened ecosystems

and under-represented ecosystems varied slightly

between scenarios (Table 4). Land use and manage-

ment change scenarios mainly affected biodiversity

through areas of regrowth and environmental plant-

ings (i.e. restoration of riparian areas), and the extent

to which condition is assumed to improve over

20 years, potentially providing habitat for an increas-

ing variety of indigenous biodiversity. A marginal

improvement in the condition and extent of threa-

tened ecosystems occurred from Scenario 1 through

to Scenario 2 and 3 where regrowth areas or riparian

plantings corresponded with the former location of

vegetation types that have been substantially cleared

(\30% extant). The potential to meet ecosystem

representation targets of 15% of effective habitat in

formal reserves as indicated by the extent of under-

represented ecosystems, improved from Scenario 1

through to Scenario 2 and 3, but with a decreasing

marginal gain. The extent of protected areas remained

constant in each scenario. The area of under-repre-

sented ecosystems increased following the restoration

of some types that were substantially cleared or

degraded, where previously the minimum represen-

tation target could not be achieved. This trend

suggests that the location of revegetation sites in

Scenario 2 and 3 could be further aligned with

priority sites for conservation.

Uncertainties in this analysis relate to the use of

expert opinion to infer the relationship between

biodiversity response and land use in order to assign

condition scores and estimate effective habitat areas.

These relationships could be improved through an

explicit elicitation framework and Bayesian estima-

tion incorporating field calibration data (e.g. Low-

Choy et al. 2009). Furthermore, in the absence of

specific information about composition and overlap

of species among vegetation classes, the species–area

relationship is used to apportion the conservation

reserve target so that rarer types have higher values

relative to common types (Faith et al. 2008; Williams

et al. 2009).

Regional economics

The EESIP results indicate that changes in land use

and management in all scenarios lead to major

changes in regional agricultural income from the

included industries (sugarcane, grazing, forestry,

bananas, horticulture and small crops) (Table 5).

These changes are mainly due to changes in land use

and, to a lesser extent, to changes in management

practices. In other words, differences in income are

larger between different production systems than

between different crop specific management prac-

tices. Scenario 1 comes at a small cost to the region

(-9%), while the biggest increase in regional income

(?32%) is obtained in Scenario 2. It must be noted,

however, that the market for fruit crops is expected to

be limited, which may lead to reduced prices and,

Table 4 Results for the current situation and three future scenarios for the Tully–Murray catchment applying the terrestrial

biodiversity model (TBM)

Effective habitat areas (in ha) Current

situation

Scenario 1 (Improving

water quality)

Scenario 2 (Tropical food

and fruit bowl)

Scenario 3

(Cassowary coast)

Overall terrestrial biodiversity 267,807 (78%) 271,212 (79%) 273,774 (80%) 276.892 (81%)

Terrestrial birds 291,642 (85%) 294,866 (86%) 295,796 (86%) 300,178 (88%)

threatened ecosystems 79,736 (47%) 83,028 (49%) 85,524 (51%) 88,471 (52%)

Under-represented ecosystems 8,349 (29%) 11,138 (34%) 13,337 (38%) 15,848 (42%)

Percentages in parentheses are based on the total area of applicable habitat relevant to each indicator and scenario. Effective habitat

areas for overall terrestrial biodiversity and birds reflect the condition of the entire landscape, relative to its pristine condition. The

areas given for threatened ecosystems are relative to the original extent of these ecosystems and under-represented ecosystems are

relative to their remnant extents

Landscape Ecol (2011) 26:1179–1198 1191

123

hence, returns. The largest decrease in regional

income (-32%) can be observed in Scenario 3. This

is to be expected as intensive production systems are

replaced by extensive and less profitable production

systems. Regional agricultural employment opportu-

nities are largest for Scenario 2 (?72%), while

Scenarios 1 and 3 lead to considerable reductions

(-18 and -43% respectively) in regional agricultural

labour requirements (Table 5). Finally, sugar mill

viability is not likely to be threatened by any of the

assessed scenarios, with mill throughput maintained

well above the minimum required 1.5 million t/year.

Integrated assessment of scenarios

Summarising the results from all scenarios and across

all component models shows that Scenario 3 not only

improves biodiversity more than any other scenario,

but also contributes to the highest reductions in

phosphorus and nitrogen delivered to the coast from

the catchment (Fig. 4). However, the agricultural

land uses in this scenario are not as profitable as in

the other scenarios, leading to a large reduction in

regional income from agricultural production. Sce-

nario 2, in contrast, delivers the highest economic

return for the basin’s agriculture, however, water

quality suffers due to the increased area under small

crops, this despite the introduction of broad riparian

buffer zones to reduce the water quality impact of

agricultural production (Fig. 4). Considering water

quality, regional economic and biodiversity out-

comes, Scenario 1 seems to strike a balance between

improving water quality at minimal cost for agricul-

ture in the region while also improving biodiversity.

However, it has to be noted that total DIN load per

year to end-of-river would be reduced only by 31%,

which is considerably lower than in Scenario 2 and 3

(Fig. 4).

The integrated assessment shows that none of the

stakeholder-defined land use and management change

scenarios for the Tully–Murray catchment improves

water quality, biodiversity as well as regional agri-

cultural income (Fig. 4). However, the results provide

a scientific basis for stakeholder discussion about

trade-offs between scenarios. The disciplinary model

results, the integrated assessment results and the

discussion about trade-offs, allow stakeholders to

make more informed decisions about the future based

on best available scientific knowledge, future prior-

ities and stakeholder values.

Discussion

In developing the Landscapes Toolkit, our aim was to

link and operationalise scenario development and

evaluation with stakeholder participation using dis-

ciplinary simulation models for quantitative assess-

ment and comparative analysis. In achieving this aim,

the Landscapes Toolkit has expanded the use of

decision-support tools that simulate land use and

management change scenarios (Nicolson et al. 2002;

Hacking and Guthrie 2008). Also, by providing a tool

that allows links between stakeholder values,

Table 5 Results for the current situation and three future scenarios for the Tully–Murray catchment applying the Environmental and

Economic Spatial Investment Prioritisation (EESIP) model

Current

situation

Scenario 1 (Improving

water quality)

Scenario 2 (Tropical food

and fruit bowl)

Scenario 3

(Cassowary coast)

Total regional income

(million AU$/year)

72.79 66.31 96.00 49.81

Total regional labour

(million hours/year)

1.09 0.89 1.88 0.62

Crop production

Sugarcane (t/year) 3,542,013 3,118,211 2,009,883 1,877,289

Grazing (t/year) 12,124 11,292 7,251 13,018

Forestry (m3/year) 119,662 113,507 106,864 100,412

Bananas (t/year) 167,875 121,937 91,442 79,302

Horticulture (t/year) 11,768 15,549 22,616 9,540

Small crops (t/year) 1,994 1,803 278,941 1,307

1192 Landscape Ecol (2011) 26:1179–1198

123

landscape planning and landscape ecology to be

established, the Landscape Toolkit contributes to

making landscape ecology a more relevant discipline.

In line with Nassauer and Opdam’s (2008) thinking,

the Landscapes Toolkits extends the pattern: process

relationship, which for a long time dominated the

landscape ecological literature, with design that is

based on human values.

Our experience with the development and appli-

cation of the Landscapes Toolkit to date highlights

some useful lessons. First, stakeholders should play a

central role in all regional and local spatial model

applications that employ the Landscapes Toolkit and

be introduced to the concept of the Landscapes

Toolkit at an early stage of the project. Second, to

manage stakeholder expectations it is important to

clarify the types of questions that can be addressed by

using the Landscapes Toolkit. Third, having experts

from different science disciplines present when future

landscape scenarios are developed, evaluated and

discussed is valuable, because experts can verify

model results and provide information about model

uncertainties (which are known beforehand) to

stakeholders. Fourth, involving stakeholders with

different interests can enrich the scenario develop-

ment process and allow for mutual learning and co-

production of knowledge. Fifth, since the disciplinary

component models in the Landscapes Toolkit require

location-specific data inputs that are not, necessarily,

universally available, minimum data set requirements

across component models need to be identified early

in the project to determine if and what (type of)

additional data need to be collected. Sixth, rigorous

data management and coordination is necessary to

ensure that spatial and temporal scales across com-

ponent models are aggregated. Seventh, as stake-

holder selection and engagement processes are likely

to influence the scenario outcomes (Renn et al. 1995),

Fig. 4 Screenshot from the

Landscapes Toolkit

software showing a

selection of water quality,

economic and biodiversity

model results for the current

situation and three future

scenarios plotted on a graph

for integrated analysis.

From the results list the user

can select multiple outputs

for comparison

Landscape Ecol (2011) 26:1179–1198 1193

123

attention should be given to research that informs the

selection of stakeholders as well as the design of

appropriate participation processes that employ the

Landscapes Toolkit.

A noteworthy feature of the Landscapes Toolkit is

that it can facilitate a participatory research process

that places stakeholders in a central rather than an

adjunct role. Because stakeholders develop spatially-

explicit scenarios for the future interactively (e.g. in

facilitated workshops), the Landscapes Toolkit offers

a more transparent process of scenario development

than is the case if relying on researcher interpretation

of stakeholder information. This stakeholder driven

approach, to some degree, by-passes questions related

to the accuracy of representation of human agents in

future scenarios (Tansey et al. 2002). Although

stakeholder choices about the future are principally

deterministic and constrained by land use allocation

rules and a set of management options available in

the Landscapes Toolkit, the Landscapes Toolkit

provides a distinct advantage in allowing exploration

of scenarios considered plausible by stakeholders.

The fact that scenarios are defined by stakeholders

also allows the process to highlight what underlying

conflicts of interest exist between stakeholder groups,

what trade-offs are acceptable and what the knowl-

edge base is of the issues the region faces (Caille

et al. 2007).

While the primary purpose for developing the

Landscapes Toolkit was to create an integrated

modelling framework that allows spatially-explicit

analysis of the impacts of land use and management

changes on environmental and socio-economic val-

ues, the project also offers a valuable example of

multi-disciplinary and participatory research.

Because the Landscapes Toolkit has been designed

to support social learning and adaptive management

through a participatory research approach that

enables the development and evaluation of stake-

holder-defined scenarios, it crosses a number of

science disciplines and approaches. Consequently,

throughout its development, the project team aimed

to strike a balance between including component

models that sufficiently capture the richness of some

key aspects of social-ecological system processes, as

well as meeting the need for simplicity and transpar-

ency to enable stakeholders to understand and

compare the results from the disciplinary models.

The ability to incorporate additional models and to

update existing models in the Landscapes Toolkit is a

particular strength of its design. For example, in the

Great Barrier Reef region where the Landscapes

Toolkit was developed, an interest has emerged in

understanding the ecosystem services provided from

different land uses. Linking an ecosystem services

model to the Landscapes Toolkit would be a

straightforward task enabling determination of the

ecosystem services provided under the various sce-

narios. Similarly, other models can be added to

provide information to answer specific (research and/

or stakeholder) questions and aid discussion. In other

words, the Landscapes Toolkit offers a useful frame-

work for linking planning for specific goals, e.g.

water quality improvement (the prime objective of

the Reef Water Quality Protection Plan), with other

management goals, e.g. biodiversity conservation

planning. Thereby, the Landscapes Toolkit makes a

useful contribution to landscape ecology.

From the component modellers’ perspectives, the

results from the current case study demonstrate the

need for further analyses of some scenario elements

(such as riparian buffers and small crops) that

appear to drive major changes in the water quality

and economic model. Thus, the Landscapes Toolkit

has not only the potential to contribute to stake-

holder discussions about future landscape develop-

ments but also to provide research direction for

regional/landscape scale field experiments for model

validation.

Future research needs to address a number of

issues present in the current version of the Land-

scapes Toolkit. First, while spatial scales of compo-

nent models are matched through rigorous

aggregation to the spatial scale of the ‘coarsest’

component model, temporal scales vary between

component models though essentially correspond

with the ‘largest’ appropriate temporal scale (Letcher

et al. 2006). Second, to account for processes that are

endogenous to the integrated system, feedbacks

between component models need to be included in

the Landscapes Toolkit (Roebeling et al. 2005).

Finally, the uncertainty of component model results

needs to be assessed and presented (e.g. based on

user-defined confidence intervals), as to give stake-

holders a realistic feel for the robustness of future

scenario results (Letcher et al. 2006). This issue is

1194 Landscape Ecol (2011) 26:1179–1198

123

particularly important when feedbacks between com-

ponent models are considered.

Conclusion

The Landscapes Toolkit offers an integrated model-

ling framework to assess water quality, terrestrial

biodiversity and regional economic outcomes of

stakeholder-developed land use and management

change scenarios. Its two great strengths are that it

is more accessible and easier to employ than the

alternative of independently consulting a battery of

independent explorative models, and that participa-

tion by the full suite of stakeholders is an integral and

central part of its design.

Acknowledgments The authors acknowledge the

contributions by Scott Wilkinson, Dan Metcalfe, Andrew

Ford, Damon Sydes, Michael Drielsma, Daniel Faith, Jeanette

Kemp, Carla Catterall, Peter Thorburn and Tony Webster to

the component models and data layers linked in the Landscapes

Toolkit. Caroline Bruce provided data management and Adam

Fakes programming support. Thanks to the local stakeholders

who participated in the project for their time, enthusiasm and

valuable feedback. Thanks to Rosemary Hill and Emma Jakku

for their interest in the use of decision-support tools and

valuable scientific discussions. Mark Smith instigated and

supported the early development of the Landscapes Toolkit;

thanks for his continued interest and comments on an earlier

version of this manuscript. Andre Zerger and three anonymous

reviewers also provided valuable comments on earlier versions

of this manuscript. CSIRO’s Water for a Healthy Country

Flagship funded the development of the Landscapes Toolkit

while the Marine and Tropical Science Research Facility

contributed towards the development of two component

models that are part of the Landscapes Toolkit.

References

Accad A, Lucas R, Pollock AB, Armston J, Bowen M, Mc-

Alpine C, Dwyer J (2010) Mapping the extent and growth

stage of woody regrowth following clearing through

integration of ALOS PALSAR and Landsat-derived

Foliage Projected Cover (FPC). In: 15 ARSPC Austral-

asian remote sensing and photogrammetry conference,

13–17 September 2010. Surveying and Spatial Sciences

Institute, Alice Springs, Australia

Aide TM, Zimmerman JK, Pascarella JB, Rivera L, Marcano-

Vega H (2000) Forest regeneration in a chronosequence of

tropical abandoned pastures: implications for restoration

ecology. Restor Ecol 8:328–338

Anon. (2003) The State of Queensland and Commonwealth of

Australia. Reef Water Quality Protection Plan for catch-

ments adjacent to the Great Barrier Reef World Heritage

Area. Queensland Department of Premier and Cabinet,

Brisbane

Armour JD, Daniels JW (2001) Banana nutrition in north

Queensland. Final report to Horticulture Australia Ltd,

project FR95013. Department of Natural Resources and

Mines/Primary Industries

Armour JD, Daniels JW, Lindsay SJ (2007) Improved phos-

phorus fertiliser management of bananas. A report to FNQ

NRM Ltd. Department of Natural Resources and Water/

Department of Primary Industries and Fisheries

Bainbridge ZT, Brodie JE, Faithful JW, Sydes DA, Lewis SE

(2009) Identifying the land-based sources of suspended

sediments, nutrients and pesticides discharged to the Great

Barrier Reef from the Tully–Murray Basin, Queensland,

Australia. Mar Freshw Res 60:1081–1090

Baker J (2003) A report on the study of land-sourced pollutants

and their impacts on water quality in and adjacent to the

Great Barrier Reef. Science Panel, Brisbane

Beunen R, Opdam P (2011) When landscape planning becomes

landscapes governance, what happens to the science?

Landsc Urban Plan 100:324–326

Bohnet I (2008) Assessing retrospective and prospective

landscape change through the development of social

profiles of landholders: a tool for improving land use

planning and policy formulation. Landsc Urban Plan

88:1–11

Bohnet IC (2010) Integrating social and ecological knowledge

for planning sustainable landscapes: a study from the

Great Barrier Reef region, Australia. Landscape Ecol

25:1201–1218

Bohnet IC, Kinjun C (2009) Community uses and values of

water informing water quality improvement planning: a

study from the Great Barrier Reef region, Australia. Mar

Freshw Res 60:1176–1182

Bohnet I, Smith DM (2007) Planning future landscapes in the

Wet Tropics of Australia: a social-ecological framework.

Landsc Urban Plan 80:137–152

Bohnet I, Brodie J, Bartley R (2008) Assessing water quality

impacts of community defined land use change scenarios

for the Douglas Shire, Far North Queensland. In: Pettit C,

Cartwright W, Bishop I, Lowell K, Pullar D, Duncan D

(eds) Landscape analysis and visualisation. Springer,

Berlin, pp 383–406

Bohnet IC, Gooch M, Hickey R (2010) Young people envision

the future of their local area: an explorative study from the

Wet Tropics, Australia. Appl Environ Commun Educ

9:185–197

Bouman BAM, Nieuwenhuyse A, Hengsdijk H (1998) PAS-

TOR: a technical coefficient generator for pasture and

livestock systems in the humid tropics, version 2.0. AB-

DLO/C.T. de Wit Graduate school for Production Ecol-

ogy, Quantitative Approaches in Systems Analysis No.

18, Wageningen

Bouman BAM, Jansen HGP, Schipper RA, Hengsdijk H,

Nieuwenhuyse A (2000) Tools for land use analysis on

different scales: with case studies for Costa Rica. Kluwer

Academic Publishers, The Netherlands

Brodie JE, De’ath G, Devlin MJ, Furnas M, Wright M (2007)

Spatial and temporal patterns of near-surface chlorophyll

a in the Great Barrier Reef lagoon. Mar Freshw Res

58:342–353

Landscape Ecol (2011) 26:1179–1198 1195

123

Brodie J, Lewis S, Bainbridge Z, Mitchell A, Waterhouse J,

Kroon F (2009a) Target setting for pollutant discharge

management of rivers in the Great Barrier Reef catchment

area. Mar Freshw Res 60:1141–1149

Brodie JE, Waterhouse J, Lewis SE, Bainbridge ZT Johnson J

(2009b) Current loads of priority pollutants discharged

from Great Barrier Reef Catchments to the Great Barrier

Reef. Report 09/02, Australian Centre for Tropical

Freshwater Research, James Cook University, Townsville

Brodie JE, Devlin MJ, Haynes D, Waterhouse J (2011)

Assessment of the eutrophication status of the Great

Barrier Reef lagoon (Australia). Biogeochemistry. doi:

10.1007/s10533-010-9542-2

Buchecker M, Hunziker M, Kienast F (2003) Participatory

landscape development: overcoming social barriers to

public involvement. Landsc Urban Plan 64:29–46

Bureau of Rural Sciences (2006) Guidelines for land use mapping

in Australia: principles, procedures and definitions: a tech-

nical handbook supporting the Australian Collaborative

Land Use Mapping Program. Department of Agriculture,

Fisheries and Forestry, Australian Government, Canberra

Caille F, Riera JL, Rodriguez-Labajos B, Middelkoop H, Ro-

sell-Mele A (2007) Participatory scenario development

for integrated assessment of nutrient flows in a Catalan

river catchment. Hydrol Earth Syst Sci 11:1843–1855

Castella JC (2009) Assessing the role of learning devices and

geovisualisation tools for collective action in natural

resource management: experiences from Vietnam.

J Environ Manag 90:1313–1319

Castella JC, Trung TN, Boissau S (2005) Participatory simu-

lation of land-use changes in the northern mountains of

Vietnam: the combined use of an agent-based model, a

role-playing game, and a geographic information system.

Ecol Soc 10(1):27 [online] URL: http://www.ecologyand

society.org/vol10/iss1/art27/

Catterall CP, Kanowski J, Wardell-Johnson GW (2007) Bio-

diversity and new forests: interacting processes, prospects

and pitfalls of rainforest restoration. In: Stork N, Turton T

(eds) Living in a dynamic tropical forest landscape.

Cambridge University Press, Cambridge, pp 510–525

Costanza R (2003) A vision of the future of science: reinte-

grating the study of humans and the rest of nature. Futures

35:651–671

De’ath G, Fabricius KE (2008) Water quality of the Great Barrier

Reef: distributions, effects on reef biota and trigger values

for the protection of ecosystem health, research publication

no. 89. Great Barrier Marine Park Authority, Townsville

De’ath G, Fabricius KE (2010) Water quality as a regional

driver of coral biodiversity and macroalgae on the Great

Barrier Reef. Ecol Appl 20:840–850

DNRW (2007) Land Use (2004) in the Murray River catch-

ment, Queensland (ANZLIC Page 0 Metadata). Depart-

ment of Natural Resources and Water, Queensland

Government, Brisbane

Drielsma M, Ferrier S (2006) Landscape scenario modelling of

vegetation condition. Ecological Management and Res-

toration 7:45–52

ESRI (1998) ESRI Shapefile Technical Description. An ESRI

White Paper—July 1998. Available from http://downloads2.

esri.com/support/whitepapers/mo_/shapefile.pdf. Accessed

23 March 2010

Faith DP, Ferrier S, Williams KJ (2008) Getting biodiversity

intactness indices right: ensuring that ‘‘biodiversity’’

reflects ‘‘diversity’’. Glob Change Biol 14:207–217

Fentie B, Joo M, Yu B, Hunter H, Marsh N, Carroll C, Dougall

C (2005) Comparison of mean annual suspended loads

estimated by the SedNet model and rating curves in the

Fitzroy Catchment, Australia. In: Zerger A, Argent R

(eds) MODSIM 2005 International Congress on Model-

ling and Simulation. December, 2005, pp 1133–1139

Ferrier S, Drielsma M (2010) Synthesis of pattern and process

in biodiversity conservation assessment: as flexible

whole-landscape modelling framework. Divers Distrib

16:386–402

Forester J (1999) The deliberative practitioner. Encouraging

participatory planning processes. MIT Press, Cambridge

Fry G, Tress B, Tress G (2007) Integrative landscape research:

facts and challenges. In: Wu J, Hobbs R (eds) Key topics

in landscape ecology. Cambridge University Press,

Cambridge, pp 246–268

Funtowicz SO, Ravetz JR (1994) The worth of a songbird:

ecological economics as a post-normal science. Ecol Econ

10:197–207

Groß M, Hoffmann-Riem H (2005) Ecological restoration as a

real-world experiment: designing robust implementation

strategies in an urban environment. Public Underst Sci

14:269–284

Guariguata MR, Ostertag R (2001) Neotropical secondary

forest succession: changes in structural and functional

characteristics. For Ecol Manag 148:185–206

Hacking T, Guthrie P (2008) A framework for clarifying the

meaning of Triple Bottom-Line, Integrated, and Sustain-

ability Assessment. Environ Impact Assess 28:73–89

Henderson B, Bui E (2005) Sediment and nutrient transport

models in ERA: determining uncertainty in deterministic

models, LWA/MDBC Project UMO43—Report No 2,

Land & Water Australia, Canberra

Hengsdijk H, Nieuwenhuyse A, Bouman BAM (1998) LUC-

TOR: land use crop technical coefficient generator, ver-

sion 2.0. AB-DLO/C.T. de Wit Graduate School for

Production Ecology, Quantitative Approaches in Systems

Analysis No. 17, Wageningen

Hirsch Hadorn G, Bradley D, Pohl C, Rist S, Wiesmann U

(2006) Implications of transdisciplinarity for sustainabil-

ity research. Ecol Econ 60:119–128

Huigen MGA (2004) First principles of the MameLuke multi-

actor modelling framework for land use change, illus-

trated with a Philippine case study. J Environ Manag

72:5–21

Hulse DW, Gregory S, Baker J (2002) Willamette River Basin

planning atlas. Oregon State University Press, Corvallis

Kanowski J, Catterall CP, Wardell-Johnson GW, Proctor H,

Reis T (2003) Development of forest structure on cleared

rainforest land in eastern Australia under different styles

of reforestation. For Ecol Manag 183:265–280

Keating BA, Carberry PS, Hammer GL, Probert ME, Robert-

son MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke

H, Hochman Z, McLean G, Verburg K, Snow V, Dimes

JP, Silburn M, Wang E, Brown S, Bristow KL, Asseng S,

Chapman S, McCown RL, Freebairn DM, Smith CJ

(2003) An overview of APSIM, a model for farming

systems simulation. Eur J Agron 18:267–288

1196 Landscape Ecol (2011) 26:1179–1198

123

http://dx.doi.org/10.1007/s10533-010-9542-2

http://www.ecologyandsociety.org/vol10/iss1/art27/

http://www.ecologyandsociety.org/vol10/iss1/art27/

http://downloads2.esri.com/support/whitepapers/mo_/shapefile.pdf

http://downloads2.esri.com/support/whitepapers/mo_/shapefile.pdf

Kemp JE, Lovatt RJ, Bahr JC, Kahler CP, Appelman CN

(2007) Pre-clearing vegetation of the coastal lowlands of

the Wet Tropics Bioregion, North Queensland. Cun-

ninghamia 10:285–329

Larson S (2007) Wellbeing and Livelihoods Survey of the Car-

dwell shire: report for the community. CSIRO, Water for a

Healthy Country National Research Flagship, Canberra

Latch P (2007) National recovery plan for the southern cas-

sowary Casuarius casuarius johnsonii. Report to the

Department of the Environment, Water, Heritage and the

Arts, Canberra. Environmental Protection Agency

Letcher RA, Croke BFW, Jakeman AJ, Merritt WS (2006) An

integrated modelling toolbox for water resources assess-

ment and management in highland catchments: model

description. Agric Syst 89:106–131

Low-Choy S, O’Leary R, Mengersen K (2009) Elicitation by

design in ecology: using expert opinion to inform priors

for Bayesian statistical models. Ecology 90(1):265–277

Luz F (2000) Participatory landscape ecology—a basis for

acceptance and implementation. Landsc Urban Plan

50:157–166

McAlpine CA, Seabrook LM, Rhodes JR, Maron M, Smith C,

Bowen ME, Butler SA, Powell O, Ryan JG, Fyfe CT,

Adams-Hosking C, Smith A, Robertson O, Howes A,

Cattarino L (2010) Can a problem-solving approach

strengthen landscape ecology’s contribution to sustainable

landscape planning? Landscape Ecol 25:1155–1168

McKergow LA, Prosser IP, Grayson RB, Heiner D (2004a)

Performance of grass and rainforest riparian buffers in the

wet tropics, far North Queensland. 1. Riparian hydrology.

Aust J Soil Res 42:473–484

McKergow LA, Prosser IP, Grayson RB, Heiner D (2004b)

Performance of grass and rainforest riparian buffers in the

wet tropics, Far North Queensland. 2. Water quality. Aust

J Soil Res 42:485–498

McKergow LA, Prosser IP, Hughes AO, Brodie J (2005a)

Sources of sediment to the Great Barrier Reef World

Heritage Area. Mar Pollut Bull 51:200–211

McKergow LA, Prosser IP, Hughes AO, Brodie J (2005b)

Regional scale nutrient modelling: exports to the Great

Barrier Reef World Heritage Area. Mar Pollut Bull

51:186–199

Merritt WS, Croke BFW, Jakeman AJ, Letcher RA, Perez P

(2004) A biophysical toolbox for assessment and man-

agement of land and water resources in rural catchments

in Northern Thailand. Ecol Model 171:279–300

Musacchio LR (2009) The scientific basis for the design of

landscape sustainability: a conceptual framework for

translational landscape research and practice of designed

landscapes and the six Es of landscape sustainability.

Landscape Ecol 24:101–993

Nassauer JI, Corry RC (2004) Using normative scenarios in

landscape ecology. Landscape Ecol 19:343–356

Nassauer JI, Opdam P (2008) Design in science: extending the

landscape ecology paradigm. Landscape Ecol 23:633–644

Naveh Z (2000) What is holistic landscape ecology? A con-

ceptual introduction. Landsc Urban Plan 50:7–26

Naveh Z (2007) Landscape ecology and sustainability. Land-

scape Ecol 22:1437–1440

Neldner VJ, Wilson BA, Thompson EJ, Dillewaard HA (2005)

Methodology for survey and mapping of regional

ecosystems and vegetation communities in Queensland.

Version 3.0. Queensland Herbarium. Environmental Pro-

tection Agency, Brisbane, Australia

Nicolson CR, Starfield AM, Kofinas GP, Kruse JA (2002) Ten

heuristics for interdisciplinary modelling projects. Eco-

systems 5:376–384

Nowotny H, Scott P, Gibbons M (2002) Re-thinking science—

knowledge and the public in an age of uncertainty. Polity

Press, Cambridge

NRMMC (2005) Directions for the national reserve system—a

partnership approach. Department of the Environment and

Heritage, Australian Government, Canberra

Opdam P (2007) Deconstructing and reassembling the land-

scape system. Landscape Ecol 22:1445–1446

Opdam P (2010) Learning science from practice. Landscape

Ecol 25:821–823

Pahl-Wostl C (2006) The importance of social learning in

restoring the multifunctionality of rivers and floodplains.

Ecol Soc 11, art10

Pearson DM, Gorman JT (2010) Exploring the relevance of a

landscape ecological paradigm for sustainable landscapes

and livelihoods: a case application from the Northern

Territory, Australia. Landscape Ecol 25:1169–1183

Pitt G, Grounds S, van den Berg D, Denham R (2007) Mapping

land use: land use change mapping from 1999 to 2004 for

the Tully River catchment. Queensland Department of

Natural Resources and Water, Brisbane, Queensland

Potschin MB, Haines-Young RH (2006) Landscapes and sus-

tainability. Landsc Urban Plan 75:155–161

Queensland Herbarium (2007) Regional Ecosystem Description

Database (REDD). Version 5.0. Updated November 2007.

Environmental Protection Agency, Brisbane, Queensland

Rahman JM, Seaton SP, Cuddy SM (2004) Making frame-

works more useable: using model introspection and

metadata to develop model processing tools. Environ

Modell Softw 19:275–284

Renn O, Webler T, Wiedeman P (eds) (1995) Fairness and

competence in citizen participation; evaluating models for

environmental discourse. Kluwer Academic Press, Boston

Roebeling PC, Bohnet I, Smith M, Westcott D, Kroon F,

Hartcher M, Hodgen M, Vleeshouwer J (2005) Land-

scapes Toolkit for triple-bottom-line assessment of land

use scenarios in Great Barrier Reef catchments. In: Zerger

A, Argent R (eds) MODSIM 2005 International Congress

on Modelling and Simulation. December, 2005,

pp 711–717

Roebeling PC, Van Grieken ME, Webster AJ, Biggs J, Thor-

burn P (2009) Cost-effective water quality improvement

in linked terrestrial and marine ecosystems: a spatial

environmental-economic modelling approach. Mar

Freshw Res 60:1150–1158

Roetter RP, Hoanh CT, Laborte AG, van Keulen H, van

Ittersum MK, Dreiser C, van Diepen CA, de Ridder N,

van Laar HH (2005) Integration of Systems Network

(SysNet) tools for regional land use scenario analysis in

Asia. Environ Modell Softw 20:291–307

Santelmann MV, White D, Freemark K, Nassauer JI, Eilers JM,

Vache KB, Danielson BJ, Corry RC, Clark ME, Polasky

S, Cruse RM, Sifneos J, Rustigian H, Coiner C, Wu J,

Debinski D (2004) Assessing alternative futures for agri-

culture in Iowa, USA. Landscape Ecol 19:357–374

Landscape Ecol (2011) 26:1179–1198 1197

123

Sarkar S, Pressey RL, Faith DP, Margules CR, Fuller T, Stoms

DM, Moffett A, Wilson K, Williams KJ, Williams PH,

Andelman S (2006) Biodiversity conservation planning

tools: present status and challenges for the future. Annu

Rev Environ Resour 31:123–159

Schroeder B, Wood A, Hurney A, Panitz J (2007) Accelerating

the adoption of best management practice nutrient man-

agement: wet tropics. BSES Limited/CSR, Bundaberg

Sherman BS, Brodie JE, Cogle AL, Carroll C (2007) Appro-

priate use of catchment models for water quality target

setting and land-use management. In: Webb BW, De Boer

D (eds) Water quality and sediment behaviour of the

future. Predictions for the 21st century. IAHS Publ. 314,

IAHS Press, pp 239–250

Smith CD, Heiner IJ, Murtha GG, Cannon MG (2004) Agri-

cultural land suitability of the Wet Tropics Coast, North

Queensland: Cardwell-Innisfail area. State of Queensland,

Department of Natural Resources and Mines, Coorparoo

DC, Queensland

Stoorvogel JJ, Antle JM (2001) Regional land use analysis: the

development of operational tools. Agric Syst 70:623–640

Stoorvogel JJ, Antle JM, Crissman CC, Bowen W (2004) The

tradeoff analysis model: integrated bio-physical and eco-

nomic modeling of agricultural production systems. Agric

Syst 80:43–66

Tabara JD, Pahl-Wostl C (2007) Sustainability learning in

natural resource use and management. Ecol Soc 12(2):3

art3

Tansey J, Carmichael J, VanWynsberghe R, Robinson J (2002)

The future is not what it used to be: participatory inte-

grated assessment in the Georgia Basin. Global Environ

Change 12:97–104

Termorshuizen JW, Opdam P, Van Den Brink A (2007)

Incorporating ecological sustainability in landscape plan-

ning. Landsc Urban Plan 79:374–384

Thackway R, Lesslie R (2006) Reporting vegetation condition

using the Vegetation Assets, States and Transitions

(VAST) framework. Ecol Manag Restor 7:53–62

Thorburn PJ (2004) Influences of N fertilizer management on

off-site loss of water contaminants. In: Roberston LN,

Troedson RJ (eds) Cane farming to improve water quality.

SRDC technical report 1/2004. Brisbane, Australia,

pp 32–34

Thorburn PJ, Meier EA, Probert ME (2005) Modelling nitro-

gen dynamics in sugarcane systems: recent advances and

applications. Field Crop Res 92:337–351

Van Grieken ME, Thomas CT, Roebeling PC, Thorburn PJ

(2011) Integrating economic drivers of social change into

agricultural water quality improvement strategies. Agric

Ecosyst Environ. doi:10.1016/j.agee.2011.06.013

Valencia-Sandoval C, Flanders DN, Kozak RA (2010) Partic-

ipatory landscape planning and sustainable community

development: methodological observations from a case

study in rural Mexico. Landsc Urban Plan 94:63–70

Veldkamp A, Lambin EF (2001) Predicting land-use change.

Agric Ecosyst Environ 85:1–6

Verburg PH, Veldkamp A (2001) The role of spatially explicit

models in land-use change research: a case study for

cropping patterns in China. Agric Ecosyst Environ 85:

177–190

Verburg PH, Veldkamp A, Rounsevell MDA (2006) Scenario-

based studies of future land use in Europe. Agric Ecosyst

Environ 114:1–6

Wilkinson S, Henderson RA, Chen Y, Sherman B, (2004)

SedNet user Guide. Client report, CSIRO Land and

Water, Canberra

Wilkinson SN, Prosser IP, Rustomji P, Read AM (2009)

Modelling and testing spatially distributed sediment

budgets to relate erosion processes to suspended sediment

yields. Environ Modell Softw 24:489–501

Williams KJ, Faith DP, Ferrier S, Hill R, Pert P (2009)

Potential gaps in the complementary representation of

regional ecosystems within protected areas of the Wet

Tropics natural resource management planning region. In:

Taylor R, Long S (eds) Proceedings of the 2008 marine

and tropical sciences research facility annual conference,

Cairns, pp 150–183

Witte C, van den Berg D, Rowland T, O’Donnell T, Denham

R, Pitt G, Simpson J (2006) Mapping land use in

Queensland—Technical report on the 1999 land use map

for Queensland, Department of Natural Resources, Mines

and Water, Brisbane

Wooldridge S, Brodie JE, Furnas M (2006) Exposure of inner-

shelf reefs to nutrient enriched runoff entering the Great

Barrier Reef Lagoon: post European changes and the design

of water quality targets. Mar Pollut Bull 52:1467–1479

Wu JG (2006) Landscape ecology, cross-disciplinarity, and

sustainability science. Landscape Ecol 21:1–4

1198 Landscape Ecol (2011) 26:1179–1198

123

http://dx.doi.org/10.1016/j.agee.2011.06.013

Landscapes Toolkit: an integrated modelling framework to assist stakeholders in exploring options...

Documents

Transcript of Landscapes Toolkit: an integrated modelling framework to assist stakeholders in exploring options...