Simple models for managing complex social–ecological systems: The Landscape Futures Analysis Tool (LFAT)
Introduction
Increasing global demand for food, energy, water, and other ecosystem goods and services, combined with the need to mitigate and adapt to climate change, is generating unprecedented competition for land (Bryan et al., 2013, Foley et al., 2011, Smith et al., 2010). Policy, planning, and management (hereafter simply management) of competing land uses and their impacts on natural capital and ecosystem services have been largely implemented at a landscape scale through regional management (Hajkowicz, 2009, Jones et al., 2013, Robins and Dovers, 2007). The complexity of environmental, economic, and social processes interacting over space and time in regional social–ecological systems (Liu et al., 2007) makes it difficult to tease out cause and effect, to identify leverage points for targeting management interventions, and to judge the likely effectiveness of those interventions. Increasingly, there is a need to identify key system drivers and explore variation across multiple future scenarios for supporting decisions (e.g. Dale et al., 2013). With a large number of variables it can be particularly difficult to forecast the future influence of management strategies with much certainty (Prato, 2007). Further complicating matters, planning horizons are often short relative to the time frames of the social–ecological processes that respond to intervention. To make informed management choices, decision-makers need to understand the impacts of a range of available options on multiple natural capital assets and ecosystem services as well as social-economic outcomes under future scenarios (Bateman et al., 2013).
Integrated assessment and modelling (IAM) can provide valuable information that can quantify complex social–ecological processes and interactions, inform stakeholders, and help decision makers weigh-up the costs, benefits, and trade-offs of regional management options (Jakeman and Letcher, 2003, Laniak et al., 2013). IAM tools can be used to explore the influence of management in the context of multiple interacting economic and environmental drivers that motivate land use and management changes over time which, in turn, result in multiple benefits and trade-offs for natural capital and ecosystem services (Bryan, 2013). For example, exploring planning scenarios of various combinations of cropping, grazing, forestry, biofuel production, and residential development, Goldstein et al. (2012) integrated ecosystem services and economic outcomes to identify land use options that satisfied diverse stakeholders and planning objectives. Similarly, Raudsepp-Hearne et al. (2010) examined bundles of ecosystem services and identified trade-offs between provisioning services (e.g. agriculture and fresh water), cultural services (e.g. recreation and tourism) and regulating services (e.g. carbon sequestration and soil fertility). Examining the economic potential for land use change from traditional agriculture to carbon monocultures and environmental plantings, Bryan and Crossman (2013) found complex interactions between financial incentives and the provision of multiple ecosystem services (i.e. food and fibre, fresh water, carbon sequestration, habitat).
Despite the success of IAM in understanding future alternatives for regional social–ecological systems, the outputs have often not been widely adopted in regional policy, planning, and management. This problem has been recognized as the implementation gap in conservation planning where sophisticated plans have rarely been adopted in practice (Knight et al., 2008, Luz, 2000). Part of the problem is that typically short planning horizons have focussed on the more immediate issues in managing regional natural capital and ecosystem services rather than longer term future impacts. However, increasing realization and acceptance of the long term and complex nature of regional social–ecological processes and the need to manage them is promoting greater interest in the outputs of IAM. In response, several attempts have been made to make IAM outputs more accessible and encourage its use in supporting decision making for regional management. For example, companion modelling has been used within participatory frameworks to engage stakeholders in the design and development of land and water management plans (e.g. Etienne et al., 2011, Gurung et al., 2006). Other deliberative processes that seek to enhance stakeholder understanding, interaction and participation have also been used to improve management decisions in complex settings with competing interests (e.g. Bryan and Kandulu, 2011, Cundill and Rodela, 2012, Garmendia and Gamboa, 2012, Hewitt et al., 2014). Some recent studies have incorporated visualisation methods (e.g. Pettit et al., 2011) and web-based interfaces (e.g. Labiosa et al., 2013, Rao et al., 2007) to deliver complex, integrated landscape scale information and facilitate participatory planning for supporting regional decision making. Similarly, many products such GIS plugins (e.g. Pert et al., 2013, Sharp et al., 2014) or web-based interfaces (e.g. Liekens et al., 2013) have been developed through community-based engagement processes in order to tailor their product and promote implementation. Nonetheless, barriers to accessing and using complex information for supporting regional policy, planning, and management decisions still remain due to several factors including limited quantitative analytical capacity, limited time and willingness to engage with information, and the need for specialized technical requirements such as software or data.
In this paper we outline the development and parameterisation of a web-based tool designed to overcome the implementation gap. The Landscape Futures Analysis Tool (LFAT) was built around series of simple models presented within an online interface designed to facilitate the exploration of environmental and economic scenarios on landscape futures for supporting regional management decision making. Four high priority regional planning issues were identified through a process of community engagement. These issues; agricultural production, carbon sequestration, biodiversity conservation, and weed management, were each addressed using different simple analytical models that were implemented within the online interface by way of separate planning modules. The online interface allows users to define a range of scenarios within each module in order to explore combinations of key variables and examine the impact on agricultural production, carbon sequestration, biodiversity conservation and weed management through visual outputs including interactive maps and summary statistics. By combining the simple models and the online interface the LFAT overcomes some of the barriers to using complex information for supporting regional policy, planning and management decisions. It provides excellent quantitative analytical capacity, a sound platform through which to engage with the information and overcomes limitations due to specialized technical requirements. In order to demonstrate LFAT functioning and outputs we provide a case study from the Eyre Peninsula Natural Resource Management (NRM) region including implementation and model parameterisation. The LFAT can be accessed directly through the website http://lfat.org.au/lfat.
Section snippets
Methods
The LFAT has been specifically designed so that different data can be used to parameterise each of the simple analytical models and thus facilitate easy application of the tool to different regions. This is reflected in the structure of the Methods below. In Section 2.1 (Engagement process) we briefly outline the engagement process through which the different planning issues were identified. In Section 2.2 (Model description) we explain the simple models that were developed to address each of
Model implementation and results
We undertook a case study to demonstrate the implementation and application of the simple models for communicating complex information to support regional management in the 55 000 km2 Eyre Peninsula region in South Australia (Fig. 1). The climate is Mediterranean with rainfall ranging from 250 mm yr−1 in the north and north-west to more than 500 mm yr−1 in the south. The major land use in the region is rain-fed cereal cropping with some sheep grazing. Remnant natural land covers approximately
Discussion
Here we present the development of the LFAT, an online tool designed to overcome the implementation gap and support management decisions in regional social–ecological systems. In order to bridge the implementation gap, the tool was designed for communicating complex information in a conceptually and physically accessible format. To facilitate this, the four simple models at the heart of the tool were specifically chosen to make the information within accessible to the non-expert. In order to
Conclusion
Regional environmental management can present complex challenges for decision making that requires understanding of the possible future impact of different decisions under alternative future environmental and economic scenarios. IAM can provide valuable information about potential landscape futures—identifying environmental and economic drivers and quantifying the possible consequences of different policy, planning, and management options. However, despite the utility of IAM, the outputs have
Acknowledgements
This work was carried out with financial support from the Australian Government (through the Department of Climate Change and Energy Efficiency and the National Water Commission), the National Climate Change Adaptation Research Facility and CSIRO’s Climate Adaptation and Sustainable Agriculture Flagships. The views expressed herein are not necessarily the views of the Commonwealth, and the Commonwealth does not accept responsibility for any information or advice contained herein. We would also
References (74)
- et al.
Parameterisation of 3-PG model for fast-growing Eucalyptus grandis plantations
For. Ecol. Manag.
(2004) - et al.
Impacts of soil damage by grazing livestock on crop productivity
Soil Tillage Res.
(2011) Incentives, land use, and ecosystem services: synthesizing complex linkages
Environ. Sci. Policy
(2013)- et al.
Landscape futures analysis: assessing the impacts of environmental targets under alternative spatial policy options and future scenarios
Environ. Model. Softw.
(2011) - et al.
The second industrial transformation of Australian landscapes
Curr. Opin. Environ. Sustain.
(2013) An ecological perspective on the valuation of ecosystem services
Biol. Conserv.
(2004)- et al.
An invasive plant and climate change threat index for weed risk management: integrating habitat distribution pattern and dispersal process
Ecol. Indic.
(2011) - et al.
Reconfiguring an irrigation landscape to improve provision of ecosystem services
Ecol. Econ.
(2010) - et al.
A review of assertions about the processes and outcomes of social learning in natural resource management
J. Environ. Manag.
(2012) - et al.
Cover crops effect on farm benefits and nitrate leaching: linking economic and environmental analysis
Agric. Syst.
(2013)
Weighting social preferences in participatory multi-criteria evaluations: a case study on sustainable natural resource management
Ecol. Econ.
The evolution of Australia's natural resource management programs: towards improved targeting and evaluation of investments
Land Use Policy
Is getting a conservation model used more important than getting it accurate?
Biol. Conserv.
Participatory land use modelling, pathways to an integrated approach
Environ. Model. Softw.
Integrated assessment and modelling: features, principles and examples for catchment management
Environ. Model. Softw.
An overview of APSIM, a model designed for farming systems simulation
Eur. J. Agron.
An integrated multi-criteria scenario evaluation web tool for participatory land-use planning in urbanized areas: the ecosystem portfolio Model
Environ. Model. Softw.
A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning
For. Ecol. Manag.
A review of Bayesian belief networks in ecosystem service modelling
Environ. Model. Softw.
Integrated environmental modeling: a vision and roadmap for the future
Environ. Model. Softw.
Developing a value function for nature development and land use policy in Flanders, Belgium
Land Use Policy
Potential impact of climate change on wheat yield in South Australia
Agric. For. Meteorol.
Participatory landscape ecology – a basis for acceptance and implementation
Landsc. Urban Plan.
Landscape zonation, benefit functions and target-based planning: unifying reserve selection strategies
Biol. Conserv.
Predicting growth and sequestration of carbon by plantations growing in regions of low-rainfall in southern Australia
For. Ecol. Manag.
Economic and employment implications of a carbon market for integrated farm forestry and biodiverse environmental plantings
Land Use Policy
Economic and employment implications of a carbon market for industrial plantation forestry
Land Use Policy
Participatory development of a new interactive tool for capturing social and ecological dynamism in conservation prioritization
Landsc. Urban Plan.
Identifying strengths and weaknesses of landscape visualisation for effective communication of future alternatives
Landsc. Urban Plan.
Evaluating land use plans under uncertainty
Land Use Policy
A web-based GIS decision support system for managing and planning USDA's Conservation Reserve Program (CRP)
Environ. Model. Softw.
Australian Commodity Statistics 2010
REST and web services
The impact of temperature variability on wheat yields
Glob. Change Biol.
Bringing ecosystem services into economic decision-making: land use in the United Kingdom
Science
Combining exploratory scenarios and participatory backcasting: using an agent-based model in participatory policy design for a multi-functional landscape
Landsc. Ecol.
Landscapes toolkit: an integrated modelling framework to assist stakeholders in exploring options for sustainable landscape development
Landsc. Ecol.
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