Representing ecological processes in agent-based ...

This article was downloaded by: [University of Waterloo] On: 03 September 2013, At: 09:08 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of Land Use Science Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tlus20

Representing ecological processes in agent-based models of land use and cover change a

b

c

Kristina A. Luus , Derek T. Robinson & Peter J. Deadman a

Interdisciplinary Centre on Climate Change, University of Waterloo, Waterloo, ON, Canada b

School of Geosciences, Edinburgh University, Edinburgh, UK

c

Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada Published online: 19 Dec 2011.

To cite this article: Kristina A. Luus, Derek T. Robinson & Peter J. Deadman (2013) Representing ecological processes in agent-based models of land use and cover change, Journal of Land Use Science, 8:2, 175-198, DOI: 10.1080/1747423X.2011.640357 To link to this article: http://dx.doi.org/10.1080/1747423X.2011.640357

PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &

Downloaded by [University of Waterloo] at 09:08 03 September 2013

Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions

Journal of Land Use Science, 2013 Vol. 8, No. 2, 175–198, http://dx.doi.org/10.1080/1747423X.2011.640357

Representing ecological processes in agent-based models of land use and cover change Kristina A. Luusa *, Derek T. Robinsonb and Peter J. Deadmanc


b

a Interdisciplinary Centre on Climate Change, University of Waterloo, Waterloo, ON, Canada; School of Geosciences, Edinburgh University, Edinburgh, UK; c Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada

(Received 31 October 2010; final version received 8 November 2011) Agent-based models of land use and cover change (ABMs/LUCC) have traditionally represented land-use and land-cover changes as arising from social, economic and demographic conditions, while spatial ecological models have tended to simulate the environmental impacts of spatially aggregated human decisions. Incorporating a dynamic representation of ecosystem processes into ABMs/LUCC can enable new or counter-intuitive insights to be gained into why certain path-dependent outcomes arise and can also spatially constrain model processes, thereby improving the spatial fit of model output against observational data. A framework is therefore provided to assist in determining an optimal approach for representing ecological processes in an ABM/LUCC according to the research question and desired application of the model. Relevant challenges limiting the integration of complex, dynamic representations of ecosystem processes into ABMs/LUCC are then assessed, with solutions provided from recent examples. ABMs/LUCC that use a dynamic representation of ecological processes may be applied to investigate the complex, long-term responses of the coupled human–natural system to a variety of climatic shifts and ecological disturbances. Keywords: agent-based model; coupled human–environmental system; land-cover change; land-use change; environmental process; ecological model

1. Introduction Agent-based models of land use and cover change (ABMs/LUCC) model the co-evolution of decision-making actors and their environment by coupling the representation of a natural landscape with decision-making agents (Connell, Parker, Berger, and Manson 2001; Parker, Manson, Janssen, Hoffman, and Deadman 2003). Historically, ABMs/LUCC have been developed by social scientists to represent human system dynamics and have tended to use a simple representation of environmental characteristics (Veldkamp and Verburg 2004). Complementary efforts within the ecological modelling community led to the development of ecological models that simulate the impacts of human disturbances on natural processes using a prescribed set of changes in land use and land management. Disciplinary divides restricted the progress of coupled human–natural system research (Liu et al. 2007) and resulted in both communities duplicating efforts to yield independent insights into the socio-economic drivers of resource management decisions (e.g. Matthews, Gilbert, Roach, *Corresponding author. Email: [email protected] © 2013 Taylor & Francis


176

K.A. Luus et al.

Ponthill, and Gotts 2007) and the ecological consequences of anthropogenic impacts (e.g. Shugart and Smith 1996; Cloern 2001). Results pointed to the need to improve our understanding of the dynamics of land-use change as a coupled human–natural system using an approach that integrates knowledge about how human and natural systems interact and behave independently (Parker, Hessl, and Davis 2008). Initially, LUCC modellers incorporated the biophysical characteristics of the landscape (e.g. soil quality, slope and elevation) since they played a pivotal role in the suitability of land for specific uses (e.g. Berger 2001; Lim, Deadman, Moran, Brondizio, and McCracken 2002; Huigen 2004). Heterogeneity in the spatial distribution of resources made certain locations more desirable for economic activities and led to heterogeneous patterns of landuse and land-cover changes (e.g. conversion of forest to agriculture in floodplain areas). In these representations, biophysical factors also influenced the pay-offs individuals or organizations would acquire from the land, so that regions with greater soil quality, for example, held greater yield potentials (Lim et al. 2002). As ABMs/LUCC matured, modellers further acknowledged that the ecological impacts of land-use change usually reduce the provision of ecosystem services, which drives subsequent changes in land use (e.g. biodiversity, Hermy and Verheyen 2007; and erosion prevention, Yang, Kanae, Oki, Koike, and Musiake 2003). Part of the goal to better understand land-use change as a process arising from a coupled human–natural system is based on the recognition that patterns of land use are not solely determined by socio-economic drivers or policy initiatives. Land-use and land-cover changes are also driven by historical location-based decisions that have altered the current provision of ecosystem services and future vulnerability of ecosystem function (Lambin et al. 2001). Incorporating explicit and dynamic representations of the impacts, interactions and feedbacks between human and ecological systems in ABMs/LUCC could offer several important benefits. Firstly, since declines in ecosystem function and services constrain future land-use decisions and the spatial patterns of land-use change (Bakker et al. 2005), incorporating ecosystem processes may allow spatial patterns of land use to be better represented. Secondly, including the role of feedbacks between the ecological system and human decision-making may provide insights into why certain path-dependent outcomes arise. Thirdly, including ecosystem processes in ABMs/LUCC may lead to surprising or counter-intuitive findings when investigating the adaptation of human decision-making to long-term changes in ecosystem services. The influence of long-term shifts in climate regimes or ecological health on economic, demographic and social drivers of land-use change could therefore be better analysed. Finally, changes in land use and land cover can undermine the long-term capacity of ecosystems to sustain food production and regulate climate (Foley et al. 2005; Pielke 2005). ABMs/LUCC that incorporate ecological processes could therefore contribute to the understanding of human decisions driving land-use change, as well as the long-term ecological, climatological and atmospheric consequences of land-use change. The objectives of this article are to explore key themes and challenges encountered when deciding upon a strategy for representing ecological processes in ABMs/LUCC and to provide a framework to assist in selecting how to represent ecological processes in an ABM/LUCC. We first examine static and dynamic approaches used to represent ecological characteristics and processes and review criterion that can be used to select the appropriate approach for a given problem. We then review a variety of applications of ABMs/LUCC to examine the influence of the desired application of the model on the selected approach for representing ecosystem dynamics. Finally, we provide a discussion of how to overcome challenges posed by incorporating dynamic or complex representations of ecosystem

Journal of Land Use Science

177

processes into ABMs/LUCC. It is hoped that the framework provided in this article will assist modellers in selecting and applying optimal strategies for representing ecological characteristics in ABMs/LUCC.


2. Representations of ecological processes and characteristics in ABMs/LUCC Historically, ABMs/LUCC have tended to represent the ecological environment using a cellular automaton that represents ecological changes in terms of the state of land use or type of land cover at a location. Land cover and land use have therefore acted as proxies for ecosystem function and services and ecological processes have not been represented as drivers of human decision-making. The representation of the ecosystem can therefore be regarded as primarily being focused on setting landscape characteristics (e.g. topography in a model by Evans and Kelley 2004) that remain static throughout the entire model run. With growing recognition that land-use outcomes are determined through interactions between ecosystem processes and the subsequent response of household decisions (e.g. Liu et al. 2007), ABMs/LUCC are now implemented using more complex approaches to represent ecosystem processes. In these formulations, approaches are used to calculate environmental variables that change dynamically throughout a model run. The sections below first outline three different scenarios in which it may be beneficial to represent the environment using landscape characteristics that remain static throughout an entire model run. An analysis is then provided of four approaches used to represent ecological processes in ABMs/LUCC so that environmental variables change dynamically throughout a model run. These approaches are classified as transition, regression, individual-based and general equilibrium formulations. A framework for selecting an appropriate representation of ecological processes in an ABM/LUCC is provided in Table 1. While exceptions to the contents of Table 1 can be found, the modelling approaches suggested have been successfully applied in the situations described and offer the best initial approach for representing different degrees of interaction between human and natural systems. 2.1. Representation of ecological characteristics In certain situations, it is beneficial to represent the condition of the ecological system as set characteristics that do not vary throughout a model run, rather than as a process. In these model formulations, the land use changes during a model run, but other aspects of the ecosystem (e.g. soil quality, topography and access to water) remain unchanged. Three situations where it is beneficial to represent ecosystems primarily using ecological characteristics are described below. Firstly, when the research question is not spatially explicit or spatially dependent, it may not be necessary to have accurate estimates of changes in ecosystem function or services. For example, the environmental landscape is represented in the MameLuke framework using a Geographic Information System (GIS) map of slope, land cover, roads and rivers (Huigen 2004). This framework can then be applied to simulate a variety of research questions regarding the societal and demographic shifts associated with land-use change. A benefit of this approach is that it is highly integrative and can be adapted to various other regions more easily than if it contained biome-specific ecological processes. A limitation of the simplicity and flexibility of the MameLuke approach is that it limits understanding of the interactions and feedbacks between ecological processes and human decision-making, which correspondingly limits the potential policy experimentation that may be performed.

178

K.A. Luus et al.


Table 1. Summary of conditions for ecological representation in agent-based models of land-use and land-cover changes. Desired level of ecological representation

Modelling approach

Research question is not spatially dependent Ecological processes change over a longer timescale than the temporal extent of the model Data limitations prevent the representation of heterogeneous drivers of ecological outcomes Ecological characteristics change in a known ordinal or nominal manner and explanatory driver variables are unavailable Human response to ecological change is heuristic, and detailed monitoring by human actors is unlikely to occur Ecological change is relevant to system behaviour but is not the focus or output of the model Ecological function is a model output and influences human decision-making (explanatory data available) Examinations of interactions between species dynamics and human decisions a goal Ecological processes differentially influence and respond to human behaviour or environmental variables across space and time Estimation of ecosystem function is a priority and output of the model and spatial interaction among ecosystem processes is not

Static representation Static representation Static representation Transition rules Transition rules Transition rules Regression Individual based Individual based Equilibrium based

In situations where the research questions are relatively broad and are not concerned with accurately representing spatial patterns of land-use change, it may suffice to use land cover as a proxy for the magnitude of change in ecosystem function or service. The spatial variation within the study region and range of anthropogenic impacts may also constrain the ecosystem model outputs such that a static representation provides similar results in a simplified manner. This approach is most useful when research questions are not spatially dependent and focus more on research questions regarding the human system rather than on land-use and land-cover changes in coupled human–environmental systems. A second situation in which it may be beneficial to represent ecosystem processes as static is when the speed over which an environmental process changes is much slower than the temporal extent of the model. For example, in relatively flat landscapes, erosion alters the elevation and slope of a landscape over very long time periods. Similarly, steep landscapes may be influenced by sea level rise over very long time periods, but these changes are unlikely to occur quickly enough to have an important influence on land-use decisions. In these situations, it is more efficient to set landscape characteristics as constant over the duration of a model run. However, these same characteristics may drive land-use decisions in different locations. For example, erosion drives land-use decisions in Lesvos, Greece (Bakker et al. 2005). Similarly, a sea level rise of 0.5 m could cause substantial inundation and damage leading to rapid land-use change in Alexandria, Egypt (El-Raey, Dewidar, and El-Hattab 1999). It is therefore important to assess the timescale of environmental processes in relation to model temporal extent. Finally, data limitations can prevent modellers from using a dynamic approach to represent ecosystem processes by reducing the ability to verify, calibrate and validate a dynamic model. Data limitations are most likely to arise when the study region is very large, either when the data are not collected at a fine enough resolution to incorporate spatial heterogeneities (e.g. soil data) or when there is not enough information about how the


179


land-management practices vary across space or the agent population. In situations where data limitations preclude the ability for the process to provide heterogeneous outcomes, it may be necessary to use only a representative value of an ecosystem characteristic, even when it is otherwise desirable to represent ecological processes using a dynamic approach. Approaches that use representative values of ecological characteristics are therefore most likely to be useful in situations where (1) research questions are broad or do not require spatially constrained output; (2) the speed at which processes vary is much slower than the temporal extents of the model or (3) inadequate data exist to inform the implementation of a dynamic representation. In remaining situations, spatial constraints and model utility are improved by using a dynamic representation of ecological processes.

2.2. Dynamic representation of ecological processes A dynamic representation of an ecological process allows the variables representing environmental characteristics to change throughout the course of a model run. Ecological processes that differentially impact and respond to human behaviour, based on changes in the socio-ecological contexts of the model, should ideally be represented in this manner. Dynamic representations of ecological processes can be classified as using transition rules, regressions, individual-based approaches or general equilibrium modelling approaches. The scale and type of research problems for which each approach is best suited are described below and summarized in Table 1. 2.2.1. Transition rules One of the simplest approaches for representing an ecological process dynamically in ABMs/LUCC is to change the ecological characteristics of a location over time using a set of simple transition rules. Implementing a transition-rule-based approach to modelling ecological processes involves first selecting an ecological characteristic of the landscape (e.g. soil quality). A series of rules that change that ecological characteristic over time or via agent actions must then be created. Lastly, the rules and their impact on the ecological characteristic are applied over time. Transition rules may be extended to incorporate spatial interaction based on the surrounding states of a location. Typically, this approach is implemented as a cellular automaton or cellular model that changes the values of the host (central) cell based on the values of the Moore or Von Neumann neighbours (e.g. Manson 2005a). Using a transition-rule approach, Manson (2005a) represented soil degradation in the agricultural landscape of the Southern Yucatan peninsula, Mexico. In their ABM/LUCC, soil quality was represented at each location as a qualitative value that changed based on different land-management choices. Leaving a parcel in fallow increased soil fertility by one unit, while implementing commercial agriculture decreased soil fertility by three units. The relative impact of different land-management choices under different land-use scenarios on regional soil quality could then be evaluated in a qualitative manner using this approach (Manson 2005a). Rule-based transitions can be used to set both linear transitions from past states as described above and reversible, cyclic or current-state-based transitions through Markov chain models (Satake, Leslie, Iwasa, and Levin 2007). The use of transition rules provides a transparent and simple approach for representing ecological processes in ABMs/LUCC. In many cases, the ecological characteristics change in an ordinal or nominal manner, which represents the quantity or quality of an ecosystem function or service.

180

K.A. Luus et al.

The challenge with this approach involves mapping the modelled values back to realworld processes. For example, the modelled characteristics (e.g. soil fertility) may not closely correspond to measurable observations (e.g. soil moisture, concentrations of various nutrients, etc.). This approach is therefore best suited to situations where extensive quantitative data on ecosystem processes are not available. Furthermore, the use of transition rules is based, in part, on the assumption that changes are reversible, and that long-term degradation does not substantially influence ecosystem function. This approach may therefore be best when working over relatively short time periods or when simulating highly resilient ecosystems.


2.2.2. Regression The use of regression-based models enables researchers to generate empirical estimates of the outcome of an ecological process without having to program the underlying mechanics of that process. In cases where explanatory variables are available for estimating the response or behaviour of an ecological process, it has been useful to take a regressionbased approach in the development of ABMs/LUCC. Iterative evaluation of the regression over time provides a representation of changes in the modelled process. When implemented in this way regressions can provide a well-justified estimate of ecological outcomes; however, incorporating feedbacks into the regression is rarely done without the inclusion of agents or other ecological processes. Prior to the development and mainstream adoption of agent-based approaches to model LUCC, regressions were extensively used to predict LUCC (e.g. Theobald and Hobbs 1998) and its ecological effects on vegetation species composition (Foster, Motzkin, and Slater 1998), catchment sediment yield (Dunne 1979) and fish habitat structure (Lammert and Allan 1999). In cases where LUCC was predicted, explanatory variables were based on the biophysical characteristics and geographic characteristics of a location (e.g. topography, soil quality and distance to urban centres). The regression would be applied to the entire landscape with each location receiving a suitability value for LUCC, followed by the implementation of LUCC transitions until a predefined quantity of change was met (Verburg et al. 2002). A downside of this approach is that little interaction is possible between land cover and decision-making. In many cases, regressions are used in ABMs/LUCC more specifically as vegetation growth response functions (e.g. Deadman, Robinson, Moran, and Brondizio 2004; Bah, Toure, Page, Ickowicz and Diop 2006). In ABMs of rural landscape, farming agents make decisions about on-farm decisions such as how much land to farm, what crops to farm and where to plant specific crops. After agents implement farming decisions, regression models are employed to determine the yield of a given crop under specific situational contexts (e.g. climate and nutrient availability). Resulting yields affect the subsequent agent decisions and in some cases also alter the ecological characteristics of the landscape (e.g. soil nutrient degradation). Using a regression-based approach to represent the ecological processes of crop growth is ideal when crop production and yields are not the primary output or goal of the model. For example, Deadman et al. (2004) use the crop yields in a simple economic model to determine farmer success and failure rates. The success and failure of farms influenced the extent of deforestation and patterns of farmed land along the TransAmazon Highway in Brazil. If the goal of the model was to estimate specific changes in crop yields, a more rigorous and dynamic crop model would have been employed instead. Regression-based representations of ecological processes associated with LUCC can be improved by incorporating a variety of information on age- or stage-based growth representations using a Markov transition matrix (e.g. Brown, Pijanowski, and Duh 2000)



181

or by incorporating neighbouring characteristics (Verburg, de Nijs, van Eck, Visser, and De Jong 2004a). When implemented on a raster data set, a regression may incorporate neighbourhood counts of different land uses and geographic characteristics to incorporate adjacency effects on the ecological process represented. Spatial interactions between cells can be represented in regression-based approaches through spatial auto-regressive techniques such as principal coordinates of neighbour matrices (Dray, Legendre, and PeresNeto 2006) or geographically weighted regression (GWR) techniques (Fotheringham, Brunsdon, and Charlton 2002). A GWR can be used to give greater influence to the characteristics of closer locations than distant ones in order to better represent the effects of landuse change on local ecological conditions. As a result, GWR has been found to have greater predictive power than ordinary least squares regression and to be less influenced by spatial autocorrelation (Tu and Xia 2008; Ogneva-Himmelberger, Pearsall, and Rakshit 2009). Using a regression-based approach to represent ecological processes is hindered by the requirement for substantial ecological data. In some cases, data may be difficult or expensive to acquire, as observations over long time periods and large regions are typically required. Furthermore, the associations between variables may shift non-linearly with continued anthropogenic disturbances or changes to the environmental system. A regression-based approach assumes stationarity of the ecological system over time such that the same regression parameter estimates can be used throughout model runs. Therefore, when the associations between modelled parameters shift under changing environmental conditions, a regression-based approach is not optimal. 2.2.3. Individual-based Individual-based models (IBMs) can be used to simulate the collective behaviour of plant populations through the performance of individuals (e.g. Zavala 2006). IBMs can be used to simulate trophic dynamics, seeding/reproduction, resilience to natural and anthropogenic disturbances and both inter- and intraspecific competitions (Abia, Angulo, and Lopez-Marcos 2005; Nuttle and Haefner 2007; Wehrli, Weisberg, Schnenberger, Brang, and Bugmann 2007). Direct anthropogenic influences on population structure through planting, weeding and harvesting can also be incorporated into IBMs. ABMs/LUCC can benefit from the inclusion of IBMs when the agents of an ABM/LUCC utilize ecological function or services that are highly dependent on population dynamics at a fine resolution. Like an ABM, IBMs can represent heterogeneity and interaction within a population of individuals (Bithell, Brasington, and Richards 2008). Specifically, in ABMs/LUCC, the IBM should have differential response functions to the agent behaviours or the outcome of their actions on the landscape. Otherwise a regressionor transition-based approach would suffice. IBMs are likely to have the greatest utility in combination with agent-based approaches when species composition is determined by a combination of anthropogenic impacts and natural influences on population (e.g. reproduction, competition and disturbances). For example, an individual-based representation of trees may be useful in ABMs used to evaluate different management practices on forest stand compositions (Caplat, Lepart, and Marty 2006) or different forestry practices on species composition and hydrology (Bithell and Brasington 2009). The main benefit of this approach is in representing the longterm feedbacks associated with decisions, such as the longer-term economic downfalls of intensive deforestation, thereby allowing analysis of these processes and feedbacks. IBMs can be combined with an agent-based approach in situations where research questions address the influence of land-use and land-cover changes on species compositions, where the temporal and spatial scopes are limited and where species

182

K.A. Luus et al.


compositions are driven by a combination of human decisions and biological population controls. So far, IBMs have tended to be combined with ABMs to simulate the effects of land-use change on forest species compositions (e.g. Bithell and Brasington 2009; Verburg and Overmars 2009). However, it could be feasible to integrate IBMs with ABMs to study the influences of economic factors on vegetation species distributions in house gardens, or even animal species distributions. Likewise, individual-based representation of trees in urban, suburban and exurban regions may be useful to estimate carbon storage or the effects of plantings on groundwater run-off. Tree cover in these situations is often represented in small patches where edge and gap dynamics are more prominent than core forest area conditions (Robinson, Brown, and Currie 2009).

2.2.4. General equilibrium General equilibrium models (GEMs) simulate the allocation of natural resources in a system where both natural and economic influences drive the consumption of ecosystem services. Ecosystem processes in these models are typically simulated using biogeochemical models representing the influence of land-use change on land-atmosphere exchange of greenhouse gases (e.g. N2 O and CO2 ) or ecosystem stocks of carbon and nitrogen. The influence of a disturbance in the ecosystem can therefore be simulated, along with its subsequent impacts on markets, households and the ecosystem (Eichner and Pethig 2005). Recently, GEMs have been applied to assess the influence of climate change and landuse change on emissions of greenhouse gases (e.g. Nordhaus and Yang 1996). GEMs have also been constructed by integrating an agent-based approach with existing biogeochemical models of carbon and nitrogen dynamics (e.g. CENTURY by Parton, Schimel, Cole, and Ojima 1987 or BIOME-BGC by Running and Hunt 1993). The resulting models have been used to assess the influence of land-use activity on stocks of carbon and nitrogen (Gaube et al. 2009; Robinson, Brown, and Currie 2009) and to evaluate the potential for soil fertility interventions to be undertaken (Matthews 2006). GEMs can be effectively integrated with agent-based approaches in situations where household decisions are heavily influenced by a need to minimize anthropogenic emissions of greenhouse gases or to maximize soil quality, or where research questions specifically address the adoption of carbon management policies by households. Although GEMs could be used in a variety of situations where factors influenced by biogeochemistry (e.g. soil fertility) affect land-use decision-making, it may not be worthwhile to employ this approach when a simpler, less computationally intensive strategy may be equally valid. It is therefore important to consider the scale and complexity required. A benefit of using GEMs is that they can explicitly incorporate the long-term impacts of anthropogenic disturbances on declines in ecosystem services, and the subsequent adoption of decision-making strategies by households (e.g. Eichner and Pethig 2005). Furthermore, as natural competition is included in these models, the influence of shifts in an ecosystem due to degradation, climate change or natural disasters and their resulting influence on land-use change could also be simulated.

3. Influence of ABM/LUCC application on selection of strategy for representing the environment The need to represent ecological characteristics and processes in ABMs/LUCC vary based on their intended use. The following sections discuss ABM/LUCC environmental


183


representations according to their primary application as exploratory, participatory or predictive.

3.1. Exploratory When ABMs/LUCC are designed to enable a theoretical study of a coupled human– environmental system, the overall behaviour of the model is used as the basis for exploratory analysis. Most ABMs used for thought experiments test hypotheses about human societies and have included simple and low-resolution representations of natural systems (Verburg et al. 2002). For example, the Sugarscape ABM uses a hypothetical landscape containing only varying amounts of resource (i.e. sugar), which agents must consume to persist. From a basic set of behavioural rules guiding individual actions and interactions among agents and the landscape, social structures emerge that are reminiscent of collective patterns observed in settlements according to resource distributions (Epstein and Axtell 1997). The result is a proof of existence, whereby simple micro-level processes (i.e. agent behaviours) are shown to give rise to aggregate macro-level patterns (Waldrop 1990). An effective model for generating thought experiments is one that provides a tool to examine the dynamics of the phenomena under study. When modelling a system using hypotheses about the nature of land-use interactions, a qualitative sense of disconnect between the hypothesized processes and real processes is adequate for piquing curiosity about what may drive the behaviour of real-world systems. Agent-based models that examine artificial worlds such as Daisyworld (Watson and Lovelock 1983) or Sugarscape (Epstein and Axtell 1996) cannot be examined against observations, as the model is not situated within a real-world context. In this case, ABMs/LUCC that are used as thought experiments are most likely to benefit from a static representation of ecological characteristics, or a simple representation of ecological processes using transition-based rules in situations where research questions directly address these processes. However, it is unlikely that individual-based, regression-based or general equilibrium approaches for representing ecosystem processes would improve the utility of ABMs/LUCC that are applied primarily for exploratory analysis. 3.2. Participatory A participatory approach to natural resource management that incorporates ecological numerical models can produce innovative solutions based on the deep, qualitative understanding of environmental processes by stakeholders (e.g. Lane et al. 2011). SAMBA-GIS is an ABM/LUCC that represents land-use change in northern Vietnam and was created in a participatory manner. The success of this model arises from its ability to create an open setting for discussion and problem solving by representing the experiences, beliefs and expectations of a community (Castella, Trung and Boissau 2005). When a model is used for participatory decision-making, the degree to which model representations of environmental processes are realistic may not be crucial to its function. However, it may be possible that any disconnect between the expected environmental impacts and actual consequences of a given policy may mean that the agreements and decisions made according to ABM/LUCC outputs are inappropriate for real-world application. In situations where resource decisions bring about long-term environmental changes that are not intuitive to stakeholders, an ABM/LUCC may benefit from including the


184

K.A. Luus et al.

representation of environmental processes. For example, a model of erosive run-off was combined with a participatory approach to investigate alternative land-use strategies and potential co-operative actions that could reduce agricultural run-off in Upper Normandy, France (Souchère et al. 2010). Although the purpose of the model was to act as a medium for discussion of potential system outcomes and stakeholder strategies, rather than outcome prediction or hypothesis testing, having a detailed representation of erosive run-off assisted in allowing stakeholders to develop a realistic and effective political solution. The benefit of accurately representing environmental processes in participatory ABMs/LUCC is that these models can guide decision-making to be made within a realistic context which may challenge perceptions of community members regarding impacts of specific resource management strategies. However, ecological approaches may complicate participatory model evaluation due to the added computational cost and complexity of model processes. The resulting model may therefore be more difficult for stakeholders to use and evaluate than models using a simpler approach. For example, Becu, Neel, Schreinemachers, and Sangkapitux (2008) developed a model of water sharing by two villages in a single watershed in northern Thailand that explicitly represented hydrological processes. The utility of this model was limited by the fact that only a quarter of participants understood the purpose of the model after three sessions of working with the model. Similarly, participatory evaluation of a model of land-use change using an ecological approach to represent post-wildfire forest succession was complicated by challenges participants faced in differentiating model structure from scenarios (Millington, Demeritt, and Romero-Calcerrada 2011). A significant trade-off therefore exists between model simplicity and the inclusion of an explicit representation of ecosystem processes. The application of transitionbased dynamic ecosystem processes may be most helpful, followed by the inclusion of regression-based strategies. Although it would possible to incorporate individual-based or general equilibrium strategies in participatory ABMs/LUCC, these approaches are unlikely to be optimal in most situations. When the research questions posed require a complex dynamic representation of ecosystem dynamics, one possible solution may be to seek community input in collecting data, quantifying and designing the representation of human decision-making processes, and later implement an ecological approach to represent the environmental processes or observables of interest to the stakeholders 3.3. Predictive ABMs/LUCC are used to simulate a variety of impacts caused by land-use and landcover changes (Verburg, Veldkamp, Willemen, Overmars, and Castella 2004b) in order to address research questions focused on anthropological (Dean et al. 2000) and economic (Tesfatsion 2002) aspects of household and resource management decision-making (Parker et al. 2003). When the research questions addressed are not specifically environmental, it may seem as though there would be limited benefits associated with improving the representation of environmental processes in predictive ABMs/LUCC. Many researchers therefore decide, reasonably so, to focus on improving the representation of human decision-making. The resulting models, however, may be limited in their ability to predict land-use change outcomes (Iverson 2000; Lancaster and Grant 2003). Yet, if a model is used to gain insight into specific real-world systems, it is important to understand the ability of the model to capture the dynamics of that real system (Gross and Strand 2000). Furthermore,



185

over longer timescales (>100 years), the locations at which environmental impacts are focused depends not only on human decision and landscape characteristics, but also on how cumulative anthropogenic alter long-term (>100 year) environmental processes, such as biodiversity decline, groundwater depletion and resource collapse. Decisions made on the basis of recent and short-term predicted environmental change may be altered dramatically if long-term environmental processes leading to ecosystem disequilibrium are incorporated. Finally, it is unclear whether inferences can be made about which human decisions drive land-use change when these decisions are made in close response to current and anticipated environmental conditions. Incorporating environmental processes into ABMs/LUCC that are used to gain an understanding of land-use change over time can therefore improve the fit of model processes and outcomes against observed data and enable additional insights to be gained into the coupled human–environmental system. ABMs/LUCC that are used to generate predictions or to gain an understanding of the potential impacts of resource management strategies on the coupled human–natural system are likely to benefit greatly from the implementation of a complex, dynamic approach to represent ecosystem dynamics. When these predictive research questions posed are not specifically environmental or when limited access exists to field data, a transition-based approach is likely to be most useful. A regression-based approach is most likely to be useful when access to quantitative observations of ecosystem characteristics over time exists. Examinations of interactions between species dynamics and human decisions over time may be best addressed using IBMs, and questions related to long-term dynamics of ecosystem health, biogeochemical stores and fluxes and human system dynamics are likely to be best addressed using a general equilibrium approach. Incorporating a more complex representation of ecosystem processes in an ABM/LUCC can yield new insights into why path-dependent outcomes arise in coupled human–natural systems and which feedbacks drive these interactions. By incorporating aspects of ecological process models that are separately validated, the resulting model also has the potential to elucidate counter-intuitive findings regarding potential human adaptations to policy or ecosystem shifts. Finally, integrating a more dynamic and interactive approach to representing ecosystem processes into an ABM/LUCC can enable innovative research questions to be addressed, such as the response of land-use change to climatic shifts or natural disturbances. A summary of the optimal approaches for representing ecological processes in an ABM/LUCC is presented in Table 2. The integration of complex, dynamic representations of ecosystem processes has been limited, in part, by the challenges posed by merging ecological and agent-based approaches. The following section therefore assesses these challenges and outlines a number of solutions. Table 2. Framework for selecting which approach to representing ecological processes may be optimal depending on the desired application of the ABMs/LUCC. Desired application of the model Exploratory Participatory Predictive

Optimal approaches for representing ecological processes Static characteristics or dynamic processes (transition rules) Dynamic processes (transition rules or regression) Dynamic processes (transition rules, regression, individual-based or general equilibrium)

186

K.A. Luus et al.

4. Challenges to representing complex ecological processes in ABMs/LUCC


Although the performance and utility of ABMs/LUCC can be improved by representing complex ecological processes, a number of challenges hinder the ability of researchers to represent complex ecological processes in ABMs/LUCC. The following section therefore reviews these challenges and provides prognoses drawn from recent examples. The main challenges are (1) interfacing models so that the influence of ecosystem services is well represented; (2) conceptual and technical hurdles arising if ecological processes are represented by linking an ABM with an ecological model; (3) matching of spatio-temporal resolution of model components and processes; (4) ensuring that model function is at an optimal level of complexity and (5) evaluating the fit of model output against observational data due to differences in the structure, function and parameters of ecological and agent-based models.

4.1. Representing the influence of ecosystem services ABMs/LUCC incorporate the influence of current environmental conditions using a variety of approaches. In theoretical ABMs/LUCC, the success of agents can be determined by environmental conditions. For example, in the Sugarscape model, agents seek out sugar and die when they have inadequate supplies of sugar (Epstein and Axtell 1997). In ABMs/LUCC situated within real-world contexts, current land-use decision-making occurs in response to the current environmental conditions of the land, represented by factors such as soil pH (Lim et al. 2002) or soil fertility (Manson 2005a). ABMs/LUCC used in participatory decision-making contain explicit linkages between decision-making and environmental conditions set according to stakeholder knowledge (Millington et al. 2011). Our review has focused on how approaches to modelling ecological characteristics and processes in ABMs/LUCC may be improved in order to better represent current environmental conditions as they influence land-use decision-making. These approaches have therefore tended to focus on situations where the environmental information incorporated into a model could be directly observed and understood by agents. However, in many cases, the ecological changes are not directly observable by individuals, firms or organizations (e.g. carbon storage and ground water recharge). Unfortunately, when these services are observed, it is due to a phase shift caused by crossing a threshold that changes process behaviour and subsequent resource availability for human consumption (e.g. drought). To better represent the feedbacks from the natural to the human system, we suggest that more ABMs/LUCC model the provision of ecosystem services, whereby Ecosystem services are the benefits people obtain from ecosystems..., which includes products such as food, fuel, and fiber; regulating services such as climate regulation and disease control; and non-material benefits such as spiritual or aesthetic benefits. (MEA 2005)

Examining the monetary impacts of land use allows quantification of extrinsic benefits for ecosystem health that can be used to assess the impacts of policies. However, great caution must be used in interpreting specifics, since the inventory-type data used to translate environmental variables into economic values tend to contain large uncertainties, especially when the timescale is long or the benefits are intrinsic (e.g. aesthetic or symbolic). However, the main benefit of this approach is that it enables agents to directly incorporate information about the current and potential future state of the environment in an economic format that is already used to inform land-use decision-making.


187


4.2. Coupling or integration of models Ecological processes can be represented in ABMs/LUCC by coupling or integrating an agent-based model with an existing ecological model. The models that are likely candidates for linkages to ABM are long established and highly detailed ecological models that are established as accredited representations of biophysical processes (e.g. CENTURY, Parton et al. 1987; SORTIE, Pacala and Deutschman 1995). Coupling models requires that feedbacks between model components are incorporated in model runs, whereas integrating models involves implementing one or two directional influences between model components. Ecological processes have initially been implemented as single direction integrations, with changes in LUCC resulting in altered ecosystem characteristics. More complex feedbacks between model components can encourage discoveries of novel research questions but does so at the expense of additional development time and computational overhead. When coupling or integrating these models, conceptual challenges may arise that make it difficult to fully understand and evaluate model performance. Technical challenges may result in a mismatch between the interaction of processes represented in the human system and those represented in the ecological system such that the model output is not representative of either. A central conceptual challenge in joining ecological and ABM approaches is in designing an effective way for the agent-based and ecological models to interact. From a conceptual standpoint, coupling or integrating models of human and ecological processes requires an understanding of the temporal and spatial scales of the represented processes. If the processes represented in the ecological model occur over a different spatial and temporal scale than the processes represented in the ABM, then it may be more difficult to successfully couple or integrate these models. However, if the processes in both models act at a variety of spatial and temporal scales and are not specific to a single scale of representation, then it is more likely that these models could be integrated or coupled. Secondly, coupling or integration of models requires that there to be feedbacks between these models that are reflective of real-world feedbacks between human decisions and ecological change. Several important technical challenges are also posed by the coupling or integration of models. Linkages between these models can vary from loose coupling (simple file sharing), to tight coupling (sharing of libraries), to integration (combining all sub-models into one system; Antle et al. 2001; Westervelt 2001). To decide upon an optimal approach for linking models, it is important to select between trade-offs involving model complexity versus computational time, programmer expertise and also consider copyright and ownership issues involved (Bithell and Brasington 2009). GIS technologies can also be joined to an ABM/LUCC in order to transfer spatial environmental information (Parker 2005) using either coupling or integration. Coupling involves linking a GIS to a model through a transfer of data (e.g. Zellner et al. 2009), whereas integration involves building a GIS-based ABM, such that decisions made by agents are spatially reflected using a GIS database (e.g. Robinson and Brown 2009). Coupling is the most popular of these two approaches because of the high costs associated with integration and because ‘existing GIS and emerging geodata standards are not capable of supporting three-dimensional, time-dependent processes’ (Bernard and Kruger 2000). Significant challenges are involved in the integration of GIS with dynamic spatial models, but this is an issue GIS agencies are slowly trying to address (e.g. Johnston in Press). Biophysical characteristics such as the composition and health of vegetation can be represented in ABMs/LUCC from remote sensing observations (Luus and Kelly 2008). However, Wiegand, Scmidt, Jeltsch, and Ward (2000) found that the parametrization of an ecological model was complicated by the use of remote sensing and GIS instead of field


188

K.A. Luus et al.

data because the data used determined the modelling resolution instead of the modelling resolution being set according to the scale of the system studied. Including remote sensing derived estimates of biophysical processes in ABMs/LUCC is therefore most likely to be beneficial when the ABM/LUCC is run over large regions, and when the spatial and temporal resolution of remote sensing observations is similar to the scale at which the ecological processes affecting land use change occur. GIS applications contain a variety of tools to enable resampling, scaling, interpolation and extraction of relevant data from both field and remote sensing sources to enable incorporation of spatial data into models. Restricted choice coupling of agent-based decisions into an ecological model (e.g. Matthews 2006) is adequate over short time periods and is easy to implement. If a more intensive coupling strategy were used, the resulting model could be widely applied to link any ABM with the selected biophysical models. However, implementation of a fully integrated coupling would still not necessarily result in successful integration because of the challenges present in estimating parameters from limited data, previous model runs or from another model. The added benefit of an integrated approach must be balanced by careful examination of the dynamics of the system studied in order to formulate a strategy for coping with model linkages, complexity, spatio-temporal integration and parameter estimation. 4.3. Temporal and spatial resolution matching Whether models are loosely coupled or integrated, it is important for them to be adjusted to a similar time-step to ensure compatibility. Without coinciding time-steps, the numerical stability of a model may be compromised, resulting in rapid growth of errors in model output. The implementation of a similar time-step is challenging, both because problems still exist within techniques to temporally integrate models and because models tend to be designed to work optimally at a specific spatial and temporal scale (Bithell and Brasington 2009). One approach to temporal integration that avoids the use of a predetermined timestep is to employ a discrete event simulation approach (Zeigler, Praehofer, and Kim 2000). Discrete event simulations schedule future state transitions for variables as needed within the simulation, rather than at each time-step. Another approach to temporal integration is to maintain time-step differentials, but to adjust model function so that the influence of this temporal gap is mitigated. In a combined agent-based, hydrological and IBM of forestry, model time-steps differed due to the model design and the desire to reduce computational intensity (Bithell and Brasington 2009). Whereas the forest model functioned on a 5-year time-step, the agent-based and hydrological processes occurred on a daily time-step. In this case, the gap was not judged to be highly problematic as household decisions made regarding forest products do not require daily updating regarding the quantity and location of forest products. However, a potential improvement to this model might involve evaluating the impact of this temporal gap on results. This could be accomplished either by conducting modelling experiments that evaluate the impact of the frequency of human decision-making on ecosystem function services or through social survey work to identify the best temporal resolution for different types of human decision-making. From a spatial perspective, it is important to adequately represent the spatial heterogeneities that influence decision-making without adding extraneous landscape details that increase model complexity and computation time. Sensitivity analysis and assessments of model realism may assist in the recognition of optimal spatial heterogeneity. Another beneficial technique involves resampling remotely sensed images to upscale or downscale their


189

resolution prior to incorporating these data into a model. GIS modelling can be used to integrate spatial information and process models to the spatial resolution of the scale of interest (Aspinall and Pearson 2000).


4.4. Model complexity A central concern in representing ecological processes in ABMs/LUCC using general equilibrium or IBM approaches is that the resulting model may contain a greater number of processes, parameters and functions than is optimal. Models which contain interacting sub-models may produce output that displays unrealistic non-linearity, a reduced fit against observational data or a limited number of outcomes. Interpretation of model outcomes can also be complicated by challenges in determining cause and effect when many drivers of land-use change are simultaneously occurring (Grimm et al. 2005). Complex models also have greater running times than their simpler components, which often limits the number, size or type of simulations that can be run. Greater complexity in representing a greater number of processes can also require more assumptions and more decisions to be made regarding which processes drive the observed behaviour of the system (O’Sullivan 2004). If the assumptions on which a model is built are unfounded, errors and artefacts can arise throughout the design and implementation of a model (Galan et al. 2009). Another important aspect of model complexity to consider is the directionality of feedbacks, and whether full bi-directional feedback loops or uni-directional drivers should be incorporated (Bithell and Brasington 2009). Uni-directional feedbacks are ideal for assessing the ecological impacts of land-use change when the represented ecological functions are not observable by the human system (e.g. carbon storage and the efflux of CO2 contributing to global climate change). Bi-directional feedbacks may be required when the human components of a land system are dependent on specific ecological functions that affect subsequent land-use decisions (e.g. crop choices lead to soil degradation, reduced yields and affect subsequent crop choices and management practices). Incorporating full feedbacks can greatly increase computational intensity of the model and requires additional processes (i.e. negative feedbacks or threshold responses) to be modelled so that unrealistic amplification of environmental processes does not occur. The temporal lag of modelled feedbacks must also be considered (Bithell and Brasington 2009). Caution should be exercised when incorporating a dynamic representation of ecological processes into an agent-based approach, and continual evaluation of model complexity should be conducted during model construction. Model complexity can be assessed using sensitivity tests, which address the relative contributions of individual parameters within a model. The potential for overly complex models to produce unrealistic output can be assessed by comparing model output against observational data throughout model construction, and conducting a final test of model realism against a data set not used in model calibration. In determining the optimal level of complexity, it is also important to consider the desired application of the ABM/LUCC. When the objective in designing the ABM/LUCC is to understand the potential impacts of various resource management strategies, the predictive capacity of the model should be a central focus. When an ABM/LUCC is designed for application in participatory decision-making, it is important for the processes, behaviours of interest, output metrics and level of analysis to be selected on the basis of their relevance to stakeholders. In situations where ecological processes are represented using transition rules or regressions, the resulting ABM/LUCC may remain at an optimal level of complexity. Conversely, when incorporating general equilibrium or individual-based representations


190

K.A. Luus et al.

of ecological processes into an ABM/LUCC, it may be of benefit to reduce the number of inputs, assumptions and processes in the representation of both the human system (e.g. demographics, social networks, economics, etc.) and the natural system (e.g. number of vegetation classes, hydrological processes, etc.). Aspects of the natural system in IBMs may have to be simplified by aggregating species into growth groups (Kohler and Huth 1998), aggregating individuals (Huth, Ditzer, and Bossel 1996) and using genetic algorithms to reduce the number of input parameters (Tietjen and Huth 2006). Following model simplification, it is important to assess the output of the ecological model in order to ensure it still has a reasonable fit against observational data. Following the validation of the simplified model, agents could then be incorporated into the simplified ecological model. Alternatively, the application of a pattern-oriented approach may assist in constructing an ecological ABM/LUCC at an optimal level of complexity as patternoriented models are designed using systematic evaluation of model structure and ability to reproduce patterns at multiple resolutions (Grimm et al. 2005). It may also be beneficial to begin model construction at a minimal level of complexity and to include a more complex representation of processes while undergoing continual evaluation of model performance. 4.5. Examining the realism of ABMs/LUCC A central challenge of combining ABMs/LUCC with ecological models is that the strategies used to initialize and examine the realism of the model differ, where model realism is defined as a high degree of fit between model output and observational data. Ecological models are usually validated, meaning that their performance is objectively measured to gauge whether they are adequate for their intended use (Rykiel 1996) using established validation strategies to assess fit against real-world measurements. However, validation tends to be based on assumptions of stationarity, normality and closure that are not characteristic of complex human–environmental systems (Parker et al. 2003; Millington et al. 2011), where many different outcomes can arise from the same stochastic processes and parameters (Connell et al. 2001; Aragao, Shimabukuro, Santo, and Williams 2005). Significant dialogue within the ABM/LUCC community continues regarding whether the realism of ABMs can and should be assessed (Manson 2005b), as well as how these assessments should be conducted (Kok, Farrow, Veldkamp, and Verburg 2001; Parker, Berger, Manson, and McConnell 2001; Brown, Page, Riolo, Zellner, and Rand 2005; Pontius, Jr. et al. 2006). ABMs/LUCC can be calibrated and evaluated in a participatory manner using input from community members, whose substantive contributions can provide important information about how realistic model processes and output appear, and the utility of the model to stakeholders (Millington et al. 2011). In situations where participatory evaluation is not possible, qualitative assessment mechanisms can be used to assess ABM/LUCC realism. The challenge in qualitative assessment mechanisms is that it is difficult to compare the relative accuracy of different models that have been assessed using qualitative methods. Quantitative tests of accuracy, on the other hand, tend to examine only the capacity of a model to replicate patterns. Innovative techniques are currently being developed to validate ABMs (e.g. Brown et al. 2005). Some validation efforts focus on the comparison of measurements between the model and observed data using landscape metrics (Riitters et al. 1995; SoaresFilhoa, Cerqueirab, and Pennachinc 2002) or aggregate statistics (Higgins, Richardson, and Cowling 2001; Pontius 2002). Other approaches include measuring the degree of path dependence in the model with observed data (Brown et al. 2005), visual analysis



191

(e.g. Lim et al. 2002; Castella et al. 2005) and comparing metrics at a variety of scales to determine at what resolution the model outcomes best match observed data (Kok et al. 2001; Pontius Jr. et al. 2006). Alternatively, the challenge posed by examining the realism of models with numerous parameters can be addressed by sampling numerous model runs (Li, Brimacombe, and Li 2008). ABM/LUCC model realism can be improved in certain situations through the use of an empirical basis for model development (Robinson et al. 2007), a high spatial resolution and a quantitative approach to model evaluation (Kok et al. 2001; Huete et al. 2002; Verburg et al. 2002). Another possibility commonly used is to incorporate quantitatively validated ecological models with ABMs and to examine results from the ecological-ABM/LUCC model qualitatively. However, linking an ABM/LUCC with a validated ecological model may be problematic in situations where the ABM/LUCC pushes the ecological model system outside the parameter bounds for which it has been validated. For example, an IBM of forest dynamics designed to simulate shading and competition in an old growth forest may not function well if linked with a forest harvesting ABM/LUCC if the ecological model does not simulate forest dynamics in recently clear-cut regions. When this occurs, the resulting ecologicalABM/LUCC model becomes less of a predictive tool and more of an exploratory tool, and would function mostly to underline lingering model issues and data limitations rather than simulating system dynamics. It is therefore important to assess whether the ecological model has been validated over the range of ecological conditions that may be prescribed by the ABM/LUCC before integrating these models. Another aspect of model realism to consider concerns differences in initialization strategies between models. In most cases, initial parameter values are based on available data collected through a combination of interviews, ecological fieldwork and remotely sensed data that are applied as a static representation of the landscape or ecological conditions at the initial start time of the model. However, some numerical models (such as BIOME-BGC) use a spin-up phase to set the initial conditions (Running and Hunt 1993). Within the context of terrestrial vegetation models, the spin-up simulation typically involves slowly growing vegetation until a dynamic equilibrium is met among climate, soil nutrients and plant physiological processes (e.g. Thornton et al. 2002). It may therefore be important to assess how well the initialized model set up fits against the initial stages of land characteristics expected at the beginning of an ABM/LUCC model run. The need for good modelling is central to efforts in both ecological and agent-based modelling. Frameworks such as the Transparent and Comprehensive Ecological Modelling (TRACE, by Schmolke et al. 2010) and the Overview, Design Concepts and Details (ODD, by Grimm et al. 2010) frameworks provide standardized techniques for the documentation of ecological and agent-based models. More widespread application of these frameworks in both agent-based and ecological models may enable improvements to be made in model formulations and allow lessons learned in one modelling exercise to be better applied in future models. The challenges presented by examining the realism of ABMs/LUCC or coupled ABM/LUCC and ecological models may therefore be mitigated through the use of standardized model frameworks such as ODD and TRACE, assessment of ABM/LUCC initialization according to ecological parameters and innovative techniques to assess the fit of model output against observational data. Integrating ecological and agent-based approaches to modelling land use therefore brings about challenges in model linkage, spatio-temporal integration, complexity, evaluation and representation of environmental influences on decision-making. However, the challenges posed can be largely mitigated using the aforementioned strategies.

192

K.A. Luus et al.


5. Conclusions Patterns of land use are determined by a combination of historical decisions, locations of anthropogenic activities, current socio-economic drivers, environmental characteristics and ecosystem resilience (Lambin et al. 2001). It is important to include environmental processes in ABMs/LUCC because human decision-making occurs both as a reaction to past land-use and in anticipation of maximizing the future value of a given parcel of land (e.g. by implementing a fallow or limiting the extent of deforestation). As a result, the utility of certain ABMs/LUCC could be improved by explicitly representing the dynamic ecological processes and conditions through which land-use decisions are made. This article has provided a framework through which an optimal strategy for representing ecosystem dynamics can be selected, followed by a review of successful strategies for overcoming the main challenges presented by integrating a dynamic representation of ecosystem processes into an ABM/LUCC. It is beneficial to represent the natural environment by setting all environmental characteristics (other than land use) to remain unchanged during a model run when (1) research questions can be answered using spatially aggregated data; (2) environmental processes alter landscape dynamics over much longer time periods than the temporal extents of the model or (3) observational data are too limited to enable processes to be represented dynamically. In remaining situations, the utility of a model is likely to be improved by using a dynamic representation of ecosystem processes. In selecting a dynamic approach for representing ecosystem processes, it is important to consider the model scale, research question and ecosystem studied. Representing ecosystem processes using transitions may be most useful in situations where inadequate data exist to use a linear regression approach, or when the ecosystem displays substantial resilience, such that degradation or changes in ecosystem function over long time periods is minimal. Regression-based approaches are most likely to be useful when transitions over time are linear and when field data exist by which to establish variable values. Spatial interactions and influences of neighbouring cells can also be represented using a regression-based approach; however, non-linear shifts in system dynamics following degradation may not be well represented using a regression-based approach. Transition and regression-based approaches are most likely to be useful when household decisions are not greatly influenced by species composition or biogeochemical stores/fluxes, when research questions are relatively general or when the representation of human decisionmaking in the ABM/LUCC is complex so as to limit the complexity of the resulting model. IBM approaches are most useful for representing the differential response of agent decisions depending on species compositions, addressing species-specific questions or examining the very long-term effects of land-use change on ecosystem dynamics. General equilibrium models (GEMs) constructed of a biogeochemical model and an agent-based model can simulate the influence of land-use change policies on the provision of ecosystem services, biogeochemical soil stores of carbon and nitrogen and atmospheric fluxes of greenhouse gases. These models can also be used to examine long-term adaptation to policies related to soil or atmospheric dynamics, as well as the long-term changes in human decisions with changes in the provision of ecosystem services. When selecting an appropriate strategy for representing the environment and its influence on human decisionmaking, it is important to assess both the research question being addressed and the desired application of the resulting model as described in the following section. ABMs/LUCC applied to understand the potential impacts of resource management strategies are most likely to benefit from the inclusion of an explicit representation of



193

dynamic ecological processes. ABMs/LUCC that are used to assess resource management strategies could therefore incorporate either transition, regression-based, individualbased or GEM approaches in representing dynamic ecological processes. Participatory ABMs/LUCC are likely to benefit from the inclusion of dynamic ecosystem processes as long as there are strategies in place to encourage stakeholder evaluation of model outcomes and limit computational cost and complexity of the strategies used to represent ecosystem processes. ABMs/LUCC that are primarily used to generate thought experiments are not likely to benefit from the inclusion of a dynamic representation of ecological processes. Efforts to represent ecological processes more dynamically in ABMs/LUCC should be made cautiously in order to mitigate the influence of challenges in coupling models, limiting model complexity, adjusting the spatial and temporal extents, representing ecosystem services and assessing the resulting fit of model output against observed data. Ecological ABMs/LUCC possess great potential to assist researchers in understanding the cumulative long-term impacts of anthropogenic land-use change, and in designing appropriate adaptations to the most pressing environmental dilemmas of our time, from climate change to loss of biodiversity. Furthermore, these models may enable investigation of predicted responses of households and ecosystems to a variety of disturbances and predicted impacts from climate change, such as rises in sea level, changes in flooding frequency and warming temperatures. Acknowledgements The authors would like to thank Editor in Chief Professor Aspinall and the anonymous reviewers for their insightful comments. This research was supported by a Vanier Canada Graduate Scholarship (K.A. Luus) from the Natural Sciences and Engineering Research Council of Canada (NSERC).

References Abia, L., Angulo, O., and Lopez-Marcos, J. (2005), “Age-Structured Population Models and Their Numerical Solution”, Ecological Modelling, 188, 112–136. Antle, J.M., Capalbo, S.M., Elliott, E.T., Hunt, H.W., Mooney, S., and Paustian, K.H. (2001), “Research Needs for Understanding and Predicting the Behavior of Managed Ecosystems: Lessons from the Study of Agroecosystems”, Ecosystems, 4(8), 723–735. Aragao, L.E.O., Shimabukuro, Y.E., Santo, F.D.E., and Williams, M. (2005), “Landscape Pattern and Spatial Variability of Leaf Area Index in Eastern Amazonia”, Forest Ecology and Management, 211, 240–256. Aspinall, R., and Pearson, D. (2000), “Integrated Geographical Assessment of Environmental Condition in Water Catchments: Linking Landscape Ecology, Environmental Modelling and GIS”, Journal of Environmental Management, 59, 299–319. Bah, A., Toure, I., Page, C.L., Ickowicz, A., and Diop, A. (2006), “An Agent-Based Model to Understand the Multiple Uses of Land and Resources Around Drillings in the Sahel”, Mathematical and Computer Modelling, 44, 513–534. Bakker, M.M., Govers, G., Kosmas, C., Vanacker, V., Oost, K., and Rounsevell, M. (2005), “Soil Erosion as a Driver of Land-Use Change”, Agriculture, Ecosystems and Environment, 105(3), 467–481. Becu, N., Neel, A., Schreinemachers, P., and Sangkapitux, C. (2008), “Participatory Computer Simulation to Support Collective Decision-Making: Potential and Limits of Stakeholder Involvement”, Land Use Policy, 25(4), 498–509. Berger, T. (2001), “Agent-Based Models Applied to Agriculture: A Simulation Tool for Technology Diffusion, Resource Use Changes and Policy Analysis”, Agricultural Economics, 25(2/3), 245–260. Bernard, L., and Kruger, T. (2000), “Integrations of GIS and Spatio-Temporal Simulation Models: Interoperable Components for Different Simulation Strategies”, Transactions in GIS, 4(3), 197–215.


194

K.A. Luus et al.

Bithell, M., and Brasington, J. (2009), “Coupling Agent-Based Models of Subsistence Farming with Individual-Based Forest Models and Dynamic Models of Water Distribution”, Environmental Modelling & Software, 24(2), 173–190. Bithell, M., Brasington, J., and Richards, K. (2008), “Discrete-Element, Individual-Based and AgentBased Models: Tools for Interdisciplinary Enquiry in Geography?” Geoforum, 39(2), 625–642. Brown, D.G., Page, S., Riolo, R., Zellner, M., and Rand, W. (2005), “Path Dependence and the Validation of Agent-Based Spatial Models of Land Use”, International Journal of Geographic Information Science, 19(2), 153–174. Brown, D.G., Pijanowski, B.C., and Duh, J.D. (2000), “Modelling the Relationships Between Land Use and Land Cover on Private Landsat in the Upper Midwest, USA”, Journal of Environmental Management, 59(4), 247–263. Caplat, P., Lepart, J., and Marty, P. (2006), “Landscape Patterns and Agriculture: Modelling the Long-Term Effects of Human Practices on Pinus Sylvestris Spatial Dynamics (Causse Mejean, France)”, Landscape Ecology, 21, 657–670. Castella, J., Trung, T., and Boissau, S. (2005), “Participatory Simulation 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”, Ecology and Society, 10(1), 27. Cloern, J.E. (2001), “Our Evolving Conceptual Model of the Coastal Eutrophication Problem”, Marine Ecology Progress Series, 210, 223–253. Connell, W.J., Parker, D.C., Berger, T., and Manson, S.M. (eds.) (2001), Agent-Based Models of Land-Use and Land-Cover Change: Report and Review of an International Workshop, Vol. LUCC Report Series 6, Irvine, CA: LUCC Focus 1 Office, Anthropological Centre for Training and Research on Global Environmental Change, Indiana University. Deadman, P., Robinson, D., Moran, E., and Brondizio, E. (2004), “Colonist Household Decision Making and Land Use Change in the Amazon Rainforest: An Agent Based Simulation”, Environment and Planning B: Planning and Design, 31(5), 693–709. Dean, J.S., Gumerman, G.J., Epstein, J.M., Axtell, R.L., Swedlund, A.C., Parker, M.T., and McCarroll, S. (2000), “Understanding Anasazi Culture Change Through Agent-Based Modelling”, Dynamics in Human and Primate Societies: Agent-Based Modelling of Social and Spatial Processes, eds. Timothy A. Kohler and George J. Gumerman, New York: Oxford University Press, pp. 179–205. Dray, S., Legendre, P., and Peres-Neto, P.R. (2006), “Spatial Modelling: A Comprehensive Framework for Principal Coordinate Analysis of Neighbour Matrices (PCNM)”, Ecological Modelling, 196(3–4), 483–493. Dunne, T. (1979), “Sediment Yield and Land Use in Tropical Catchments”, Journal of Hydrology, 42(3–4), 281–300. Eichner, T., and Pethig, R. (2005), “Ecosystem and Economy: An Integrated Dynamic General Equilibrium Approach”, Journal of Economics, 85(3), 213–249. El-Raey, M., Dewidar, K.R., and El-Hattab, M. (1999), “Adaptation to the Impacts of Sea Level Rise in Egypt”, Mitigation and Adaptation Strategies for Global Change, 4, 343–361. Epstein, J.M., and Axtell, R. (1996), Growing Artificial Societies: Social Science From the Bottom up, Cambridge, MA: MIT Press. ———. (1997), “Artificial Societies and Generative Social Science”, Artificial Life and Robotics, 1, 33–34. Evans, T.P., and Kelley, H. (2004), “Multi-Scale Analysis of a Household Level Agent-Based Model of Landcover Change”, Journal of Environmental Management, 72, 57–72. Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A., Kucharik, C.J., Monfreda, C., Patz, J.A., Prentice, C., Ramankutty, N., and Snyder, P.K. (2005), “Global Consequences of Land Use”, Science, 309(5734), 570–574. Foster, D.R., Motzkin, G., and Slater, B. (1998), “Land-Use History as Long-Term Broad-Scale Disturbance: Regional Forest Dynamics in Central New England”, Ecosystems, 1(1), 96–119. Fotheringham, A.S., Brunsdon, C., and Charlton, M. (2002), Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Hoboken, NJ: Wiley. Galan, J.M., Izquierdo, L.R., Izquierdo, S.S., Santos, J.I., Del Olmo, R., Lopez-Paredes, A., and Edmonds, B. (2009), “Errors and Artefacts in Agent-Based Modelling”, Journal of Artificial Societies and Social Simulation, 12(11), 1. Available at http://jasss.soc.surrey.ac.uk/12/1/1.html.



195

Gaube, V., Kaiser, C., Wildenberg, M., Adensam, H., Fleissner, P., Kobler, J., Lutz, J., Schaumberger, A., Schaumburger, J., Smetschka, B., Wolf, A., Richter, A., and Haberl, H. (2009), “Combining Agent-Based and Stock-Flow Modelling Approaches in a Participative Analysis of the Integrated System in Reichraming, Austria”, Landscape Ecology, 24, 1149–1165. Grimm, V., Berger, U., DeAngelis, D.L., Polhill, J.G., Giske, J., and Railsback, S.F. (2010), “The ODD Protocol: A Review and First Update”, Ecological Modelling, 221(23), 2760–2768. Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W.M., Railsback, S.F., Thulke, H.-H., Weiner, J., Weigand, T., and DeAngelis, D.L. (2005), “Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons From Ecology”, Science, 310(5750), 987–991. Gross, D., and Strand, R. (2000), “Can Agent-Based Models Assist Decisions on Large-Scale Practical Problems? A Philosophical Analysis”, Complexity, 5, 26–33. Hermy, M., and Verheyen, K. (2007), “Legacies of the Past in the Present-Day Forest Biodiversity: A Review of Past Land-Use Effects on Forest Plant Species Composition and Diversity”, Ecological Research, 22(3), 361–371. Higgins, S.I., Richardson, D.M., and Cowling, R.M. (2001), “Validation of a Spatial Simulation Model of a Spreading Alien Plant Population”, Journal of Applied Ecology, 38(3), 571–584. Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X., and Ferreira, L. (2002), “Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices”, Remote Sensing of Environment, 83, 195–213. Huigen, M.G. (2004), “First Principles of the MAmeLUKE Multi-Actor Modelling Framework for Land Use Change, Illustrated with a Philippine Case Study”, Journal of Environmental Management, 72, 5–21. Huth, A., Ditzer, T., and Bossel, H. (1996), “Simulation of the Growth of Tropical Rain Forests: Formix 3”, Technical Report P9602, University of Kassel, Environmental Systems Research Centre. Iverson, R. (2000), “How Should Mathematical Models of Geomorphic Processes Be Judged?” in Prediction in Geomorphology, eds. P. Wilcock and R. Iverson, Washington, DC: American Geophysical Union, pp. 83–94. Johnston, K. (in press), Agent-Based Modelling in ArcGIS, ESRI Press. Kohler, P., and Huth, A. (1998), “An Individual Based Rain Forest Model- Concepts and Simulation Results”, in Individual-Based Structural and Functional Models in Ecology (Vol. 52), eds. A. Kastner-Maresch, W. Kurth, M. Sonntag and B. Breckling, Bayreuth: Bayreuther Forum Okologie, pp. 1–18. Kok, K., Farrow, A., Veldkamp, T.A., and Verburg, P.H. (2001), “A Method and Application of Multi-Scale Validation in Spatial Land Use Models”, Agriculture, Ecosystems, & Environment, 85(1–3), 223–238. Lambin, E.F., Turner, B.L., Geist, H.J., Agbola, S.B., Angelsen, A., Bruce, J.W., Coomes, O.T., Dirzgo, R., Fischer, G., Folke, C., George, P.S., Homewood, K., Imbernon, J., Leemans, R., Li, X., Moran, E.F., Mortimore, M., Ramakrishnan, P.S., Richards, J.F., Skanes, H., Steffen, W., Stone, G.D., Svedin, U., Veldkamp, T.A., Vogel, C., and Xu, J. (2001), “The Causes of Land-Use and Land-Cover Change: Moving Beyond the Myths”, Global Environmental Change, 11(4), 261–269. Lammert, M., and Allan, J.D. (1999), “Assessing Biotic Integrity of Streams: Effects of Scale in Measuring the Influence of Land Use/Cover and Habitat Structure on Fish and Macroinvertebrates”, Environmental Management, 23(2), 257–270. Lancaster, S.T., and Grant, G.E. (2003), “You Want Me to Predict What?” in Prediction in Geomorphology (Vol. 135), eds. P.R. Wilcock and R.M. Iverson, Washington, DC: American Geophysical Union. Lane, S.N., Odoni, N., Landstrom, C., Whatmore, S.J., Ward, N., and Bradley, S. (2011), “Doing Flood Risk Science Differently: An Experiment in Radical Scientific Method”, Transactions of the Institute of British Geographers, 36(1), 15–36. Li, Y., Brimacombe, A.J., and Li, C. (2008), “Agent-Based Services for the Validation and Calibration of Multi-Agent Models”, Computers, Environment and Urban Systems, 32(6), 464–473. Lim, K., Deadman, P., Moran, E., Brondizio, E., and McCracken, S. (2002), “Agent-Based Simulations of Household Decision Making and Land Use Change Near Altamira, Brazil”, in Integrating Geographic Information Systems and Agent-Based Modelling Techniques for


196

K.A. Luus et al.

Simulating Social and Ecological Processes, ed. H.R. Gimblett, Santa Fe Institute Studies in the Sciences of Complexity, Oxford: Oxford University Press, pp. 277–310. Liu, J., Dietz, T., Carpenter, S.R., Alberti, M., Folke, C., Moran, E., Pell, A.N., Deadman, P., Kratz, T., Lubchenco, J., Ostrom, E., Ouyang, Z., Provencher, W., Redman, C.L., Schneider, S.H., and Taylor, W.W. (2007), “Complexity of Coupled Human and Natural Systems”, Science, 317(5844), 1513. Luus, K.A., and Kelly, R.E.J. (2008), “Assessing Productivity of Vegetation in the Amazon Using Remote Sensing and Modelling”, Progress in Physical Geography, 32(4), 363–377. Manson, S.M. (2005a), “Agent Based Modelling and Genetic Programming for Modelling Land Change in the Southern Yucatan Peninsular Region of Mexico”, Agriculture, Ecosystems and Environment, 111, 47–62. ———. (2005b), “Validation and Verification of Multi-Agent Systems”, in Complexity and Ecosystem Management: The Theory and Practice of Multi-Agent Systems, ed. M.A. Janssen, Northampton, MA: Edward Elgar, pp. 63–74. Matthews, R. (2006), “The People and Landscape Model (PALM): Towards Full Integration of Human Decision-Making and Biophysical Simulation Models”, Ecological Modelling, 194(4), 329–343. Matthews, R.B., Gilbert, N.G., Roach, A., Ponthill, J.G., and Gotts, N.M. (2007), “Agent-Based Land-Use Models: A Review of Applications”, Landscape Ecology, 22(10), 1447–1459. Millenium Ecosystem Assessment (2005), Chapter 1: MA Conceptual Framework, Ecosystems and Human Well-Being: Synthesis. Volume 1: Current State & Trends, Washington, DC: Island Press. Millington, J.D.A., Demeritt, D., and Romero-Calcerrada, R. (2011), “Participatory Evaluation of Agent-Based Land Use Models”, Journal of Land Use Science, 6(2), 195–210. Nordhaus, W.D., and Yang, Z. (1996), “A Regional Dynamic General-Equilibrium Model of Alternative Climate-Change Strategies”, The American Economic Review, 86(4), 741–765. Nuttle, T., and Haefner, J.W. (2007), “Design and Validation of a Spatially Explicit Simulation Model for Bottomland Hardwood Forests”, Ecological Modelling, 200(1–2), 20–32. Ogneva-Himmelberger, Y., Pearsall, H., and Rakshit, R. (2009), “Concrete Evidence & Geographically Weighted Regression: A Regional Analysis of Wealth and Land Cover in Massachusetts”, Applied Geography, 29(4), 478–487. O’Sullivan, S. (2004), “Complexity Science and Human Geography”, Transactions of the Institute of British Geographers, 29(3), 282–295. Parker, D.C. (2005), “Integration of Geographic Information Systems and Agent-Based Models of Land Use: Prospects and Challenges”, in GIS, Spatial Analysis and Modelling, eds. D.J. Maguire, M. Batty and M.F. Goodchild, Redlands, CA: ESRI Press, pp. 403–422. Parker, D.C., Berger, T., Manson, S.M., and McConnell, W.J. (eds.) (2001), Meeting the Challenge of Complexity: Proceedings of a Special Workshop on Land-Use/Land-Cover Change (Vol. CCR-3), Santa Barbara, CA: CIPEC. Parker, D.C., Hessl, A., and Davis, S.C. (2008), “Complexity, Land-Use Modelling, and the Human Dimension: Fundamental Challenges for Mapping Unknown Outcome Spaces”, Geoforum, 39(2), 789–804. Parker, D.C., Manson, S.M., Janssen, M.A., Hoffman, M.J., and Deadman, P. (2003), “Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review”, Annals of the Association of American Geographers, 93, 314–337. Parton, W.J., Schimel, D.S., Cole, C.V., and Ojima, D.S. (1987), “Analysis of Factors Controlling Soil Organic Matter Levels in Great Plains Grasslands”, Soil Science Society of America Journal, 51, 1173–1179. Pielke, R.A. (2005), “Land Use and Climate Change”, Science, 310(5754), 1625–1626. Pontius, R. (2002), “Statistical Methods to Partition Effects of Quantity and Location During Comparison of Categorical Maps at Multiple Resolutions”, Photogrammetric Engineering & Remote Sensing, 68, 1041–1049. Pontius, Jr., R.G., Boersma, W., Castella, J.-C., Clarke, K., de Nijs, T., Dietzel, C., Duan, Z., Fotsing, E., Goldstein, N., Kok, K., Koomen, E., Lippitt, C.D., McConnell, W., Sood, A.M., Pijanowski, B., Pithadia, S., Sweeney, S., Trung, T.N., Veldkamp, A.T., and, Verburg, P.H., (2006), “Comparing the Input, Output and Validation Maps for Several Models of Land Change”, Annals of Regional Science, 42(1), 1–50. Riitters, K.H., O’Neill, R.V., Hunsaker, C.T., Wickham, J.D., Yankee, D.H., Timminsa, S.P., Jones, K.B., and Jackson, B.L. (1995), “A Factor Analysis of Landscape Pattern and Structure Metrics”, Landscape Ecology, 10(1), 23–29.



197

Robinson, D.T., and Brown, D.G. (2009), “Evaluating the Effects of Land-Use Development Policies 1110 on Ex-Urban Forest Cover: An Integrated Agent-Based GIS Approach”, International Journal of Geographical Information Science, 23(9), 1211–1232. Robinson, D.T., Brown, D.G., and Currie, W.S. (2009), “Modelling Carbon Storage in Highly Fragmented and Human-Dominated Landscapes: Linking Land-Cover Patterns and Ecosystem Models”, Ecological Modelling, 220(9–10), 1325–1338. Robinson, D.T., Brown, D.G., Parker, D.C., Schreinemachers, P., Janssen, M.A., Huigen, M., Wittmer, H., Gotts, N., Promburom, P., Irwin, E., Berger, T., Gatzweiler, F., and Barnaud, C. (2007), “Comparison of Empirical Methods for Building Agent-Based Models in Land Use Science”, Journal of Land Use Science, 2(1), 31–55. Running, S.W., and Hunt, R.E. (1993), Generalization of a Forest Ecosystem Process Model for Global Scale Models. Scaling Physiologic Processes: Leaf to Globe. Eds: J.R. Ehleringer and C.B. Field. San Diego, CA: Academic Press, pp. 141–158. Rykiel, E.J. (1996), “Testing Ecological Models: The Meaning of Validation”, Ecological Modelling, 90(3), 229–244. Satake, A., Leslie, H.M., Iwasa, Y., and Levin, S.A. (2007), “Coupled Ecological Social Dynamics in a Forested Landscape: Spatial Interactions and Information Flow”, Journal of Theoretical Biology, 246, 695–707. Schmolke, A., Thorbek, P., DeAngelis, D.L., and Grimm, V. (2010), “Ecological Models Supporting Environmental Decision Making: A Strategy for the Future”, Trends in Ecology and Evolution, 25(8), 479–486. Shugart, H.H., and Smith, T.M. (1996), “A Review of Forest Patch Models and Their Application to Global Change Research”, Climatic Change, 34(2), 131–153. Soares-Filhoa, B.S., Cerqueirab, G.C., and Pennachinc, C.L. (2002), “DINAMICA – a Stochastic Cellular Automata Model Designed to Simulate the Landscape Dynamics in an Amazonian Colonization Frontier”, Ecological Modelling, 154(3), 217–235. Souchère, V., Millair, L., Echeverria, J., Bousquet, F., Le Page, C., and Etienne, M. (2010), “CoConstructing with Stakeholders a Role-Playing Game to Initiate Collective Management of Erosive Runoff Risks at the Watershed Scale”, Environmental Modelling & Software, 25, 1359–1370. Tesfatsion, L. (2002), “Agent-Based Computational Economics: Growing Economies From the Bottom up”, Artificial Life, 8(1), 55–82. Theobald, D.M., and Hobbs, N.T. (1998), “Forecasting Rural Land-Use Change: A Comparison of Regression- and Spatial Transition-Based Models”, Geographical & Environmental Modelling, 2(1), 65–82. Thornton, P.E., Law, B.E., Gholz, H.L., Clark, K.L., Falge, E., Ellsworth, D.S., Goldstein, A.H., Monson, R.K., Hollinger, D., Falk, M., Chen, J., and Sparks, J.P. (2002), “Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests”, Agricultural and Forest Meteorology, 113, 185–222. Tietjen, B., and Huth, A. (2006), “Modelling Dynamics of Managed Tropical Rainforets – an Aggregated Approach”, Ecological Modelling, 199(4), 421–432. Tu, J., and Xia, Z.-G. (2008), “Examining Spatially Varying Relationships Between Land Use and Water Quality Using Geographically Weighted Regression I: Model Design and Evaluation”, Science of the Total Environment, 407(1), 358–378. Veldkamp, A., and Verburg, P. (2004), “Modelling Land Use Change and Environmental Impact”, Journal of Environmental Management, 72(1–2), 1–3. Verburg, P., de Nijs, T.C.M., van Eck, J.R., Visser, H., and De Jong, K. (2004a), “A Method to Analyse Neighbourhood Characteristics of Land Use Patterns”, Computers, Environment and Urban Systems, 28(6), 667–690. Verburg, P., Veldkamp, A., Willemen, L., Overmars, K., and Castella, J. (2004b), “Landscape Level Analysis of the Spatial and Temporal Complexity of Land-Use Change”, in Ecosystems and Land Use, Geophysical Monograph Series (Vol. 153), eds. R.S. DeFries, G.P. Asner, and R.A. Houghton, Washington, DC: American Geophysical Union, pp. 217–230. Verburg, P.H., and Overmars, K.P. (2009), “Combining Top-Down and Bottom-up Dynamics in Land Use Modelling: Exploring the Future of Abandoned Farmlands in Europe with the Dyna-CLUE Model”, Landscape Ecology, 24, 1167–1181. Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., and Mastura, S.S. (2002), “Modelling the Spatial Dynamics of Regional Land Use: The CLUE-S Model”, Environmental Management, 30(3), 391–405.


198

K.A. Luus et al.

Waldrop, M.M. (1990), “Asking for the Moon”, Science 247(4943), 637–638. Watson, A.J., and Lovelock, J.E. (1983), “Biological Homeostasis of the Global Environment: The Parable of Daisyworld”, Tellus B, 35(4), 284–289. Wehrli, A., Weisberg, P.J., Schnenberger, W., Brang, P., and Bugmann, H. (2007), “Improving the Establishment Submodel of a Forest Patch Model to Assess the Long-Term Protective Effect of Mountain Forests”, European Journal of Forest Research, 126(1), 131–145. Westervelt, J.D. (2001), “Geographic Information Systems and Agent-Based Modelling”, in Integrating Geographic Information Systems and Agent-Based Modelling Techniques, ed. H.R. Gimbett, New York: Oxford University Press, pp. 83–104. Wiegand, K., Scmidt, H., Jeltsch, F., and Ward, D. (2000), “Linking a Spatially-Explicit Model of Acacias to GIS and Remotely-Sensed Images”, Folio Geobotanica, 35(2), 211–230. Yang, D., Kanae, S., Oki, T., Koike, T., and Musiake, K. (2003), “Global Potential Soil Erosion with Reference to Land Use and Climate Changes”, Hydrological Processes, 17(14), 2913–2928. Zavala, M., Angulo, O., de la Parra, R.B., and Lopez-Marcos, J. (2006), “An Analytical Model of Stand Dynamics as a Function of Tree Growth, Mortality and Recruitment: The Shade ToleranceStand Structure Hypothesis Revisited”, Journal of Theoretical Biology, 244(3), 440–450. Zeigler, B.P., H. Praehofer, and T.G. Kim (2000), Theory of Modelling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems (Vol. 2), San Diego, CA: Academic Press, p. 510. Zellner, M., Page, S., Rand, W., Brown, D., Robinson, D., Nassauer, J., and Low, B. (2009), “The Emergence of Zoning Policy Games in Exurban Jurisdictions: Informing Collective Action Theory”, Land Use Policy, 26(2), 356–367.

Representing ecological processes in agent-based ...

Representing ecological processes in agent-based ...

Suggest Documents

Chapter 7: Representing Decision Processes

Provisional PDF - Ecological Processes

View PDF - Ecological Processes

Representing Temporal, Geographical, and Ecological Diversity

Representing Real-World Processes - Semantic Scholar

Understanding Ecological and Biophysical Processes in ... - WetlandInfo

Understanding Ecological and Biophysical Processes in ... - WetlandInfo

Key Ecological Processes in Kimberley Benthic

Understanding Ecological and Biophysical Processes in ... - WetlandInfo

Representing Spatiotemporal Processes to Support Knowledge ...

Soil Biodiversity, Ecological Processes

Re-representing and abstracting as creative processes in the design

Ecological Modelling Non-linear ecological processes ... - Google Sites

Institutional constraints and ecological processes - IngentaConnect

Representing the Dimensions of an Ecological Niche - CiteSeerX

Ecological Processes and Contemporary Coral Reef ... - MDPI

Marine Ecological Processes - School of Forest Resources ...

Nutrient Addition Differentially Affects Ecological Processes of ...

Non-linear ecological processes, fires, environmental heterogeneity ...

Ecological Modelling Eliciting cognitive processes ...

Agentbased modeling forex anteassessment of tree ...

Mixing processes in lakes: mechanisms and ecological ... - Springer Link

Effects of season on ecological processes in extensive ... - SciELO

Effective discharge analysis of ecological processes in streams