Can modelling enable us to understand the rôle of humans in landscape evolution?

Available online at www.sciencedirect.com

www.elsevier.com/locate/geoforum

Geoforum 39 (2008) 659–674

Can modelling enable us to understand the role of humansin landscape evolution?

John Wainwright

Department of Geography, Sheffield Centre for International Drylands Research, University of Sheffield, Winter Street, Sheffield, S10 2TN, UK

Received 17 June 2005; received in revised form 29 August 2006

Abstract

Landform-evolution models have typically failed to include human actions, or have done so only in a static, scenario-based way. Thisfailure is despite the extensive empirical data that suggest rates of soil erosion are most sensitive to anthropic pressure. The CYBEROSION

modelling framework overcomes this limitation by using an agent-based approach to simulating the dynamic interactions of people andtheir landscapes. The interactions simulated relate to basic processes of food acquisition (hunting, gathering and basic agriculture) inprehistoric communities. Simulations demonstrate the value of this approach in supporting the vulnerability of landform evolution toanthropic pressures, and demonstrate the limitations of existing models that ignore human and animal agency, which are likely to pro-duce both quantitatively and qualitatively different results. The model is also a useful heuristic tool for understanding human–landscapeinteractions and for suggesting directions for future research. Despite the acknowledged limitations of agent-based approaches in sim-ulating human populations, it is suggested that further research will be fruitful, especially if combined with a range of field evidence.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Modelling; Geomorphology; Erosion; Population change; Landform evolution; Agent-based models; Self-organization

1. Introduction

Anthropic impacts on soil erosion and the consequentlandscape evolution have been long appreciated. For exam-ple, following the Dust Bowl of the 1930s in the UnitedStates, the work of the Soil Conservation Service and thenthe Agricultural Research Service focussed on the impactsof erosion, and in particular on means to define the impactsof different land-use practices. This work resulted in thedevelopment of the so-called ‘‘Universal’’ Soil-Loss Equa-tion (USLE) (Wischmeier and Smith, 1978; Laflen andMoldenhauer, 2003). In a Mediterranean context, theresults of Kosmas et al. (1997) demonstrate that undersemi-natural, matorral vegetation, plot-based estimates oferosion rates vary from 7 to 17 t km�2 a�1, across a rangeof annual precipitation regimes of 150–550 mm. In contrast,changes of land use could both reduce rates, in the case of

0016-7185/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.geoforum.2006.09.011

E-mail address: [email protected]

olives, but also substantially increase them, as in the caseof wheat (18 t km�2 a�1) and vines (143 t km�2 a�1). Gen-erally, it can be considered that erosion rates are moresensitive to land-use change than to climate change orvariability.

Analyses of historical erosion patterns have also increas-ingly tended to shift from climatic (e.g. Vita-Finzi, 1969) tohuman-induced explanations (e.g. Butzer, 1974), or indeedto the complex interplay between the two sets of factors.Van Andel and Runnels (1987) demonstrated the relation-ship between population pressure or the development ofunsuitable land-management practice and accelerated ero-sion in the Argolid in Southern Greece. Subsequent analy-ses throughout Greece suggested phases of acceleratederosion coinciding with initial phases of agriculture ormore intense agriculture in the Bronze Age (Van Andelet al., 1990). Wainwright (2000) also suggested that theagricultural technology developments of the Bronze Agein Western Mediterranean Europe were responsible for awidespread, but highly localized pattern of erosion. This

mailto:[email protected]

660 J. Wainwright / Geoforum 39 (2008) 659–674

analysis was extended by Wainwright and Thornes (2003)and Wainwright (2004) for the Mediterranean as a whole,leading to the suggestion that local conditions and the pasthistory of human interactions with the landscape are fun-damental in understanding the presence or absence ofaccelerated erosion in any particular location. At a morelocal scale, ongoing investigations around the archaeologi-cal site of Roucadour (Lot, France) have suggested thaterosion rates have varied over several orders of magnitudesince the Middle Neolithic as a result of complex changingpatterns of human land-use (Fig. 1; Gasco et al., 2004;Wainwright et al., 2006).

Spatial and temporal variability in the anthropicimpacts on erosion rates should thus be seen as the norm.This variability should not be considered surprising giventhe nature of pre-industrialized agriculture, and especiallyin landscapes such as the Mediterranean where the topog-raphy tends to enhance the spatial fragmentation of landuse. However, because of the often highly partial natureof the data – erosion, by definition, tends to destroy theevidence of its occurrence – there is still a tendency toovergeneralize from a limited amount of field evidence.The aim of this paper is to investigate whether modellingof the interaction between people and their landscape canbe used to investigate the gaps in this evidence, and atthe same time to test hypotheses of past land use in partic-ular settings, especially those in which historical data arelacking.

Fig. 1. Evidence for highly variable patterns of erosion and deposition arou(Gasco et al., 2004; Gernigon et al., 2000; and unpublished field data), su(Wainwright et al., 2006). Values in metres represent the maximum depth at whthus show sedimentation since the acceleration of human activity in the area.

2. Modelling approaches to landform evolution

The earliest approaches to the mathematical modellingof landform evolution were limited to analytical assess-ments of typical or characteristic slope forms under varioussuites of process (e.g. Strahler, 1952; Scheidegger, 1961,1970; Ahnert, 1970; Kirkby, 1971). Subsequent advancesin technology allowed the numerical simulation of slopeprofiles and topographic evolution (e.g. Smith and Brether-ton, 1972; Ahnert, 1976, 1987; Kirkby, 1986) and completetopography–soil landscapes (Huggett, 1975). A combina-tion of difficulties in testing these models, a shift in geomor-phological focus to generally shorter term process studiesand technological considerations probably led to a lull inthis sort of approach within the discipline. A more recentrenaissance has been due to the development of techniquesfor landscape evolution (e.g. Cockburn and Summerfield,2004); an increasing realization that geomorphology needsto integrate both shorter and longer term processes, oftenreflecting a more general recognition of the importance ofscale as a framing device (e.g. Rodriguez-Iturbe andRinaldo, 1997); and the ever-accelerating pace of com-puter-technology development (e.g. Voller and Porte-Agel,2002). A parallel development from a more geological per-spective – often building on but seemingly unaware of theprevious body of literature (but see Kooi and Beaumont,1996) – has also begun to investigate the links between tec-tonics, climate and landform evolution.

nd the site of Roucadour, Lot, France based on archaeological evidencerface surveys and coring of sediments deposited in the base of dolinesich charcoal is found in augur cores through the sediments in dolines andValues in mm a�1 are reconstructed sedimentation rates.

J. Wainwright / Geoforum 39 (2008) 659–674 661

Despite the increasing complexity of these approaches,the development of new techniques such as cellular models(e.g. Coulthard et al., 2002; Tucker and Slingerland, 1997),and the developing body of knowledge on the impacts ofhuman activity both empirically and within a modellingframework (indeed since the USLE), few attempts havebeen made to link human activity to landscape evolutionexplicitly. Wainwright (1994a) used a two-dimensionalslope-evolution model to look at conditions of survival ofhypothetical archaeological sites and applied the sameapproach to investigating conditions of land degradationin the Bronze Age in Southern France (Wainwright,1994b). In the latter study, a range of scenarios wasemployed to evaluate the most likely conditions ofobserved erosion and deposition patterns. Favis-Mortlocket al. (1997) used the EPIC model to estimate changes oferosion rates (but not landform evolution) since 7000 bpbased on scenarios of land-use change derived from archae-ological and historical data. Coulthard and Macklin (2001,2003) derived scenarios for land-cover (and implicitly,land-use) change from pollen proxies in their simulationsof evolution of the rivers Swale, Ure, Nidd and Wharfeover a 9000-year period. Similar approaches have beenused to define changes in catchment behaviour since the16th Century in Russia (Sidorchuk et al., 2003) as part ofthe LUCIFS (Land Use and Climate Impacts on FluvialSystems during the Period of Agriculture) research pro-gramme of PAGES (2005). The latter programme demon-strates an increasing recognition for the integration ofhuman impacts in this type of modelling.

Despite this recognition, there is still little evidence ofmuch progress in this area. The approaches thus far seemto be limited to the development of scenarios of varyingcomplexity based on evidence that is often internal to thesystem being modelled. There is thus a potential problemof circularity of logic in developing explanations ofhuman-induced landform change. Furthermore, such sce-narios are static representations of the complex interrela-tionship between human activity and landform evolution,which potentially fail to take into account dynamic interac-tions and key feedbacks. In the worst case, they can lead tothe simulation of totally unrealistic conditions (e.g. thecontinuation of farming once the total soil thickness hasbeen lost from an area). As noted above, the interrelation-ships are generally complex in space and in time, whereasthe scenario-based approach is often applied quite broadlyover large spatial extents (and often remaining staticthrough long periods of time). One approach that has beencommonly used in environmental modelling as a whole totry to account for such dynamic interactions is the use ofagent-based models (see the review in Wainwright andMulligan, 2003). Agent-based models employ a numberof autonomous computational objects to represent dynam-ically changing features. Indeed, there has been significantprogress in the dynamic modelling of land use and land-usechange employing an agent-based approach (see reviews inBousquet and Le Page, 2004; Parker et al., 2004). For

example, Castella et al. (2005) investigated different pat-terns of land use following decollectivization in Vietnam,and showed that different patterns emerged as a functionof the relative population size to resource availability anddistribution that were consistent with field observations.Schmit and Rounsevell (2006) used the approach to evalu-ate hypotheses that land-use patterns in central Belgiumcould be explained by farmers’ imitating their neighbours.They found that simple imitation could not explain theobserved patterns. Thus, the approach has considerablepotential for testing the spatial patterns of land use (e.g.Pontius et al., 2004) and explanations for their evolutionthrough time (Parker et al., 2004). The application of anagent-based approach to landform evolution under humanimpact is thus investigated in this paper.

3. Agent-based models

Agent- (or individual-) based models have been success-fully employed in ecology for over two decades (Judson,1994). For example, Reynolds (1987) used simple sets ofrules to describe the movements of simulated animals (or‘boids’) that produced emergent patterns of the develop-ment of flocks, herds and schools (although this workwas originally in relation to the development of computergraphics, the work has subsequently been widely cited; anearlier ecological example relating to ecological researchis given by Hogeweg and Hesper (1983)). Turner et al.(1993) used the approach to investigate survival patternsof moose over winter in the Yellowstone National Park;while Peters (2002) model the life cycles of individual plantsin response to different environmental constraints. Epsteinand Axtell (1996) adopted the approach for the investiga-tion of societies in their attempt to build social science‘‘from the bottom up’’. Their simulations investigated thepatterns that emerge according to a range of sets of rulesrelating to kinship, learning, social exclusion and diseasein a simple landscape called ‘‘SugarScape’’. Subsequentreal-world applications of their work included an investiga-tion of the collapse of the Kayenta Anasazi in Arizona inresponse to changing environmental conditions (Axtellet al., 2002). However, these changes were imposed onthe model (based on interpretations of rainfall change fromtree rings), rather than being dynamically emergent fromthe model. Beyond the land-use and land-cover changeapplications of agent-based models already mentioned,there have been a number of geographical applications ofthe approach. Torrens (2006) evaluated the mechanismsof contemporary suburban sprawl in the US with a specificapplication to the Midwestern Megalopolis surroundingLake Michigan, while Sanders et al. (1997) investigatedthe development of urban centres in post-mediaeval south-ern France. At a smaller spatial scale, there is a growingbody of agent-based work on urban dynamics, from thelevel of individual buildings to entire cities (Batty, 2005).This work has both applied aspects (for example the workof Batty et al., 2003; investigating congestion and safety


issues relating to the Notting Hill Carnival) and explana-tory aspects (e.g. the work on pedestrian behaviour ofKerridge et al., 2001 and Haklay et al., 2001).

The common structure underlying this wide range ofapproaches is a focus on individual agents or entities ratherthan attempts to simulate the overall response of a systemfrom a ‘‘top-down’’ series of equations (although seeOkubo, 1986; for an early example of comparisons of thedifferent approaches). Parker et al. (2004) define three char-acteristics of agents: autonomy, communication/interac-tion and decision-making; Macy and Willer (2002)include a further characteristic, that of adaptive and back-ward-looking ability. At a more general level, agents haveattributes (including their location in a spatially explicitmodel) and perform tasks (Ginot et al., 2002) either interms of interaction with other agents or with the environ-ment. Specific implementations of agent-based modelsrequire the definition of these tasks at different levels ofcomplexity. Agents may interrogate the environment inwhich they are situated and the resulting information isemployed to make decisions about how to interact withthe environment. Similarly agents may communicate witheach other and consequently make decisions about theirinteraction with each other and with the environment.Issues relating to the effectiveness with which the agentscan have knowledge of the environment and each other,and the consequent implications for modelling, are consid-ered later.

Fig. 2. General structure of the CYB

4. The CYBEROSION modelling framework

4.1. Model structure

The CYBEROSION model employs a cellular representa-tion of a landscape based on a digital elevation model(DEM) of the topography (Fig. 2). At this stage, the modelaims to reproduce the main interactions between the land-scape, vegetation, animals and people, rather than repre-senting the detail of processes with respect to any ofthese elements. Cellular approaches to landscape-evolutionmodelling are becoming an increasingly importantapproach as they avoid the linearization of process, andtop-down forcing of structure on the landscape that areinherent in finite difference based approaches (Chase,1992; Coulthard et al., 2002; Favis-Mortlock et al., 2000;Luo, 2001; Murray and Paola, 1997; Wainwright, 2006).Each cell in CYBEROSION has a series of landscape charac-teristics that can evolve through time. It has a thicknessof soil at a specific elevation (given by the DEM), whichhas relative particle-size characteristics (fine matrix versusstone content) and nutrient content. The surface-litter layeris also represented. The vegetation structure is divided intosix classes of vegetation appropriate to Mediterranean con-ditions over Quaternary time scales (e.g. Mommersteeget al., 1995) – dry evergreen woodland, cool evergreenwoodland, deciduous woodland, shrubs, grasses and cere-als – in order to simulate the different possible types of

EROSION modelling framework.


vegetation possible and competition between them withoutneeding to simulate the details of individual species. Vege-tation-related processes operating within the model do soat a daily time step so that they respond to the consump-tion by animals. Vegetation growth is simulated using alogistic growth model (Kot, 2001) in which the growth-rateand carrying-capacity parameters are functions of the veg-etation type and local environmental factors (Table 1). Soilthickness, elevation, particle size and nutrient contentchange as functions of changing rates of erosion, deposi-tion and weathering. Nutrients are also functions of vege-tation cover and climate. Runoff is generated on anannual basis, with a runoff coefficient based on soil texture,vegetation cover and soil moisture. Flow through the land-scape is based on the DEM, with flow leaving a cell in pro-portion to the relative slope in a given direction. Erosion isthus simulated on an annual basis, with both diffuse (over-land flow) erosion and concentrated (rills, gullies and chan-nels) erosion represented; the threshold between the two isbased on a critical flow rate. No other forms of erosionsuch as creep or slope instability are currently represented.Weathering is simulated as both chemical (dissolution) andphysical processes. Because of the relatively slow rates ofweathering, these two processes are also calculated on anannual time step.

Within this landscape a number of different types ofagent representing different species of animal are distrib-

Table 1Summary of process-related parameters in CYBEROSION and their related cont

Parameter Functional controlsa

VegetationCool evergreen woodland growth Low temperatures, slightly negatDry evergreen woodland growth Hot temperatures, negative waterDeciduous woodland growth Warm temperatures, high rainfalShrub growth Open vegetation structureGrass growth Open vegetation structureCereal growth As grass but nutrient and shadin

SoilElevation Balance between erosion, depositSoil thickness Balance between erosion, depositFines content Low erosion, high deposition, hiStones content High erosion, low deposition, lowNutrient content High rate of leaf fall, high tempe

(Kirkby and Neale, 1987)Organic matter High rate of leaf fall

RunoffRunoff coefficient High rainfall, low stone content,Flow accumulation Routing from eight neighbouring

Erosion rateDiffuse overland flow erosion Low stoniness/high fines content

(Wainwright, 2006)Concentrated (rill/gully/channelized)erosion

High runoff, high slope, low vege

Weathering rateChemical weathering High available water (DreybrodtPhysical weathering High soil thickness (Ahnert, 1987

a Conditions required for parameter to increase.

uted. These agents are process-based in that they need toobtain energy from the landscape to survive, grow, repro-duce and move; they die either by obtaining insufficientenergy or reaching the end of a typical lifespan. Similarapproaches have been used extensively in population ecol-ogy (e.g. Turner et al., 1993; Bond et al., 2000). In theCYBEROSION framework, the agents permit the necessaryfeedback between animal behaviour and landscape evolu-tion, primarily through the interaction with vegetation ofdifferent types for food. Because the agents can movedynamically through the landscape in search of food, theyenable a self-organization of the landscape structure basedon local edaphic conditions and their interaction with thelocations of agents at any point in time. The vegetationcover then feeds back to the runoff generation and erodibil-ity components of the model, creating the link with land-form evolution.

Agents are divided into two broad types. First, wild ordomestic animal species are represented. Although thereis no reason to limit the number of animals beyondissues of practicality and computational power, at presentCYBEROSION can represent five types typical of later Euro-pean prehistory, namely cattle (or aurochs), pig (or wildboar), sheep, goat and deer. Agents are assigned a sex ran-domly at birth, which then affects characteristics relating tomaximum weight, potential rate of growth, energy costs ofmovement and ability to transfer food to energy, and for

rols

ive water balance, little competition from other treesbalances

l, little competition from other trees

g limited

ion and weatheringion and weatheringgh chemical weathering, low physical weathering

chemical weathering, high physical weatheringratures, high transpiration rates, high rainfall relative to runoff

low vegetation cover, high soil moisturecells using flow proportionate to relative slope of contributing cells

, low nutrient content, low vegetation cover, high runoff, high slope

tation cover (Wainwright, 2006)

, 1988; Kaufmann and Braun, 2001))


female agents energy costs of pregnancy and lactation(Table 2). Each simulated day, an agent has an assessedtarget of food consumption based on its age, sex and exist-ing weight, which it then tries to satisfy by eating vegeta-tion from different parts of the six functional classes. Thedifferent animal types obtain different proportions of theirdiet from the different vegetational types and thus will tryto satisfy their needs according to the type of vegetationpresent. It is currently assumed that the animals have a per-fect knowledge of the vegetation in the cell in which theyare situated. If there is insufficient vegetation to satisfy thisenergy need in the present location (cell) of the animal,then it looks for the adjacent cell with the highest appropri-ate level of vegetation for it to consume and moves there(cf. Turner et al., 1993). Again, it is assumed that the ani-mal can ‘‘see’’ perfectly what is in the adjacent cells. Themovement consumes energy, which thus has to be balancedby further consumption of vegetation. The energy con-sumption is a function of both distance and slope steep-ness. The process is repeated until the energy requirementhas been met or until too many steps have been takenwithin the day. The energy balance for the day is calculatedand converted to a change in weight. If the agent’s weightfalls below the critical value for its age and sex, then it diesand no further simulations are made with it. The criticalvalue is assumed to be 70% of the average body weightfor an animal of that age and sex, following Turner et al.(1993). Female agents become pregnant at appropriatetimes through the year, depending on the species, age andweight (if they have insufficient reserves, then they are con-sidered unable to conceive), using a random model on adaily basis to ensure that births occur throughout theappropriate season. Once pregnant, they then followappropriate periods of pregnancy and lactation, during

Table 2Summary of parameters used in energy balance model of animal agents

Parameter Cattle

Birth weight (kg) 28.7

Adult weight (kg)Female 413Male 618

Maximum age (days) 4380

Forage intake (% body weight [BW])Female 3.125Male 3.345

Grazing cost (MJ kg�0.75 BW day�1) 0.0653Movement cost (MJ kg�0.75 BW km�1) 0.2177Energy use in metabolic maintenance (MJ kg�0.75 BW day�1) 0.4353Energy to weight conversion (kg MJ�1 day�1) 0.182Energy use in lactation (MJ kg�0.75 BW day�1) 0.0435Minimum reproductive age (days) 496Gestation period (days) 283Suckling period (days) 245Anoestus duration (days) 80

Data are from AFRC Technical Committee (1993, 1998), ARC Working ParCouncil (1981a,b, 1985, 1998, 2000), Perry (1984) and Thomas et al. (1983).

which their energy requirements increase. Simple rules areemployed to ensure groups of animals do not become toolarge in relation to available local resources, althoughdetailed herding and seasonal migration behaviour arenot simulated in this version of the model.

Secondly, human agents are simulated within CYBERO-

SION. The basic energetics model that drives the other ani-mal species is also used to simulate the human agents,and has a similar set of parameters. Where the humanagents are different is in their behaviour within the land-scape. In the example simulations to be presented here,the human agents will be considered to have a developedhunter-gatherer lifestyle. The behaviour is controlled by asimple set of rules at present, although the modellingframework is left open to allow the subsequent develop-ment of more complex sets of rules, or other approachesto the simulation of agent behaviour and interaction.Human agents first search for animals in their present loca-tion (cell) to hunt, and if successful, consume the animal.Otherwise, they gather vegetal foodstuffs from the currentcell and attempt to move to adjacent cells to hunt. Again,movement entails an energy cost. If sufficient energy hasbeen gained, the human agent may then decide to clearwoodland (this approach is a simplification to the basic cul-tivation of cereals because of the way the vegetation modeloperates) or spend time on other things (e.g. leisure), or tomove to more appropriate locations in the landscape.

4.2. Example model applications

The model can thus be used to evaluate the response ofthe landscape to different conditions, populations of ani-mals and people, and initial conditions. In the followingexample, an application is made to represent middle Neo-

Pig Sheep Goat Deer

1.5 9.8 5.0 12.2

130 53.3 28.6 156156 71.5 52 325

3650 5110 4380 6570

1.474 2.182 2.414 2.6781.497 2.269 2.621 3.005

0.0293 0.0994 0.1061 0.06530.1334 0.1790 0.2652 0.21770.4437 0.3977 0.4244 0.43530.0967 0.1989 0.303 0.1820.0444 0.0398 0.0424 0.0435240 300 270 496115 148 151 23421 75 69 9060 80 80 90

ty (1980), Ellis et al. (2000), McDonald et al. (1995), National Research


lithic settlement at Roucadour (Fig. 1), where hunting andsimple cultivation were the dominant modes of landscapeexploitation according to archaeological data (Gasco

Fig. 3. DEM of the area used in the model experiments described in the paper.the area enclosed by the dashed rectangle in the map inset in Fig. 1.

Table 3Summary of initial conditions of model runs analyzed

Run number Number of animals Number of peop

1 0 02 0 03 1000 04 1000 105 1000 106 1000 257 1000 508 1000 1009 5000 50

10 5000 10011 10,000 10012 1000 1013 1000 1014 1000 10

15 1000 10

16 1000 10

17 1000 10

a Figures in brackets are the initial mean biomass of the six vegetation typesshrubs, grasses, cereals) in kg ha�1.

et al., 2004; Wainwright et al., 2006). A realistic topogra-phy of an area of approximately 11 km · 11 km surround-ing the site is derived using SRTM DEM data at a 90-m

The DEM is derived from the SRTM dataset (USGS, 2005) and represents

le Other conditions

No vegetationOpen vegetation [10, 10, 10, 50, 500, 500]a

Open vegetation [10, 10, 10, 50, 500, 500]a


Replicate of run 4Open vegetation [10, 10, 10, 50, 500, 500]a






Deciduous woodland [10, 10, 5000, 1000, 50, 50]a

Deciduous forest [10, 10, 7500, 500, 50, 50]a

Mixed woodland [500, 1000, 4000, 2000, 50, 2000]a

Hunting success 50% as efficientMedium human reproduction rateMixed woodland [500, 1000, 4000, 2000, 50, 2000]a

Hunting success 50% as efficientLow human reproduction rateMixed woodland [500, 1000, 4000, 2000, 50, 2000]a

Hunting success 50% as efficientHigh human reproduction rateMixed woodland [500, 1000, 4000, 2000, 50, 2000]a

Hunting success 50% as efficientVery high human reproduction rate

(dry evergreen woodland, cool evergreen woodland, deciduous woodland,


resolution (USGS, 2005; Fig. 3), and a uniform soil coverof 1 m added. A mean rainfall of 800 mm and mean annualtemperature of 20 �C is used. All other parameters are con-sidered spatially constant for simplicity at this stage. Theseconditions are not necessarily realistic, and so the modelexperiments described should not be considered in this caseas an attempt to reconstruct landscape evolution in thisparticular period and time. Rather they are intended toevaluate the effect on the behaviour of landform-evolutionmodels of the ‘‘missing link’’ of animal and human activity.Therefore, four sets of simulation have been carried out:first, the landscape is simulated without vegetation coverand without any agents; secondly, the effect of vegetationcover is simulated, again without agents; thirdly, the inter-action between vegetation and animals is investigated; andfourthly, the impacts of adding human agents are explored.A summary of the initial conditions used in different simu-lations is given in Table 3. Initialization of the vegetationincludes a small random variation added to each point.Where animal agents are simulated, animals are randomlyallocated species and sex, and are initially distributed ran-domly through the landscape. In the simulation withhuman agents, the initial population is randomly distrib-uted throughout a limited area in the north-east of thelandscape (corresponding broadly to the site catchment

Fig. 4. Time series of model simulations to evaluate differences in the presenceagents, as described in the text: (a) total landscape vegetation biomass; (b) pesimulations with animals and with animals and people and (d) net landscape erin the vegetation only and vegetation and animal agents simulations.

of Roucadour). All simulations were run on a daily timestep for a period of 500 years. The boundary conditionfor agents in the landscape is assumed for the sake of sim-plicity to be a ‘‘wrap-around’’ – i.e. agents moving off theright-hand side of the simulation domain reappears onthe left-hand side (and vice versa, and the same for theupper and lower boundaries) so that there are no edgeeffects such as issues with the gradual loss of agents as theymove and no need to specify external controls on the pro-cess. Water and sediment fluxes across domain boundariesare entirely flux-controlled based on the characteristics ofthe edge cells.

5. Results

Fig. 4 presents summary model outputs in the form ofannual time series for the first four sets of simulation,which compare landscape behaviour with and without veg-etation, and in the latter case with animal agents and thenanimal and human agents. The landscape supports thehighest total biomass when no agents are present, andreaches a steady state value after about 80 years of simula-tion. The steps in the biomass curve in this case representdifferent phases in the succession of different vegetationtypes; the longer term stable vegetation in this case being

and absence of vegetation, and presence and absence of animal and humanrcentage of this biomass which is tree cover; (c) number of agents in theosion rate (total erosion less total deposition). Note that no erosion occurs


represented by a mixture of shrub and cereal types. Anintermediate vegetation cover is encountered when animalagents are included in the modelling, with clear oscillationsin the cover from about 100 years of simulation. The coverin this case is dominated by the grass and shrub types.When the human agents are present, the lowest vegetationcovers are found, again with oscillating total values as thepopulations of agents fluctuate. The longer term vegetationis dominated by grasses. Tree cover is highest when animalagents (only) are present, and lowest when both animal andhuman agents are simulated.

The animal population in all simulations where animalagents are present came to be dominated by cattle, reflect-ing the type of vegetation available for consumption underthe climate conditions simulated (800 mm annual averagerainfall and 20 �C annual average temperature). Whereno human agents are present, the cattle population oscil-lated between around 1100 and 1350 individuals after 100years, with a cyclicity of approximately 12 years. Whenhuman agents are simulated, the cattle population initiallyincreases more rapidly, but is hunted to extinction afterabout 40 years. The human population in these simulationsmanages to survive for another 300 years on vegetal foodsupplies alone (oscillating between 160 and 210 individualswith a cyclicity of about 8 years), but finally die out afterabout 340 years. The small population from about 250

Fig. 5. Spatial patterns of vegetation cover and erosion for the simulations wExample after 50 simulated years. Note the much more concentrated spatial llocalized erosion rates. Each pixel is 90 m; light colours represent high valuesvalues.

years and ultimate extinction of all the agents accountsfor the stabilization of the vegetation cover in these simu-lations after about 250 years.

Erosion results are presented in Fig. 4 as net erosionrates – total erosion within the simulated area less totaldeposition within the area, or in other words, totalsediment outflux from the simulated area – although thepattern of total erosion is highly correlated. The highesterosion rates are simulated under the unvegetated, noagents conditions. Under these conditions, erosion gradu-ally decreases through time as the landscape becomesadjusted to the climate conditions. No erosion is producedunder the simulations with vegetation only or with vegeta-tion and animal agents. When human agents are present,erosion rates are low but persistent until the populationreaches its peak, at which time there is a sudden increasein erosion rate. The higher rates persist for about 30 years,at which point they decrease again to the earlier lowerrates. The erosion continues even after the total extinctionof the human agents, and this result probably explains thelower biomass in these simulations in the longer term. Inthis sense, the simulations have produced conditions akinto long term land degradation. It should be noted thatthe differences in total biomass required to produce this dif-ference in the longer term are not large, reflecting thethreshold nature of the erosion system.

here animals and animal and human agents are present in the landscape.oss of vegetation where human agents are present and the corresponding(biomass in upper figures, erosion in lower figure) and dark colours low

Fig. 6. Spatial pattern of erosion in the landscape (a) where no vegetationand no agents; and (b) vegetation and animal and human agents aresimulated. Each pixel is 90 m; light colours represent high values and darkcolours low values.


As well as different totals and temporal patterns, thespatial patterns of erosion are different with the presenceof human agents. When only animal agents are simulated,the consumption of vegetation is relatively diffusely distrib-uted across the landscape (Fig. 5). However, where humanagents are added, there is a much more focussed loss ofvegetation cover around a small area in the north-west ofthe simulated area. It should be noted that this pattern isrelated only to the emergence of a spatial pattern throughthe interaction of the rules that control the animal andhuman agents. There are no explicit rules in the simulationsthat confine either to this location, or the human activity toa specific base camp or site. The highly localized nature ofthis vegetation loss is what is responsible for the localizednature of erosion under the simulations with humanagents, and does not occur when human agents are notpresent. The spatial pattern of erosion where no vegetationis present (and no agents) is much more related to the con-figuration of steep slopes and channelized areas (Fig. 6).Thus, the human agents are responsible for both qualita-tive and quantitative changes in the patterns of erosion thattake place within the landscape.

A second set of simulations was carried out to investigatethe effects of different initial numbers of animal and humanagents present within the same, initially open landscape.Initial numbers of animal agents varied between 1000 and10,000 and initial numbers of human agents varied between10 and 100. Qualitatively similar patterns of landscape evo-lution occur, albeit with some quantitative differences(Fig. 7). Net erosion rates are generally higher when morepeople start in the landscape, and also when there are moreanimals. An exception to this case is for initially very highnumbers of animal and human agents (10,000 and 1000,respectively), when there is a slight reduction in overall ero-sion compared to the next highest initial density of agents.There seems to be no clear explanation for this patterneither in terms of population densities or of vegetation bio-mass at the time of peak erosion, and it appears to be morelikely to be related to the sequence of changes at particu-larly sensitive points in the landscape. The timing of majorpeaks in net erosion rates corresponds best to the first majorfall in biomass, which is typically delayed with lower initialnumbers of agents, because of the time lag required for pop-ulations to increase.

The effect of different initial vegetation compositionswas also investigated. The lowest number of agents (1000animals and 10 humans) was distributed on landscapes rep-resenting open vegetation (run 5: Table 3), open deciduouswoodland (run 12) and dense deciduous woodland (run13). In these simulations (Fig. 8), the total vegetation bio-mass in the landscape is higher for a period of 50–60 yearswhen there is initially woodland or forest, but these initialconditions permit the agent populations to become greater,especially in the case of the human agents. Following arapid expansion of the agents, the total biomass drops toapproximately the same level as with the initially openvegetation but after 175–230 years the wooded or forested

initial conditions permit some recovery to denser total bio-mass, but not to the same initial vegetation structure.Again, these results illustrate longer term impacts of shortphases of landscape change. The onset of erosion is delayedby the initially denser vegetation. Although the peak rate ofnet erosion increases with the initial vegetation density, thetotal net erosion is smallest in the intermediate case,because the erosion under the open vegetation conditionsis maintained over a longer time period. The punctual nat-ure of erosion is emphasized by the fact that the erosionpeaks during a time when the landscape is significantlywooded.

A final series of simulations was carried out to evaluatethe effect of different human reproduction rates on the evo-lution of other agents and the landscape. For these simula-tions, a mixed woodland environment was used, and thehunting success rate was assumed to be 50% that of the pre-vious experiments. Under the low reproduction rate, the

Fig. 7. Time series of example model simulations used to evaluate sensitivity of the open landscape to initial numbers of animal and human agents: (a)total landscape vegetation biomass; (b) percentage of this biomass which is tree cover; (c) number of cattle agents; (d) number of human agents and (e) netlandscape erosion rate (total erosion less total deposition).


human agents die out rapidly and although the animalagents persist longer in the landscape (cattle to 202 years,pig to 75 years and deer to 134 years), they do not consumesufficient vegetation to lead to any significant erosion(Fig. 9). The persistence and size of animal populations isinversely proportional to the size and rate of growth ofthe human agent populations, with corresponding changesin the timing and magnitude of the onset of erosion. In allof the cases where there is a significant impact on erosion,the biomass takes at least 200 years to recover, again dem-onstrating the persistence of impacts.

6. Discussion and conclusions

The use of agent-based modelling holds great promisefor the development of realistic process-based, landform-evolution models. While great strides have been made over

the last 15–20 years to include vegetation processes intogeomorphic models (and understanding in the broadersense), much less has been achieved in the incorporationof the dynamics of other biotic influences and feedbacks.Yet the modelling carried out here demonstrates that thelandscape can indeed be sensitive to them and produce aseries of complex and threshold responses. Introducing ani-mal agents changes the vegetation structure and pattern(biomass, type and spatial organization), but in the temper-ate conditions simulated these changes are insufficient totrigger an erosion episode. Human agents trigger further,different changes to the vegetation, which are locally suffi-cient – at least for a relatively short time during the popu-lation-expansion phase (cf. Wainwright and Thornes, 2003)– to cause a significant peak in the erosion rates. The agent-based approach has significant advantages over scenario-based approaches, in that it permits these patterns to

Fig. 8. Time series of example model simulations used to evaluate sensitivity of the open landscape to initial type and thus density of vegetation: (a) totallandscape vegetation biomass; (b) percentage of this biomass which is tree cover; (c) number of cattle agents; (d) number of human agents and (e) netlandscape erosion rate (total erosion less total deposition).


emerge from hypothesized behaviour, allows dynamic feed-back (so that for example in these simulations overexploi-tation of resources seems to lead to land degradation andan ultimate lack of sustainability for the human popula-tion) and produces emergent spatial patterns that can becompared with field data, but whose generation are inde-pendent of those data. The emergent patterns vary in acomplex way as a function of initial conditions (numberand type of agents as well as patterns of vegetation) andhuman agent rules used (efficiency of hunting and controlson reproduction rates). At present, the CYBEROSION model-ling framework is still in the course of development, so theresults presented above should be considered to be qualita-tive evaluations rather than simulations of actual landformdevelopment in a specific environment. In this sense, themodel is a useful heuristic in terms of evaluating strategiesthat are required for holistic modelling of landform evolu-tion over a variety of time scales.

The model as it stands has a number of deficiencies. Inpart these point to requirements for further work for exam-ple in the need for better process understandings of

human–animal–vegetation–erosion feedbacks. For exam-ple, at present the vegetation effect on erosion is simplymediated through vegetation cover, while it is likely thatthe spatial pattern and connectivity within patches of veg-etation are important, especially as the removal of covercontinues. If agent behaviour changes during this ongoingprocess, the resulting patterns at small scales may propa-gate to control landform response at much larger scales.The current form of the model also has great difficultymaintaining species diversity over medium to long timescales (several decades to centuries), even in the absenceof human agents. Further investigations are required toevaluate whether this is a scale effect – the model is a‘‘closed world’’ at present, with the lower boundary com-municating with the upper boundary and the left commu-nicating with the right as far as agents (but not water orsediment) are concerned, so that edge effects are avoided– or relates to missing processes within the model (suchas a realistic predation function or population-density-dependent fertility). Alternatively, given that diversity ismaintained longer in the more forested landscapes, the lack

Fig. 9. Time series of example model simulations used to evaluate sensitivity of the open landscape to human reproduction rates: (a) total landscapevegetation biomass; (b) percentage of this biomass which is tree cover; (c) number of cattle agents; (d) number of human agents and (e) net landscapeerosion rate (total erosion less total deposition).


of larger distance migration and seasonal patterns of move-ment and behaviour may suggest that much bigger, diverseareas are required to simulate these species, and that there-fore more complex rules for movement are needed. How-ever, the rules controlling agents have thus far been keptdeliberately simple by zealous application of Occam’srazor. If excessively complicated rules are used, then anevaluation of the minimum requirement (or sets of require-ment) for explaining landform response can be made. Themore complicated the rule base, the more likely it is thatcircular logic will be employed to explain specific patternsfrom data that are also used to infer the success of themodel outcomes. Indeed, the more akin the process willbe to defining static scenarios, thus defeating the objectof the approach. It is clear, though, that the simplest rulebase as included in the present examples, is insufficient toexplain all of the patterns within the landscape. Otherscales of variability, and feedback in behaviour (especiallyin relation to patterns of overkill and social behaviour thatfor example affects birth rates or hunting patterns: considerSahlins’ (1972) ‘‘structure of underproduction’’) must be

involved in the operation of landscapes such as these. Aprogressive addition of rules that can explain all of theaspects of the observed landscape patterns should be fol-lowed as an appropriate methodological approach (Macyand Willer, 2002).

O’Sullivan and Haklay (2000) have produced a broadcritique of the agent-based modelling approach. They pointout that agent-based models have tended to emphasizeindividualistic rather than social characterizations of soci-ety, despite the attempts of some practitioners to introducesimple representations of social and institutional traits (e.g.Epstein and Axtell, 1996). The behaviour of individualsshould evolve and the representation of the social shouldalso evolve from individual behaviour and interactionsbetween individuals, while still accounting for the historicallegacies of the context of study. These criticisms are true ofthe current version of CYBEROSION, and developments areunderway in order to overcome some of these issues. Forexample, a rule-based approach allows only limited flexibil-ity for adaptability, so that investigations using artificialneural networks (e.g. Rolls and Treves, 1998) are underway


to allow agents to learn directly from their environmentand from each other. Different levels of structure couldthen be used to represent inherited learned behaviour andother traits of social interaction, including also behaviourthat allows non-economically (s.l.) rational behaviour tobe represented (e.g. Stern, 2000; Wolozin, 2002). Certainly,these sorts of development are required to avoid the agent-based approach from producing simplistic (as opposed tosimple), normative explanations of human-environmentinteractions.

The modelling approach is not an end in its own right. Itmust be compared and tested with empirical data and theresults interpreted as to their explanatory value. For exam-ple, the results in Fig. 5 produce an emergence of spatialpatterning of vegetation and erosion responses that couldbe considered to relate to a high impact zone around a spe-cific ‘‘site’’. This type of emergent behaviour is an advan-tage of the agent-based approach, and in this case seemsto represent the impact of the interaction between humanand animal agents. However, it is important not to over-interpret this sort of emergent patterning (e.g. Frigg,2003). There is no notion of a ‘‘site’’ in this version ofthe model, nor any way of the human agents’ developingone. The implication of the results may be that the focusof activity naturally tends to crystallize around particularlocations in non-deterministic ways, but other sets of rulesmay lead to highly dispersed activity, and it would only beby comparing against archaeological or ethnographic datathat the meaning of this sort of emergent pattern could beinterpreted. Conversely, including a notion of sedentarypopulation around a site may introduce bias in terms ofthe response of the environment. However, at least themodelling approach has advantages over the alternativesin allowing an evaluation of the realism of such interpre-tations. Modelling is therefore a significant tool in thedevelopment of rich interpretations of human-landscapeinteractions.

Acknowledgements

A number of the ideas in this paper have been fruitfullydiscussed with George Perry, James Millington and DavidDemerrit, although of course none of them should be heldresponsible for my interpretations. Two anonymousreviewers made helpful and constructive comments on thefirst version of the paper. I would like to thank Jean Gascofor his continuing support both in the field and conceptu-ally, and for assistance with Figs. 1 and 2. This work hasbeen supported by a fellowship from the LeverhulmeFoundation.

References

AFRC Technical Committee, 1993. Energy and Protein Requirements ofRuminants. CAB International, Wallingford.

AFRC Technical Committee, 1998. The Nutrition of Goats. CABInternational, Wallingford.

Ahnert, F., 1970. Functional relationships between denudation, relief anduplift in large mid-latitude drainage basins. American Journal ofScience 268, 243–263.

Ahnert, F., 1976. Brief description of a comprehensive three-dimensionalmodel of landform development. Zeitschrift fur Geomorphologie NFSupplementband 25, 29–49.

Ahnert, F., 1987. Approaches to dynamic equilibrium in theoreticalsimulations of slope development. Earth Surface Processes andLandforms 12, 3–15.

ARC Working Party, 1980. The Nutrient Requirements of RuminantLivestock. CAB, Slough.

Axtell, R.L., Epstein, J.M., Dean, J.S., Gumerman, G.J., Swedlund, A.C.,Harburger, J., Chakravarty, S., Hammond, R., Parker, J., Parker, M.,2002. Population growth and collapse in a multiagent model of theKayenta Anasazi in Long House Valley. Proceedings of the NationalAcademy of Sciences 99, 7275–7279.

Batty, M., 2005. Agents, cells and cities: new representational models forsimulating multiscale urban dynamics. Environment and Planning A37, 1373–1394.

Batty, M., Desyllas, J., Duxbury, E., 2003. The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals andstreet parades. International Journal of Geographical InformationScience 17, 673–697.

Bond, N.R., Perry, G.L.W., Downes, B.J., 2000. Dispersal of organisms ina patchy stream environment under different settlement scenarios.Journal of Animal Ecology 69, 608–619.

Bousquet, F., Le Page, C., 2004. Multi-agent simulations and ecosystemmanagement: a review. Ecological Modelling 176, 313–332.

Butzer, K.-W., 1974. Accelerated soil erosion: a problem of man-landrelationships. In: Manners, I.R., Mikesell, M.W. (Eds.), Perspectiveson Environment. Association of American Geographers, Washington,DC, pp. 57–77.

Castella, J.-C., Boissau, S., Trung, T.N., Quang, D.D., 2005. Agrariantransition and lowland–upland interactions in mountain areas innorthern Vietnam: application of a multi-agent simulation model.Agricultural Systems 86, 312–332.

Chase, C.G., 1992. Fluvial landsculpting and the fractal dimension oftopography. Geomorphology 5, 39–57.

Cockburn, H.A.P., Summerfield, M.A., 2004. Geomorphological appli-cations of cosmogenic isotope analysis. Progress in Physical Geogra-phy 28, 1–42.

Coulthard, T., Macklin, M.G., 2001. How sensitive are river systems toclimate and land-use changes? A model-based evaluation. Journal ofQuaternary Science 16, 347–351.

Coulthard, T., Macklin, M.G., Kirkby, M.J., 2002. A cellular model ofHolocene upland river basin and alluvial fan evolution. Earth SurfaceProcesses and Landforms 27, 269–288.

Coulthard, T.J., Macklin, M.G., 2003. Long-term and large scale highresolution catchment modelling: innovations and challenges arisingfrom the NERC land–ocean interaction study (LOIS). In: Lang, A.,Hennrich, K., Dikau, R. (Eds.), Long term hillslope and fluvial systemmodelling, 101. Springer Lecture Notes in Earth Sciences, Berlin, pp.123–134.

Dreybrodt, W., 1988. Processes in Karst Systems. Springer-Verlag, Berlin.Ellis, W.C., Poppi, D., Matis, J.H., 2000. Feed intake in ruminants: kinetic

aspects. In: D’Mello, J.P.F. (Ed.), Farm Animal Metabolism andNutrition. CAB International, Wallingford, pp. 335–363.

Epstein, J.M., Axtell, R., 1996. Growing artificial societies: socialscience from the bottom up. Brookings Institution Press, Washington,DC.

Favis-Mortlock, D., Boardman, J., Bell, M., 1997. Modelling long-termanthropogenic erosion of a loess cover: South Downs, UK. Holocene7, 79–89.

Favis-Mortlock, D.T., Boardman, J., Parsons, A.J., Lascelles, B., 2000.Emergence and erosion: a model for rill initiation and development.Hydrological Processes 14, 2173–2205.

Frigg, R., 2003. Self-organised criticality—what it is and what it isn’t.Studies in History and Philosophy of Science 34, 613–632.


Gasco, J., Briois, F., Bruxelles, L., Charvet, A., Gernigon, K., Muller, C.,Tresset, A., Vigne, J.-D., Wainwright, J., 2004. ‘Revision de lastratigraphie neolithique de Roucadour (le Neolithique ancien etmoyen).’ In: Dartevelle, H. (Ed.), Auvergne et Midi: Actualite de laRecherche. 5emes Rencontres Meridionales de Prehistoire Recente,Clermont Ferrand, November 2002, 353–378, Prehistoire du Sud-Ouest Supplement no. 9, Cressenac.

Gernigon, K., Valdeyron, N., Kerebel, J., 2000. Une occupation de la findu premier age du fer dans la doline des Escabasses (Themines, Lot).Prehistoire du Sud-Ouest 7, 83–93.

Ginot, V., Le Page, C., Souissi, S., 2002. A multi-agents architecture toenhance end-user individual based modelling. Ecological Modelling157, 23–41.

Haklay, M., O’Sullivan, D., Thurstain-Goodwin, M., Schelhorn, T., 2001.So go downtown: simulating pedestrian movement in town centres.Environment and Planning B 28, 343–359.

Hogeweg, P., Hesper, B., 1983. The ontogeny of the interaction structurein bumble bee colonies: a mirror model. Behavioural Ecology andSociobiology 12, 271–283.

Huggett, R.J., 1975. Soil landscape systems: a model of soil genesis.Geoderma 13, 1–22.

Judson, O.P., 1994. The rise of the individual-based model in ecology.Trends in Ecology and Evolution 9, 9–14.

Kaufmann, G., Braun, J., 2001. Modelling karst denudation on asynthetic landscape. Terra Nova 13, 313–320.

Kerridge, J., Hine, J., Wigan, M., 2001. Agent-based modelling ofpedestrian movements: the questions that need to be asked andanswered. Environment and Planning B 28, 327–341.

Kirkby, M.J., 1971. Hillslope process-response models based on thecontinuity equation. In: Brunsden, D. (Ed.), Slope Form and Process.Institute of British Geographers, London, pp. 15–30 (Special Publi-cation no. 3).

Kirkby, M.J., 1986. A two-dimensional simulation model for slope andstream evolution. In: Abrahams, A.D. (Ed.), Hillslope Processes. Allenand Unwin, London, pp. 203–222.

Kirkby, M.J., Neale, R.H., 1987. A soil erosion model incorporatingseasonal factors. In: Gardiner, V. (Ed.), International Geomorphol-ogy, vol. II. John Wiley and Sons, Chichester, pp. 210–289.

Kooi, H., Beaumont, C., 1996. Large-scale geomorphology: classicalconcepts reconciled and integrated with contemporary ideas via asurface processes model. Journal of Geophysical Research 101, 3361–3381.

Kosmas, C., Danalatos, N., Cammeraat, L.H., Chabart, M., Diamanto-poulos, J., Farand, R., Gutierrez, L., Jacob, A., Marques, H.,Martinez-Fernandez, J., Mizara, A., Moustakas, N., Nicolau, J.M.,Oliveros, C., Pinna, G., Puddu, R., Puigdefabregas, J., Roxo, M.,Simao, A., Stamou, G., Tomasi, N., Usai, D., Vacca, A., 1997. Theeffect of land use on runoff and soil erosion rates under Mediterraneanconditions. Catena 29, 45–59.

Kot, M., 2001. Elements of Mathematical Ecology. Cambridge UniversityPress, Cambridge.

Laflen, J.M., Moldenhauer, W.C., 2003. Pioneering Soil-Erosion Predic-tion. The USLE Story. World Association of Soil and WaterConservation, Beijing (Special Publication no. 1).

Luo, W., 2001. LANDSAP: a coupled surface and subsurface cellularautomata model for landform simulation. Computers and Geosciences27, 363–367.

McDonald, P., Edwards, R.A., Greenhalgh, J.F.D., Morgan, C.A., 1995.Animal Nutrition, sixth ed. Longman, Harlow.

Macy, M.W., Willer, R., 2002. From factors to actors: computationalsociology and agent-based modelling. Annual Review of Sociology 28,143–166.

Mommersteeg, H.J.P.M., Loutre, M.-F., Young, R., Wijmstra, T.A.,Hooghiemstra, H., 1995. Orbital forced dynamics in the 975,000 yearpollen record from Tenaghi Philippon (Greece). Climate Dynamics 11,4–24.

Murray, A.B., Paola, C., 1997. Properties of a cellular braided-streammodel. Earth Surface Processes and Landforms 22, 1001–1025.

National Research Council, 1981a. Effect of Environment on NutrientRequirements of Domestic Animals. National Academy of Sciences,Washington, DC.

National Research Council, 1981b. Nutrient Requirements of Goats:Angora, Dairy and Meat Goats in Temperate and Tropical Countries.National Academy of Sciences, Washington, DC.

National Research Council, 1985. Nutrient Requirements of Sheep, sixthed. National Academy of Sciences, Washington, DC.

National Research Council, 1998. Nutrient Requirements of Swine, 10thed. National Academy of Sciences, Washington, DC.

National Research Council, 2000. Nutrient Requirements of Beef Cattle,seventh ed. National Academy of Sciences, Washington, DC.

Okubo, A., 1986. Dynamical aspects of animal grouping: swarms, schools,flocks, and herds. Advances in Biophysics 22, 1–94.

O’Sullivan, D., Haklay, M., 2000. Agent-based models and individualism:is the world agent-based?. Environment and Planning A 32 1409–1425.

PAGES 2005. Past Global Changes Focus 5: LUCIFS Land Use andClimate Impacts on Fluvial Systems during the Period of Agriculture.http://www.pages-igbp.org/about/research/lucifs.html (accessed 17.05.2005).

Parker, D.C., Manson, S.M., Janssen, M.A., Hoffman, M.J., Deadman,P., 2004. Multi-agent systems for the simulation of land-use and land-cover change. Annals of the Association of American Geographers 93,314–337.

Perry, T.W., 1984. Animal Life-Cycle Feeding and Nutrition. AcademicPress, London.

Peters, D.P.C., 2002. Plant species dominance at a grassland-shrublandecotone: an individual-based gap dynamics model of herbaceous andwoody species. Ecological Modelling 152, 5–32.

Pontius Jr., R.G., Huffaker, D., Denman, K., 2004. Useful techniques ofvalidation for spatially explicit land-change models. Ecological Mod-elling 179, 445–461.

Reynolds, C.W., 1987. Flocks, herds, and schools: a distributed behavioralmodel. Computer Graphics 21, 25–34.

Rodriguez-Iturbe, I., Rinaldo, A., 1997. Fractal River Basins. CambridgeUniversity Press, Cambridge.

Rolls, E.T., Treves, A., 1998. Neural Networks and Brain Function.Oxford University Press, Oxford.

Sahlins, M., 1972. Stone Age Economics. Aldine, Chicago.Sanders, L., Pumain, D., Mathian, H., Guerin-Pace, F., Bura, S., 1997.

SIMPOP: a multiagent system for the study of urbanism. Environmentand Planning B 24, 287–305.

Scheidegger, A.E., 1961, 1970. Theoretical Geomorphology, first andsecond eds. Springer-Verlag, Berlin.

Schmit, C., Rounsevell, M.D.A., 2006. Are agricultural land use patternsinfluenced by farmer imitation? Agriculture Ecosystems and Environ-ment 115, 113–127.

Sidorchuk, A., Walling, D.E., Wasson, R., 2003. A LUCIFS strategy:modelling the sediment budgets of fluvial systems. In: Lang, A.,Hennrich, K., Dikau, R. (Eds.), Long Term Hillslope and FluvialSystem Modelling. Springer Lecture Notes in Earth Sciences 101,Berlin. pp. 19–35.

Smith, T., Bretherton, F., 1972. Stability and the conservation of massin drainage basin evolution. Water Resources Research 8, 1506–1529.

Stern, P.C., 2000. Psychology and the science of human-environmentinteractions. American Psychologist 55, 523–530.

Strahler, A.N., 1952. Dynamic basis of geomorphology. Bulletin of theGeological Society of America 63, 923–938.

Thomas, D.G.M., Benton, D.G., Herbert, T.G.C., Lloyd Jones, J., 1983.Animal Husbandry, third ed. Bailliere Tindall, London.

Torrens, P.M., 2006. Simulating sprawl. Annals of the Association ofAmerican Geographers 96, 248–275.

Tucker, G.E., Slingerland, R., 1997. Drainage-basin responses to climatechange. Water Resources Research 33, 2031–2047.

Turner, M.G., Wu, Y., Romme, W.H., Wallace, L.L., 1993. A landscapesimulation model of winter foraging by large ungulates. EcologicalModelling 69, 163–184.

http://www.pages-igbp.org/about/research/lucifs.html


USGS, 2005. Shuttle Radar Topography Mission (SRTM). http://srtm.usgs.gov/ (accessed 17.05.2005).

Van Andel, T., Runnels, C.N., 1987. Beyond the Acropolis: A RuralGreek Past. Stanford University Press, Stanford.

Van Andel, T., Zangger, E., Demitrack, A., 1990. Land use and soilerosion in prehistoric and historical Greece. Journal of Field Archæ-ology 17, 379–396.

Vita-Finzi, C., 1969. The Mediterranean Valleys. Cambridge UniversityPress, Cambridge.

Voller, V.R., Porte-Agel, P., 2002. Moore’s law and numerical modelling.Journal of Computational Physics 179, 698–703.

Wainwright, J., 1994a. Erosion of archæological sites: implications of asite simulation model. Geoarchaeology 9, 173–202.

Wainwright, J., 1994b. Anthropogenic factors in the degradation of semiarid regions: a prehistoric case study in Southern France. In:Millington, A.C., Pye, K. (Eds.), Effects of Environmental Changeon Drylands. John Wiley and Sons, Chichester, pp. 285–304.

Wainwright, J., 2000. Contextes geomorphologiques et geoarcheologiquesdes habitats de l’Age du Bronze en Mediterranee Occidentale. In:Gasco, J., Claustre, F. (Eds.), XXIVe Congres Prehistorique deFrance, Carcassonne, 26–30 septembre, 1994: Habitats, Economies etSocietes du Nord-Ouest Mediterraneen de l’Age du Bronze au PremierAge du Fer. – Actes du Colloque International. Societe Prehistoriquede la France, Paris, pp. 11–26.

Wainwright, J., 2004. A review of European Union funded research intothe history and evolution of Mediterranean desertification. Advancesin Environmental Monitoring and Modelling (4), 1–87. Availableonline at http://www.ambiotek.com/advances/advemm/vol1no4/wainwrig.pdf.

Wainwright, J., Thornes, J.B., 2003. Environmental Issues in theMediterranean Processes and Perspectives from the Past and Present.Routledge, London.

Wainwright, J., Mulligan, M., 2003. Modelling human decision-making.In: Wainwright, J., Mulligan, M. (Eds.), Environmental Modelling:Finding Simplicity in Complexity. John Wiley and Sons, Chichester,pp. 225–244.

Wainwright, J., 2006. Degrees of separation: hillslope-channel couplingand the limits of palaeohydrological reconstruction. Catena 66, 93–106.

Wainwright, J., Gasco, J., Muller, C., 2006. Vers une comprehension dupaysage du Causse de Gramat et du peuplement de Roucadour depuisle Neolithique. In: Fouere, P., (Ed.), Actes des 6emes RencontresMeridionales de Prehistoire Recente, Perigueux, pp. 83–98.

Wischmeier, W.H., Smith, D.D., 1978. Predicting rainfall-erosion losses.USDA Agricultural Research Service Handbook 537, Washington,DC.

Wolozin, H., 2002. The individual in economic analysis: toward psychol-ogy of economic behavior. Journal of Socio-Economics 31, 45–57.

http://srtm.usgs.gov/

http://srtm.usgs.gov/

http://www.ambiotek.com/advances/advemm/vol1no4/wainwrig.pdf

http://www.ambiotek.com/advances/advemm/vol1no4/wainwrig.pdf

Documents

Can modelling enable us to understand the rôle of humans in landscape evolution?