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ARTICLE IN PRESSG ModelCOMOD-7294; No. of Pages 15

Ecological Modelling xxx (2014) xxx–xxx

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Ecological Modelling

journa l h om epa ge: www.elsev ier .com/ locate /eco lmodel

ynamic integration of sustainability indicators in insularocio-ecological systems

sabel Banos-Gonzáleza,∗, Julia Martínez-Fernándezb, Miguel Ángel Esteve-Selmaa

Department of Ecology and Hydrology, University of Murcia, Campus de Espinardo, 30100 Murcia, SpainDepartment of Applied Biology, University of Miguel Hernández, Edificio Torreblanca, Avd. de la Universidad s/n, 03202 Elche, Alicante, Spain

r t i c l e i n f o

rticle history:vailable online xxx

eywords:ndicators dynamic integrationnsular systemsntegral modelsocio-ecological systemsustainability

a b s t r a c t

The sustainability assessment on socio-ecological systems requires a systemic perspective in order toaddress the close relationships between the environmental and socio-economic processes. This need isespecially urgent in the case of arid insular systems where limiting factors, as land and water resources, aremore evident. The hyperarid island of Fuerteventura (The Canary Islands, Spain) represents a challengingcase due to the need for compatibilizing the rising tourist development with the sustainable managementof its natural resources, highly vulnerable due to processes such as the degradation of natural habitats –which hosts endemic and endangered species – or the high dependence of allocthonous energy sourcesfor basic processes, including water supply.

In this work we present an integral dynamic model, the Fuerteventura sustainability model (FSM),tested and calibrated for 1996–2011 period. The FSM allows to understand the main components of thissocio-ecological system and their changes along time, as well as the interaction between the includedsustainability indicators and other factors within the system. Results have shown the existence of poten-tial trade-offs not only between socioeconomic development and conservation options, but also betweensustainability goals under different management options. The conservation of the Houbara habitat mightrequire the elimination of traditional agro-systems restoration plans, although these agro-systems offerimportant environmental functions. Besides, a reduction of cattle herd in order to control the degradation
of high quality vegetation might negatively affect the endangered population of scavengers on the island.The water–energy binomial offers another trade-off regarding sustainable development, due to the strongdependency of the water availability on energy consumption. In this sense, the FSM has shown to be auseful tool to improve the comprehensive diagnosis of the system and to identify trade-offs betweensustainability indicators to orientate management policies for this insular socio-ecological system.
© 2014 Elsevier B.V. All rights reserved.

. Introduction

The analysis of a socio-ecological system (SES1) should be tack-ed from a holistic, systemic perspective that enables an integratedssessment of socioeconomic and ecological factors and the linearnd nonlinear interactions and feedbacks, which characterize com-lex socio-ecological systems (Lacitignola et al., 2007; Halliday and

Please cite this article in press as: Banos-González, I., et al., Dynamic isystems. Ecol. Model. (2014), http://dx.doi.org/10.1016/j.ecolmodel.2

laser, 2011).The application of this systemic perspective for sustainability

ssessment on insular socio-ecological systems has an increasing

∗ Corresponding author. Tel.: +34 868 88 8111; fax: +34 868 88 3963.E-mail address: [email protected] (I. Banos-González).

1 SES: socio-ecological systems.

ttp://dx.doi.org/10.1016/j.ecolmodel.2014.08.014304-3800/© 2014 Elsevier B.V. All rights reserved.

interest (Patterson et al., 2004; Aretano et al., 2013), due to its largepotential as observatories of sustainability, where the narrow inter-action between ecological aspects and socioeconomic processesis explicitly acknowledged. Regarding sustainability analysis andmodelling, two advantages have been identified in the case of insu-lar systems (Jørgensen, 2013; Petrosillo et al., 2013): (i) an easieridentification of flows, facilitating the quantification of sectors andvariables and (ii) insular systems allow to visualize the existenceof physical limits and carrying capacity and to set sustainabilitythresholds.

Indicators are an essential component of sustainability assess-

ntegration of sustainability indicators in insular socio-ecological014.08.014

ment. Despite of this potential, sustainable indicators have had amoderate influence on the adoption and assessment of sustainablepolicies and practices (Hukkinen, 2003; Levrel et al., 2009; Kajikawaet al., 2011). Among other reasons, the use of static catalogues

dx.doi.org/10.1016/j.ecolmodel.2014.08.014


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ARTICLE IN PRESSG ModelECOMOD-7294; No. of Pages 15

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ventur

obt

spttMsvotSsie

mS(rfrsii

dataa

Fig. 1. Study area: Fuerte

f indicators, which do not consider the dynamic interrelationsetween the relevant processes, represents one of the most impor-ant limitations in their application.

In order to overcome some of these limitations, this workuggests the use of system dynamics modelling tools, since theyrovide a framework for the development of sustainability models,hanks to their capacity to conceptualize the complex interrela-ions of these SES (Bérard, 2010; Blanco, 2013; Wei et al., 2013).

oreover, the proposed methodological approach integrates theustainable indicators into the system dynamic models (SDMs2) toisualize their change along time and to assess how any variationn one indicator may lead to a series of responses on other indica-ors (Lacitignola et al., 2007; Jin et al., 2009; Liu et al., 2014). Besides,DMs represent useful learning tools that enhance system under-tanding and facilitate involvement of non-technical stakeholdersn the decision making processes (Costanza and Ruth, 1998; Kellyt al., 2013).

In this work we present a dynamic model to contribute to aore balanced and multifunctional development of one insular

ES: the Fuerteventura sustainability model (FSM3). FuerteventuraThe Canary Islands, Spain) represents one of the most arid envi-onments in Europe, with a very low productivity and a particularauna and flora with numerous endemic species, threatened by theecent tourist activities. This hyperarid and insular socio-ecologicalystem represents a challenging case of study in order to compat-bilize the tourist development with a sustainable management ofts natural resources.

The specific aims of this work are: (i) to develop an integralynamic model of Fuerteventura island, which collects the factors


nd key processes of the socio-ecological system; (ii) to includehe most relevant sustainability indicators in the FSM and (iii) tonalyze the main changes and interactions between those factorsnd indicators.

2 SDMs: system dynamic models.3 FSM: Fuerteventura sustainability model.

a (Canary Islands, Spain).

1.1. Study case

Fuerteventura, situated in the Canary Islands, Spain (between28◦45′04′′ and 28◦02′16′′ north latitude, and between 13◦49′12′′

and 14◦30′24′′ west longitude), has an area of 1655 km2 (Fig. 1).It has a desertic hyperarid infra-thermomediterranean climate(Torres Cabrera, 1995), with an average annual rainfall of 120 mm.

The vegetation is dominated by xerophytic scrubs andannual grasslands frequently degraded due to goats overgrazing(Rodríguez-Rodríguez et al., 2005; Schuster et al., 2012). Neverthe-less, the insular character favours a wide variety of endemic plantand animal species, with around a 5% of endemism (Arechavaletaet al., 2010; Scholz and Palacios, 2013).

The island is characterized by cultural landscapes marked bythe aridity and the traditional management of water and land. Inthe last decades, the traditional productive activities (ranching,artisanal fishing and non irrigated land farming in gavias) havebeen mainly substituted by tourist and related activities. Therefore,tourism represents the main driving force of the socioeconomic andenvironmental changes in the island in the last years (Fernández-Palacios and Whittaker, 2008; Santana-Jiménez and Hernández,2011), leading to the emergence of new socio-ecologic require-ments, which should be addressed.

Fuerteventura was declared as Biosphere Reserve by UNESCOin 2009. The Action Plan of the Fuerteventura Biosphere Reserve(AP4) represents a guide to achieve a series of sustainability goals(AP, 2013) and constitutes one of the bases for this work.

2. Methodological approach

System dynamics models allow to understand the structure and


behaviour of complex systems, by means of the causal relation-ships, feedback loops, delays and other processes of the system(Kampmann and Oliva, 2008; Li et al., 2012; Martínez-Moyano and

4 AP: Action Plan of the Fuerteventura Biosphere Reserve.


ARTICLE ING ModelECOMOD-7294; No. of Pages 15

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Rled

oMaoe2

2

bdttimittac(tawtaFfc

3

3

B

Fig. 2. Simplified diagram of the methodological approach.

ichardson, 2013). According to Kelly et al. (2013), SDMs are usefulearning tools that help improve system understanding and knowl-dge integration for modellers and end users, and facilitate theevelopment of integral models.

Moreover, SDMs have been revealed very useful in the studyf a wide number of SES (Martínez-Fernández et al., 2000;artínez-Fernández and Esteve-Selma, 2004; Pérez et al., 2012)

nd, specifically, to facilitate the seek of an integrated managementn insular SES (Patterson et al., 2004; Chang et al., 2008; Gonzálezt al., 2008) and to monitor sustainability indicators (Feng et al.,012; Vidal et al., 2013).

.1. Modelling process

The iterative process to elaborate the Fuerteventura sustaina-ility model started with the development of a conceptual model,etermining the factors and key processes of the sustainability ofhe system, their interactions and feedbacks (Fig. 2). The concep-ualizing phase was carried out from the results of a workshopn the framework of the XIth. Atlantic Conference of the Environ-

ent, in which the most relevant themes for sustainability weredentified with the participation of an experts panel. In relation tohese results, a set of sustainability indicators was integrated inhe model in order to facilitate the diagnosis, analyze the progressnd open challenges for the sustainability of the island. The indi-ators derive from a proposal of the Cabildo – the island councilCáceres, 2010) – and the Scientific Committee of the Fuerteven-ura Biosphere Reserve (pers. com.), in line with the sustainabilityims of the AP (2013). Then, all model variables and parametersere defined and formulated starting from scientific literature and

he available information. For parameters with no available data,n automatic calibration process was carried out (Oliva, 2003). TheSM was calibrated for the 1996–2011 period, using 20 variablesor which available observed data exist. Several model testing pro-edures were then applied, as it is explained in detail later.

. Results


.1. Model description

The model is structured in 5 sectors (Socio-tourist, Land Uses,iodiversity, Environmental Quality and Water Resources), and

PRESSodelling xxx (2014) xxx–xxx 3

includes 375 variables, 21 of them are state variables (Appendix A) –accumulations in the system (Saysel and Barlas, 2001) – and 34 rep-resent environmental and socio-economic sustainability indicatorswhich were integrated in the model (Table 1).

3.1.1. Socio-tourist sectorTourism represents the main driving force of the employment

and wealth generation in Fuerteventura. One of the key factors isthe tourist equivalent population (etp), expressed as a function ofthe total annual tourist arrivals and the length of stay, which allowsto asses the pressure of the tourism over the territory and the nat-ural resources, independently of the seasonality (Patterson et al.,2008; BPIA, 2012). Its modellization (Eq. (1)) includes the touristchoice of destination (Hyde and Laesser, 2009) which is calculatedbased mainly on: (i) GDP evolution of the most important marketsfor outbound tourism for the island (Zhang and Jensen, 2005; Garín-Munoz, 2006); (ii) the tourist accommodation offer (Cruz, 2009) –in relation to the occupancy rate and the reference capacity – beingthe tourist accommodation capacity a state variable; and (iii) touristattraction index (Santana-Jiménez and Hernández, 2011; Wei et al.,2013), based on three aspects: the available beach per capita, thenatural vegetation factor and the tourist prices index of the island.Likewise, the effect of the called Arab Spring has been consideredas an exogenous shock over the tourist arrivals (Canalis, 2013):

etp = etpi · gdpf · ae · bpc · nf · tpif · as · tcif (1)

where etpi is the initial value of etp; gdpf represents a factor basedon the GDP of the most important markets for outbound tourism;ae is referred to the accommodation offer effect; bpc representsthe beach per capita factor; nf means the natural vegetation factor,which is the ratio between the actual and the initial area covered bynatural vegetation; tpif is referred to the tourist prices index fac-tor; as is the Arab Spring effect; and tcif represents an automaticcalibration parameter.

The model includes another state variable: the resident pop-ulation (Fig. 3). The migratory flows are strongly influenced bythe employment offer in the tourist activities, which represents anaverage of 33% of the total employments in Fuerteventura (ISTAC,2012a). Thus, the increase in etp leads to a raise in tourist employ-ment and in other productive branches of the economy of the island,which has favoured a total population growth and the demandof tourist and residential accommodations. This fact could affectsome natural resources and services, eroding some aspects of thetourist attraction index, as the beach per capita factor or the naturalvegetation factor. Thereby, the tourist choice of destination couldbe negatively affected even in destinies in development phase,as Fuerteventura Island, according to Butler’s tourism destinationcycle (Patterson et al., 2008). This example highlights the impor-tance of the internal component given by feedback loops insidethis sector, despite of the fact that tourist dynamic is largely drivenby exogenous factors (von Bergner and Lohmann, 2014).

3.1.2. Land uses sectorThis sector considers different uses of the land and their main

changes along time. The included 12 state variables are gathered in3 categories (Fig. 4): urban uses and infrastructures (residential,hotel and non-hotel tourist accommodation, golf courses, roadsand tracks), agricultural (irrigation, active gavias and abandonedgavias) and natural, where high quality and low quality vegeta-tion areas are considered, in terms of the potential (non altered)and actual vegetation according to Canary Islands vegetation map(GRAFCAN, 2011; del Arco et al., 2010). The protected areas and the


Marine-Terrestrial Public Domain is considered in the model as nontransformable high quality vegetation.

The increase in tourist and resident population has trig-gered the rise in built-up land – the area occupied by urban




Table 1Sustainability indicators integrated in the FSM.

Sectors Indicator Units

Socio-tourist Population growth rate %Occupancy rate %Tourist attraction index Dimensionless (dmls)Tourist choice of destination dmlsTourist accommodation capacity no. tourist accomod.Tourist employment employmentsResident-tourist ratio %

Land uses Built-up land proportion %Non protected area with high environmental functionality %Landscape indicator %High quality vegetation area HaOvergrazing indicator dmlsRoads density km/km2

Beach per capita m2/inhab

Biodiversity Houbara habitat Ha/yearEgyptian vulture population ind/yearKey species deaths by electrocution ind/yearProtected area proportion %

Environmental quality Motorization index vehicle/inhab/yearEnergy self-sufficiency dmlsPrimary energy consumption kw/inhab/yearElectric energy consumption kw/inhab/yearUSW generation kg/yearSelective waste management index kg/yearReciclyng rate of waste extracted from mix %Waste neither reused nor recicled kg/year

Water resources Resident water consumption m3/inhab/yearTourist water consumption m3/inhab/yearTotal gross water demand m3/yearPercentage of waste water treatment %Percentage of waste water reused %Energy consumption in seawater desalination kWh/yearLosses in water distribution network %

3

btgtlotG

Aquifer recharge

uilt-up and infrastructures (roads and tracks). Besides, Fuerteven-ura attends the gradual loss of traditional agro-systems, calledavias, whose abandonment gives way to irrigated crops. Never-heless, gavias offer important environmental functions, such as


andscape enhancement, increased rates of aquifer recharge andrganic nutrients and water retention (Díaz et al., 2011). That is whyhe Cabildo has promoted the implementation of an Abandonedavias Restoration Plan (Fuerteventura Cabildo, 2009).

Fig. 3. Simplified stock and flow diagram of the socio-tourist sector

m /year

As mentioned before, the island is facing a vegetation degrada-tion problem. Some authors suggest that grazing is the main cause(Gangoso et al., 2006; Nogales et al., 2006; Schuster et al., 2012),whereas others state that grazing is highly desirable for the main-


tenance of certain species, thoroughly adapted to the presence ofthis ungulates (Arévalo et al., 2007; Fernández-Lugo et al., 2013).In our model the overgrazing effect on the high quality naturalvegetation was formulated taking into account, on one side, the

. The variables in grey colour belongs to other model sectors.



I. Banos-González et al. / Ecological Modelling xxx (2014) xxx–xxx 5

ow dia

mloFcdtowpp

o

wilr

ia

l

wvmt

Fig. 4. Simplified stock and fl

aximum-sustainable-stocking rate capacity offered by the insu-ar territory, highly dependent of annual rainfall; and, on thether side, the proportion of livestock which actually grazes inuerteventura. When this overcomes the sustainable stocking rateapacity, the overgrazing indicator reaches values over 1, and theegradation of the high quality vegetation occurs (Eq. (2)). Dueo the fact that potential effects of this degradation (such as lossf aerial biomass, reduction of seeds and sprouts production, etc.)ill not be recovered immediately after the impact, the period ofersistence of the effects was set up by an automatic calibrationrocess around 4 averaged years:

i =(

ls · ngp

rf · src

)(2)

here oi is the overgrazing indicator; ls is the livestock of thesland; ngp is the net grazing proportion, this is the proportion ofivestock needs which is not covered by supplementary food; rfepresents the rainfall, src is the sustainable stocking rate capacity.

Another sustainability indicator of this sector is the landscapendicator (Eq. (3)), which includes the positive aesthetic value ofctive gavias.

i =(

hqv + gavbu + in

)(3)


here li is the landscape indicator; hqv refers to the high qualityegetation area; gav means the area occupied by active gavias; bueans the urban built-up area; in is the area occupied by infras-

ructures (roads and tracks).

gram of the land uses sector.

Two secondary succession processes are included in the model:first, the succession produced after the abandonment of agriculturalareas; and second, the succession from low quality to high qual-ity natural vegetation, which is much slower due to the hyperaridcharacteristics of the island.

3.1.3. Flagship species sectorIn this version of the FSM, this sector (Fig. 5) is focused on

two endangered species included in the National Catalogue ofThreatened Species (BirdLife, 2004; Lorenzo, 2004) and endemicsubspecies of The Canary Islands: the Canarian Houbara Bustard(Chlamydotis undulada fuerteventurae) and the Egyptian vulture(Neophron percnopterus majorensis). Both are very representativeanimal species of the island with a specific reference in the ActionPlan of the Biosphere Reserve; therefore it is important to know towhat extent changes which took place on the island have affectedboth species in the last decades. Although the use of these keyspecies does not guarantee the conservation of the species rich-ness (Carrascal et al., 2012), they are considered as flagship specieswhich may facilitate the social support to biodiversity conserva-tion policies (Walpole and Leader-Williams, 2002; Verissimo et al.,2011).

The habitat loss is the main threat factor for Canarian HoubaraBustard population on the island (Carrascal et al., 2008; Schuster


et al., 2012). The two state variables which represent the potentialhabitat in the model, primary and secondary habitats – differenti-ated by the Houbara density they have – are affected by the increasein urban areas and infraestructures related to tourist and urban




diagra

drwhH

ii((e

Fig. 5. Simplified stock and flow

evelopment. The threatening factors for the habitat (urban uses,oads, tracks and active crops) and their specific ratios of changeere defined according to Carrascal et al. (2008). On the otherand, the abandoned gavias constitute the secondary habitat of theoubara.

The population of Egyptian vulture was modelled consider-ng denso-dependence factors and the effect of livestock, which


ncreases the island carrying capacity to host this scavenger (Eq.4)). Its main threat factors are poisonings and electrocutionsDonázar et al., 2002; Palacios, 2004). The factors which influ-nce the electrocution probability were also considered, including

Fig. 6. Simplified stock and flow diagram

m of the flagship species sector.

stochastic and determinist components, such as implementing cor-rective measures in power lines. The change in the Egyptian vultureis expressed as:

ech =(

ev · mir · k + kls − evk + kls

)− ((ep · pli · ev + fstk) + pos) (4)

where ech is the annual change in Egyptian vulture population;


ev represents the population of the Egyptian vulture; mir is themaximum or intrinsic growth ratio for the Egyptian vultures; k isreferred to the Egyptian vulture carrying capacity without consid-ering the livestock effect; kls is the additional carrying capacity

of the environmental quality sector.




diagra

gefp

3

sdicTtmtdCt

staus

e

wtpMfi

Fig. 7. Simplified stock and flow

enerated by the existence of livestock; ep means the probability oflectrocution; pli concerns the length of power lines on the island;

stk represents the stochastic factor included in the electrocutionrobability; pos refers to poisonings.

.1.4. Environmental quality sectorThis sector encompasses both the energy generation and con-

umption, and the waste production and management (Fig. 6). Theifferent energy demands – from tourist equivalent population, res-

dent population, transport, productive activities and the energyonsumed by the desalination process – were taken into account.he increase in the regional GDP leads a rise in the per capita elec-ric consumption ratio, as well as in the motorization index. The

odel also allows to calculate the energy self-sufficiency indica-or (Eq. (5)) in order to analyze the proportion of the total energyemand covered by renewable resources (Denis and Parker, 2009;hester, 2010), which on the island are, mainly: wind power, solarhermal and photovoltaic.

Regarding the urban waste management, the efficiency of theeparation and the recycling, and the quantity of wastes left inhe dump were considered. By means of the selective waste man-gement index we may analyze the proportion of the generatedrban waste which is actually recycled, as a key component of theustainability of the island (Cáceres, 2010):

ss = wp + thp + php

pep + pet + pei + ped(5)


here ess is referred to the energy self-sufficiency indicator; wp,hp and php are the energy produced by renewable resources: windower, thermal power and photovoltaic power, respectively (inwh/year); pep, pet, pei and ped are the primary energy demand

rom population (resident and tourist), transport, productive activ-ties and the seawater desalination, respectively (in Mwh/year).

m of the water resources sector.

3.1.5. Water resources sectorIn the case of Fuerteventura island, water resources scarcity

traditionally represented one of the limiting factors for the islanddevelopment and, particularly, its tourist development. Neverthe-less, the technological advances in relation to seawater desalinationhave favoured the overcome of this key limitation in a hyperaridisland.

This model sector consists of 3 state variables: groundwater,surface water and the reservoir capacity (Fig. 7). The total grosswater demand indicator has been built taking into account thedifferentiated demands of: livestock, irrigation, golf courses, res-ident and tourist consumption (Eq. (6)). The surface resources arenot enough to satisfy the increasing population demands or theirrigation requirements. The groundwater resources, dominantlybrackish (Herrera and Custodio, 2000), are aimed at agricultural andfarming uses, which must be desalinated before being used. Thisgives an idea of the importance of the roll played by the desalina-tion to cover the total water demand (Custodio and Cabrera, 2013),as well as the importance of the water supply on the sustainabilityof a tourist island (Deyà and Tirado, 2011):

gwd =n∑

i=1

(hi · di) +m∑

j=1

(sj · rj + lj) +p∑

k=1

(ihk · ck + fk) (6)

where gwd is the total gross water demand indicator; hi is the num-ber of heads of n number of i class of livestock (caprine, ovine,bovine and porcine); di means the water consumption rate of eachclass of livestock i (m3/cattle head); sj refers to the area (in hectares)of m number of j land uses (irrigation crops and golf courses); rj


is the water consumption of each j land use (in m3/ha); lj meansthe conveyance losses of each j land use; ihk is the number ofinhabitants of p number of k groups (resident and tourist equiva-lent population); ck means the water consumption of each k group


ARTICLE ING ModelECOMOD-7294; No. of Pages 15

8 I. Banos-González et al. / Ecological M

Table 2Summary of the main results of the goodness of fit. Detailed results for each variablemay be consulted in Appendix B.

MAPE (%) NRMSE (%)

Average error value 6.67 8.38

No. variables included in the intervals<10% 15 13

(p

3

1ews

nptrecaig

citMe(at

N

wvr

sMi

bLbo2o

le

10–20% 4 520–30% 1 2

in m3/inhabitant); fk is referred to the water distribution and trans-ort losses (in m3/year).

.2. Model testing

A set of model testing procedures was applied (Barlas,996), including: dimensional consistency test, sensitivity analysis,xtreme conditions test and goodness of fit test for the 20 variablesith available observed data series. The model successfully passed

uch testing procedures.The sensitivity analysis, very useful to assess the model robust-

ess (Loehle, 1997; Graham et al., 2002), was carried out on thearameters set by automatic calibration, of which only one – relatedo the tourist choice of destination – showed a high sensitivity. Inelation to extreme condition tests (Li et al., 2012), the model gen-rates the expected results when it is subjected to several extremeonditions such as an unexpected drop of the tourist arrivals, anccelerated demand of built-up land, extreme droughts, total elim-nation of the Abandoned Gavias Restoration Plan, or an increase inrazing.

The comparison of the simulation results to the observed dataonstitutes a measure of the goodness of fit and, therefore, the abil-ty of the model to track the actual behaviour of the system ando capture its key questions (Solecki and Oliveri, 2004; Martínez-

oyano and Richardson, 2013). The normalized root mean squarerror (NRMSE, Eq. (7)), calculated according to Andarizan et al.2011), Ganderson and Price (2012) and Sepaskhah et al. (2013),nd the mean absolute percentage error (MAPE, Eq. (8)), accordingo Goh and Law (2002) and Oliva (2003), were determined:

RMSE = 1

A

√√√√1n

n∑t=1

(St − At)2 (7)

MAPE = 1n

n∑t=1

∣∣∣St − At

At

∣∣∣∣∣ (8)

here A is the observed average value, n is the number of obser-ations and St and At the simulated and observed value at time t,espectively.

Results for the 20 variables with available observed series showimilar values for both statistics. Table 2 shows the average values ofAPE and NRMSE for the 20 variables, and the number of variables

ncluded in the intervals, according to the goodness of fit results.A total of 15 variables have a mean absolute percentage error

elow 10%, which is considered an excellent degree of fit (Goh andaw, 2002), whereas 4 variables achieve a good degree of fit (MAPEetween 10 and 20%). Only one variable, immigration, has a degreef fit only acceptable according to these authors (MAPE between0 and 30%), which might be related to the lack of reliability of this


bserved data series.Regarding the NRMSE calculation, 13 variables present an excel-

ent degree of fit according to Andarizan et al. (2011) and Sepaskhaht al. (2013), who state the same intervals, 5 variables achieve a

PRESSodelling xxx (2014) xxx–xxx

good degree of fit, and 2 variables achieve an acceptable degree offit: immigration and golf courses.

It can be concluded that the results of the model testing proce-dures point to a high degree of fit between simulation results andobserved series, which supports the ability of the model to trackthe behaviour of the SES of Fuerteventura.

3.3. Simulation results

The model testing results offer an adequate degree of modelconfidence to use it as a tool to analyze the changes in the mainsustainability issues of Fuerteventura.

Regarding the socio-tourist sector, the tourist equivalent pop-ulation (etp) shows a rising trend during most of the simulationperiod (Fig. 8a). Nonetheless, since the economic crisis began in2008, with a major impact on GDP factor, a sharp drop of thetourist arrivals in 2009 was produced and, therefore, a fall of thetourist employment (Fig. 8b). Since then, several factors have driventhe recuperation of the etp and the occupancy rate (Fig. 8c): (i)the beginning of the economic recovery in the main markets thatprovide tourists bound to Fuerteventura; (ii) the contraction oftourist prices on the island; and (iii) the consequences of the ArabSpring revolts on the tourism. Nevertheless, the recovery of theemployment has not been as immediate as the tourist arrivals. Infact, the recession has produced a deep change on job creation, witha reduction of jobs per tourist ratio.

The tourist activity is one of the key factors in the extraordi-nary population growth which has taken place in Fuerteventura.The resident population has doubled in only 10 years (1996–2006),beating 100,000 residents in 2011 (Fig. 8d). This trend is not onlyexplained because of the strong vegetative growth (with a birth rateover the national average, Fig. 8e), but it is mainly due to a positivemigratory flow (Fig. 8f) driven by the increase in the employment(Santana-Jiménez and Hernández, 2011). This trend in the touristactivity of the island has also triggered the offer of tourist accom-modations, which has almost tripled during the simulation period(Fig. 8g).

The rise in both resident and tourist equivalent population rep-resents a driving factor for the land uses sustainability indicators.Among them, the proportion of built-up land respect to the totalinsular area can be highlighted (Spilanis et al., 2009; BPIA, 2012),since land uptake constitutes one of the changes promoting unsus-tainable processes at broad scales, despite the apparently modestvalues respect to total land. Even though the proportion of built-up land does not exceed 6% of the total island surface, the urbanbuilt-up area has tripled along the simulation period (Fig. 9a).

On the other hand, a more sustainable and efficient use of landrequires the maintenance of environmentally active natural andrural systems. According to this aim, which is explicitly addressed inthe Fuerteventura Biosphere Reserve Action Plan, the FSM includesthe proportion of active gavias, considered as a key indicator.Socioeconomic changes have led towards a progressive abandon-ment of gavias. This trend has suffered an important change since2002, due to the timely plans of gavias restoration. Without theseplans, the area of active gavias would have been around 50% less atthe end of the simulation period (Fig. 9b). Despite the increase ofthe area of active gavias, the landscape indicator tends to decreasemainly due to the increase in the proportion of the built-up land.

Fuerteventura still maintains a high proportion of the insulararea covered by natural uses. Along the simulation period, the netloss of natural land (both high quality and low quality vegetation)is 5324 ha, which means around 3.5% of the initial value. However,


the Scientific Committee of the Reserve (pers. com.) stands that thedegradation of the vegetation represents one of the most worryingprocesses. In particular, some authors suggest that grazing could beone of the drivers of this degradation (Gangoso et al., 2006; Nogales




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ig. 8. Observed data and simulation results between 1996 and 2011. (a) Tourist equccommodation). (d) Resident population. (e) Births. (f) Immigration rate. (g) Touri

bserved data source: ISTAC.

t al., 2006; Schuster et al., 2012). According to the simulationsesults, the model does not support the existence of a continuedegetation degradation caused by the livestock during this period,ince the fact that overgrazing indicator maintains, in general, val-es below 1 (Fig. 9c). In contrast, it seems that, during especially

ntense droughts, such as the one that took place between 2009nd 2010, there would be a degradation of high quality vege-ation due to overgrazing, whose effects might remain for someears.


Fig. 9 also shows the comparison between observed data andimulation results for the area occupied by: roads and tracksFig. 9d), the total natural vegetation and crops (Fig. 9e) and therrigated lands and golf courses (Fig. 9f).

t population (etp). (b) Tourist employment. (c) Occupancy rate (hotel and non-hotelmmodation.

The effects of land uptake and fragmentation represent one ofthe main threatening factors for the biodiversity of the island and,in particular, for the potential habitat of the Canarian Houbara.Simulation results show an adequate fit to estimated values fromliterature and available cartographic information (years 1996, 2002and 2010). Fig. 10a shows the reduction of the potential Houbarahabitat, as a result of disturbances caused mainly by the rise in theland uptake. The decrease in abandoned gavias, mainly due to therestoration plan, has also favoured the habitat loss since abandoned


gavias constitute secondary habitat for Houbara.The population of the Egyptian vulture has increased during the

simulation period, directly related to the rise in cattle herd andto the reduction of the electrocutions since 2006, thanks to the




Fig. 9. Observed data and simulation results between 1996 and 2011. (a) Urban built-up and built-up land proportion (second axis). (b) Active gavias area and landscapeindicator (second axis). (c) Simulation results between 1996 and 2011 of high quality vegetation and overgrazing index (second axis). (d) Roads and tracks. (e) Total naturalv

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egetation and crops (second axis). (e) Irrigated lands and golf courses.

bserved data source: ISTAC, Cadastre (2012) and GRAFCAN (2011).

mplementation of management measures aimed at decreasing theortality in power lines. As shown in Fig. 10b, without a rise in the

ivestock grazing on the island, the number of Egyptian vulturesould have been around 33% smaller at the end of the simulationeriod.

In relation to the energy issues, the total electric energy con-


umption has increased (Fig. 11a), due to the rise in the per capitalectric consumption ratio related to the regional GDP and anncrease in the total population (both resident and tourist equiv-lent). Likewise, the rise in the regional GDP is also related to the

ig. 10. Observed data and simulation results between 1996 and 2011. (a) Houbara poulture population and simulation results of the Egyptian vultures under the hypothesis

bserved data sources for Houbara potential habitat estimated from: 1996, Lorenzo et aources for Egyptian vulture population: 1998, Palacios (2000); 1999–2001, Donázar et a

vehicles fleet (Fig. 11b) and, therefore, the motorization index andthe transport energy demand.

Fig. 11c shows a decrease in the energy self-sufficiency indicatoralong the simulation period since, despite some moderate rise inrenewable energy sources in Fuerteventura, the increase in the totalprimary energy demand has been much higher.


Regarding water resources, there is a noticeable lack of observeddata series. Nevertheless, overvalues and ranges of simulationresults are consistent with the available scattered informa-tion. Fig. 12a shows the demands from the considered sectors:

tential habitat and change in abandoned gavias (simulation results). (b) Egyptianof no rise in grazing.

l. (2004); 2002, Carrascal et al. (2008); 2010, Schuster et al. (2012). Observed datal. (2002); 2002–2007, Díez et al. (2008); 2008–2010, Mallo and Díez (2009, 2010).




Fig. 11. Observed data and simulation results between 1996 and 2011. (a) Electric energy consumption. (b) Vehicles fleet. (c) Simulations results of the total primary energydemand and the energy self-sufficiency indicator.

Observed data source: ISTAC (2012b) and Special Territorial Plan for Energy Facilities Management (PTEOIEFV, 2008).

Fig. 12. (a) Gross water demand per sectors. Simulations results for period 1996–2011 (b). Net water demand by resident population. Observed data and simulation resultsbetween 1996 and 2011. (c) Total demand and available water per source. Simulations results for period 1996–2011. Where: Tot demand: total gross water demand of alls ing frow

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ectors; desalination: seawater desalination; surface: regulated surface water comater plus groundwater pumping.

bserved data source: CIAFV (2013).

opulation (resident and tourist equivalent) represents the biggestroportion of the demand, the 69% of the total demand (around2.5 Hm3 in 2011). The net consumption per resident was 180 l pererson and per day (Fig. 12b); while tourists consumed around 378 lnd 221 l per person and day in hotels and non-hotel tourist accom-odations, respectively (CIAFV, 2009). Golf courses, irrigation and

ivestock demands correspond to around 2.31 Hm3, 3.45 Hm3 and.23 Hm3 at the end of the simulation period, respectively. Fig. 12bhows the total water demand and the different water sources. Sur-ace water and groundwater pumping are clearly insufficient to fithe total water demands, covering around 20% in average. Thereforeeawater desalination is required to satisfy the remaining demand.

. Discussion

The Fuerteventura sustainability model allows to understandhe main components of this socio-ecological system and theirhanges along time, as well as the synergies and interactionsetween sustainability indicators and other factors, which mayelp to improve the diagnosis and decision-making processes asell as the assessment of sustainable policies.

Regarding the flagship species sector, the model has allowedo analyze the change in two key endangered species, linked to the


ynamics of their main threatening factors. This analysis is requiredo develop strategies for their protection (Feld et al., 2010). In ordero reduce the Houbara habitat loss, one of the measures which coulde considered might be the elimination of the Abandoned Gavias

m the reservoir; groundwater: total groundwater pumping; Surf+Ground: surface

Restoration Plan, since abandoned gavias are part of its secondaryhabitat. Nevertheless, active gavias constitute a traditional agro-system which positively contributes to the scenic quality of thelandscape, whereas their morphology favours the organic nutri-ents and soil water content, contributing to the natural fertilizationof the crops and the aquifer recharge (Hernández-Moreno et al.,2007; Díaz et al., 2011). The simulation results (Table 3) sup-port the existence of some trade-offs between environmental aimsunder the same management measure, in which the optimizationof some aims implies the reduction of others (MEA, 2005; Rodríguezet al., 2006; Vidal et al., 2013). Although the impact of the Restora-tion Plan is limited during the study period (around 400 restoredhectares) and does not imply noticeable changes in the mentionedindicators, this trade-off might become of concern under moreextensive plans of gavias restoration. Therefore, the developed FSMcould be useful to quantify the relative magnitude of these andother trade-offs.

Many experts point to the urgent need for a stronger control onthe loss of high quality vegetation. Some authors claim that graz-ing could be one of the triggers of the degradation in The CanaryIslands (Nogales et al., 2006; Garzón-Machado et al., 2010; Schusteret al., 2012); whereas others state that grazing is highly desirablefor the maintenance of certain species, adapted to the presence


of these ungulates (Arévalo et al., 2007; Fernández-Lugo et al.,2013). In relation to this issue, the model results do not supportthe existence of a continuous overgrazing on the island. However,during severe droughts, the grazing requirements do exceed the




Table 3Comparison of results for 6 sustainability indicators (gavias, landscape indicator, aquifer recharge, net grazing proportion, average overgrazing index in drought period(2009–2010), high quality vegetation area and Egyptian vulture population) under the base simulation (column 1), expected results if no gavias restoration is implemented(column 2) and if no grazing rise takes place, which would suppose that net grazing remains at 3500 LU, as at the beginning of the simulation period, instead of around 7700LU (column 3).

Indicators Measures

Base simulation Simulation without gavias restoration Simulation without grazing rise

Gavias (Ha) 453.23 150.78 453.23Landscape indicator (dml) 2.68 2.64 3.34Aquifer recharge (Hm3) 12.63 12.13 12.63Overgrazing index in drought period (dml) 1.60 1.60 0.77High quality vegetation (Ha) 22,875 22,905 28,661

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Egyptian vulture population (no.) 203

tocking rate capacity and, therefore, the vegetation degradationakes place. In order to avoid these potential episodes, whose effectsver the vegetal species composition and the landscape indica-or may remain for some years, some authors claim the need for

easures to control the livestock. Nonetheless, a mere cattle herdeduction might lead to negative impacts on the insular populationf scavengers, such as the endangered Egyptian vulture (Donázart al., 2002; Gangoso et al., 2006). As Table 3 shows, if the graz-ng had not increased during the study period, the Egyptian vultureopulation would not have exceeded 140 specimens, which woulduppose a more critical threat status and more expensive conser-ation measures would be necessary to avoid its extinction.

The water–energy binomial offers another trade-off regardingustainable development. Whereas the seawater desalination, theain source of water on the island, has enabled to overcome

he limitations of water scarcity on the socioeconomic activi-ies, its negative side – a high energy consumption, an increasednergy dependence and greenhouse and brine emissions – must beddressed (Meerganz von Medeazza and Moreau, 2007; Lattemannnd Höpner, 2008; Melián-Martel et al., 2013), particularly in annsular system with a low and decreasing self-sufficiency indica-or, as aforementioned (Fig. 11c). This dependence on allocthonous,on renewable energy resources on Fuerteventura is rising, whichepresents a clear sign of unsustainability. Even more, the strongependency of water availability on energy consumption – 80% ofotal water demand is covered by seawater desalination – implies

high vulnerability of the whole socio-ecological system, even forasic needs, to socioeconomic changes such as those in the energyolicies and markets, and to the ongoing global change (Kruyt et al.,009).

The existence of potential trade-offs between environmentalims as well as between socioeconomic development and conser-ation options, as described above, should be taken into account inhe decision-making in order to achieve a more sustainable man-gement of any socio-ecological system (Rodríguez et al., 2006; Sut al., 2012; Moeller et al., 2013; Vidal et al., 2013).

The difficulties to achieve the sustainability goals lie on the com-lex cause–effect relations which determine the socio-ecologicalystems behaviour. In this sense, the catalogues of sustainabilityndicators, traditionally applied in a static way (Prescott-Allen,001; Spangenberg, 2002), have important shortcomings, givenheir inability to address this complexity and interactions betweenndicators, which might lead to a biased assessment of the diagnosisnd options. Only coping with this complexity, these trade-offs cane identified and quantified as input for a decision-making process.n this context, SDMs provide a useful tool to improve the integraliagnosis of the socio-ecological problems and, therefore, to reduce


he conflicts between management options (Kelly et al., 2013).This Fuerteventura sustainability model presents some short-

omings. On one side, it has a limited reusability, a principle claimedn environmental modelling activities (Granell et al., 2013), since

203 140

it has been developed using a context-specific approach, as manyother integral models (in the sense of Voinov and Shugart, 2013).However, we do support the need for problem-specific perspec-tives to deal with the complexity of each real SES, as other studieshave shown (Jin et al., 2009; Li et al., 2012; Marín et al., 2012;Martínez-Fernández et al., 2013).

Another shortcoming lies on the lack of a detailed sensitivityanalysis, in order to develop a deep assessment of the sources andthe degree of uncertainty of the main model outcomes, assess-ment which will be addressed in subsequent works. Moreover, anextension of the binomial water–energy issues is needed in orderto improve the diagnosis of this important component of the sus-tainability in Fuerteventura. Besides this, next works will focuson the application of the FSM to explore the system behaviourunder different scenarios and policy options with the final aim ofcontributing to the long-run sustainability of Fuerteventura socio-ecological system.

5. Conclusions

As contribution to the sustainability assessment of socio-ecological insular systems, a dynamic model of the sustainabilityof Fuerteventura (The Canary Islands) has been elaborated and cal-ibrated for 1996–2011 period. Model testing has shown satisfactoryresults, which support the usefulness of the model to analyze thesustainability of this SES. The model has enabled the integration ofthe main sustainability indicators, which facilitates an integral anddynamic assessment. It highlights the usefulness of using dynamictools, as FSM, in order to identify and quantify potential trade-offs,not only between socioeconomic developments and environmentalgoals, but also between different environmental and sustainabilityindicators, trade-offs which may often go unnoticed when only aset of static indicators is used.

Acknowledgements

The authors wish to thank Mr. César Terrer, Mr. Jesus Minanoand Ms. Francisca Carreno for their personal support which allowedus to carry out this research. This work as been developed as partof the project: “An integrated tool for the sustainable managementand the development of an information and participation system inBiosphere Reserves”, funded by Ministry of Industry, Tourism andCommerce. Plan AVANZA COMPETITIVIDAD I+D+I (TSI-020302-2010-16). It has been also supported by IDIGEO Project: “Platformfor Research and Development of Geomatic Information Systems”,


funded by MiCINN Acteparq Year 2009–2011 (PCT-430000-2009-7). We specially thank Interra S.L, main coordinator of both projects,for the support. The manuscript was significantly improved fromthe comments of three anonymous reviewers.


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ppendix A.

tate variables included in the Fuerteventura sustainability model.

State variables Definition Units

Resident population Resident inhabitants inhabitantsTourist accommodation capacity Tourist accommodation capacity (Hotels + non hotels) bedsHotel Surface occupied by hotel accommodations hectares (ha)Non hotel Surface occupied by non-hotel accommodations haResidential Surface occupied by residential and other urban uses haGolf courses Surface occupied by golf courses haTrans hq natural veg Surface occupied by transformable high quality natural vegetation haNotrans hq natural veg Surface occupied by non transformable high quality natural vegetation haLow quality natural vegetation Surface occupied by low quality natural vegetation haAbandoned gavias Surface occupied by abandoned gavias haGavias Surface occupied by active gavias haIrrigation Surface occupied by irrigated lands haRoads Surface occupied by roads haTracks Surface occupied by tracks haPrimary habitat Primary habitat of the Canarian Houbara Bustard haSecondary habitat Primary habitat of the Canarian Houbara Bustard haEgyptian vulture pop Egyptian vulture population Number of Egyptian vulturesGroundwater Groundwater volume m3

Surface water Surface water m3

Reservoir capacity Reservoir capacity m3

chGDPca Cummulated annual change in the Canarian GDP dimensionless

ppendix B.

etailed results of the goodness of fit test for the 20 variables with available observed data series.

Variables n MAPE (%) RMSE (%)

Resident population 16 4.38 5.61Births 12 6.66 8.87Inmigration 16 20.44 21.03Tourist equivalent population 16 9.51 11.64Tourist accommodation capacity 16 7.25 9.21Occupancy rate 16 8.92 11.20Tourist employment 13 4.78 5.95Houbara habitat 3 0.93 1.15Egyptian vulture population 13 6.29 6.47Urban built-up 16 2.97 3.46Tracks 3 0.96 1.53Roads 3 0.78 1.11Active crops area 15 10.14 11.39Irrigated crops area 15 11.75 13.69Active gavias area 15 10.49 11.55Natural vegetation area 3 0.29 0.43Golf courses area 15 10.01 24.45Vehicles fleet 12 4.82 4.97Electric energy consumption 14 6.30 7.84Net water demand by residents 12 5.76 5.97

ppendix C. Supplementary data

Supplementary data associated with this article can be found,n the online version, at http://dx.doi.org/10.1016/j.ecolmodel.014.08.014.

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