Ecological Informatics 14 (2013) 38–43

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Ecological Informatics

j ourna l homepage: www.e lsev ie r .com/ locate /eco l in f

A Model Web approach to modelling climate change in biomes of ImportantBird Areas

Jon Olav Skøien a,⁎, Michael Schulz a, Gregoire Dubois a, Ian Fisher b, Mark Balman c,Ian May c, Éamonn Ó Tuama d

a Land Resource Management Unit, Institute for Environment and Sustainability, Joint Research Centre of the European Commission, 21027 Ispra (VA), Italyb Royal Society for the Protection of Birds, RSPB, The Lodge, Potton Road, Sandy, Bedfordshire, SG19 2DL, UKc BirdLife International, Cambridge, Wellbrook Court, Girton Road, Cambridge, CB3 0NA, UKd Global Biodiversity Information Facility (GBIF), Copenhagen, Denmark

⁎ Corresponding author.E-mail address: jon.skoien@jrc.ec.europa.eu (J.O. Skø

1574-9541/$ – see front matter © 2012 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.ecoinf.2012.12.003

a b s t r a c t

a r t i c l e i n f o

Available online 13 December 2012

Keywords:Web servicesEcological forecastingModel WebBird conservation

Protected Areas (PA) are designated to conserve species and habitats and protect against anthropogenic pres-sures. Park boundaries, however, offer no protection against climatic change and where boundaries are actualconstructions, they may also act as physical barriers to species movements to new suitable habitats. Themeans for assessing the consequences of climate change on ecosystems and for identifying gaps in PA connectiv-ity are therefore a conservation priority. The complexity of the scientific questions raised requires a multi-disciplinary approach given the variety of the information required. This includes species observations andtheir theoretical distributions, aswell as ecosystem assessments and climate changemodels. Such complex ques-tions can be more easily dealt with if there is appropriate access to data and models, a strategy endorsed byGEO-BON, the Group on Earth Observations Biodiversity Observation Network. In this paper, we show howdata andmodels recentlymade available on theWorldWideWeb can be coupled through interoperable servicesand used for climate change forecasting in the context of Important Bird Areas (IBAs) and how, for any bird spe-cies described in the databases, areas can be identified where the species may find a more suitable environmentin the future. As presented, this is an example of the Model Web.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

Species distribution models (SDMs) are typically used for identify-ing the suitability of habitats based on observations of the species anda set of environmental indicators assumed to include the species'niche. There are a range of such models used in ecology (Guisan andZimmerman, 2000). A relatively widely used method is based on theMahalanobis distance to create environmental suitability maps (ESM)(Clark et al., 1993; Knick and Dyer, 1997; Rotenberry et al., 2002).Another method is the MaxEnt method (Phillips et al., 2006) which isbased on the creation of pseudo-absence locations.

Common to these and other models is that they can be used to gen-erate suitability or similaritymapswhich can help in identifying regionswhere a given species is more likely to be observed. These models cantherefore be also used to define those locations where species couldmigrate should their current habitats become unsuitable as a result ofhuman activities.

In addition to modelling the potential distribution of species, suchmodels can also be used for estimating similarities between ecosystemsfound in protected areas and those observed in their surroundings. This

ien).

rights reserved.

approach uses the size of the area with a similarity level above a certainthreshold as a measure of the ecological uniqueness of a national park(Hartley et al., 2007; Nelson et al., 2009).

There are a range of challenges in using species distribution model-ling for predicting the effects of climate change (Sinclair et al., 2010). Inthis paper, we will focus on how to simplify the use of such modelsthrough coupling of interoperable web services, a concept that is alsoreferred to as the Model Web (Geller and Turner, 2007). In the ModelWeb, a simple interoperable web service which can be either offeringor processing data, and delivering the result in a standardized format,can be reused by the next service. While manyweb services are alreadyavailable, only a few of these are sufficiently interoperable for the tasksoutlined in this paper. In the ecological world, many services are WebMap Services, where maps can be overlaid in a web client. Theseservices are, however, usually deployed for improving the visualisationof the environmental context of the analysed information rather thanfor delivering new products which can be further processed.

It is the purpose of this paper to discuss how a number of interoper-able web services can interact with each other and produce new infor-mation. The services described have been developed mainly in relationto establishing the Digital Observatory for Protected Areas (DOPA), abiodiversity information system (Dubois et al., 2010a, 2010b) devel-oped by the Joint Research Centre of the European Commission in

39J.O. Skøien et al. / Ecological Informatics 14 (2013) 38–43

collaboration with other international organizations, including theGlobal Biodiversity Information Facility GBIF, UNEPWorld ConservationMonitoring Centre (WCMC), BirdLife International and the Royal Socie-ty for the Protection of Birds (RSPB). In particular, we will present anapplication of a modelling component of the DOPA, the eHabitat WPS(Dubois et al., 2011) designed as a flexible modelling service for identi-fying similarities in ecosystems with those found in a reference area.Used in conjunction with other web services providing data on speciesoccurrences (GBIF), bird distributionmaps and boundaries of ImportantBird Areas (IBAs), both developed by the RSPB and BirdLife Internation-al, we will show that eHabitat can easily be used for modelling ecolog-ical niche for birds and for assessing possible climate change impacton IBAs. We will first introduce the use of a simple statistical function,the Mahalanobis distance, for identifying ecosystems of similar proper-ties. We will then describe how this function can be used for a broadrange of purposes, from ecological niche modelling to climate changeforecasting, by simply using different variables.Wewill go on to discussthe benefits of making suchmodels available via web based services, inparticular Web Processing Services, as this allows different models anddata services to build more complex modelling chains. Lastly, a casestudy will be presented to illustrate how these various web servicescan interact with each other and provide useful information to decisionmakers. The example used will address the potential impact of climatechange on the habitat of an African bird, Grauer's Swamp-warbler. Wewill conclude with a discussion on the advantages and limitations ofusing such interacting services.

2. Methods

2.1. Mahalanobis distance and similarity index

For a set of environmental variables available for the region ofinterest, there are different ways of modelling the environmental simi-larity between this region and a reference geometry, typically a set ofpoints referring to presence observations or the points within aprotected area. The Mahalanobis distance Di here is used as a measureof the similarity of a set of environmental variables between a pixel iand the averages of these environmental variables for the referencegeometry, and is defined as:

D2i ¼ xi−μ½ �T C½ �−1 xi−μ½ � ð1Þ

where xi is the vector of the values of the environmental variables forpixel i, μ is the mean of the environmental variables for the referencegeometry, and [C] is the covariance matrix of the environmental vari-ables for the reference geometry. The covariance matrix for n variablesis given by

C½ �

COV x1; x1ð Þ COV x1; x2ð Þ ⋯ ⋯ COV x1; xnð ÞCOV x2; x1ð Þ COV x2; x2ð Þ ⋯ ⋯ COV x2; xnð Þ

⋮ ⋮ ⋱ ⋮⋮ ⋮ ⋱ ⋮

COV xn; x1ð Þ COV xn; x2ð Þ COV xn; xnð Þ

266664

377775 ð2Þ

and the covariance between any two variables, xk and xl, with means μkand μl and number of points in the reference geometry J is given by

COV xk; x1ð Þ ¼XJ

j¼1

xkj−μk

� �xlj−μ l

� �J

0@

1A: ð3Þ

The use of the inverse of the covariance matrix makes theMahalanobis distance independent of the different scales of the vari-ables. Highly correlated variables will have less effect on Di thanuncorrelated variables. When the environmental variables used to gen-erate the mean vector and covariance matrix are normally distributed,then Di

2 is distributed approximately according to a χ2 distribution

with n degrees of freedom, and so we can convert Di2 into p-values.

The p-values (or probability values) range from 0.0 representing nosimilarity to 1.0 for areas which are identical to the mean of the refer-ence area. If the predictor variables are not normally distributed, theconversion is still useful as it rescales the unbounded Di

2 values to a[0–1] range. This p-value can be seen as the probability that the pixel ihas a similar set of environmental variables as the ones found for thereference area, or of the probability that a pixel in the future has a sim-ilar set of environmental variables.

2.2. Ecological niche modelling & ecological forecasting

The traditional use of theMahalanobis distance is for identifyinghab-itats for a given species (see e.g. Clark et al., 1993; Rotenberry et al.,2006). Instead of computing the mean and the variances, μ and [C],over a whole protected area, one is computing these statistics for theecological variables measured at all the locations where a species hasbeen observed. In this way, the Mahalanobis distance can be computedto produce a map of probabilities of finding habitats that are specific tothe given species, a map that is thus a probabilistic representation ofthe species' ecological niche.

Climatic factors are one of the typical parameters one would usewhen modelling the ecological niche of a species, as these are determi-nant in species distributions. Three climatic variables, (mean annualprecipitation (P), annual average of the biotemperature (B), and theratio of mean annual potential evapotranspiration to precipitation(PETR)), have been successfully used to generate a bioclimatic schemeat the global level (Holdridge, 1947). This simple scheme can be re-configured to identify the bioclimatic area in which a species wouldfall and, using models for climate change, forecasting possible changesto a species' ecological niche has become possible.

3. Establishing a Web Processing Service for ecological forecastingand niche modelling

Web service standards (e.g. ISO TC211 and the Open GeospatialConsortium OGC), are the basis for generic services to exchange geo-graphic data and have become relatively straightforward to develop.Less widespread are Web Processing Services (WPS) which providea standardised interface facilitating the publishing of geospatial pro-cesses including any algorithm, calculation or model that operates onspatially referenced data (Schut, 2007). One example of such a WPSwhere end-users can request from the service the computation ofmodelling ecological niches is the openModeller WPS (Muñoz et al.,2011). A limitation of the service is that it can use only data availableon the server. The means to access other sources of data and modelswould dramatically extend the number of its potential applications. Inthe following,wewill describe eHabitat, aWPS designed for finding eco-logical similarities in datasets which can be used for ecological nichemodelling or ecological forecasting by accessing other data services.

3.1. Data and associated web services

3.1.1. Important Bird Areas (IBAs)A site is recognized as an Important Bird Area (IBA) only if it meets

certain criteria, based on the occurrence of key bird species that arevulnerable to global extinction or whose populations are otherwiseirreplaceable (http://www.birdlife.org/action/science/sites/). IBAsare identified by BirdLife International, an international partnershipof conservation organizations focused on the protection of birds.IBAs are not always parts of the existing network of protectedareas, but recognition of a non-protected area as an IBA will usuallyincrease its priority for being protected in future conservationplans. By 2012 around 12,000 IBAs in more than 200 countries/terri-tories had been identified. The boundaries of these IBAs are now ac-cessible through a web service put in place by BirdLife International.

40 J.O. Skøien et al. / Ecological Informatics 14 (2013) 38–43

3.1.2. Species' rangesIn addition to the IBAs, BirdLife International also provides a collec-

tion of predicted species rangemaps. These have beendeveloped from avariety of data sources, including up-to-date and historic observationsand literature ranges. The species maps are offered as polygons thatare separated by season (breeding, non-breeding, passage, resident),origin (native, reintroduced, introduced, vagrant) and presence (extant,probably extant, possibly extant, possibly extinct, extinct post 1500).

3.1.3. Species occurrencesThere are many organizations that are observing species around

the world, and BirdLife International partners play a key role in co-ordinating this work. For species observations, however, the best inter-net provider is the Global Biodiversity Information Facility (GBIF) towhich the RSPB and BirdLife International also provide their data.GBIF was established and funded by governments in 2001 through anOECD Global Science Forum recommendation making it, today, theworld's largest multilateral initiative for enabling free access to biodi-versity data via the Internet to underpin scientific research, conserva-tion and sustainable development. The GBIF Data Portal (www.gbif.org) provides unified access to a continually expanding set of biodi-versity data records. By 2012, there were over 323 million recordsfrom some 8800 datasets from 370 data publishers, derived from aworldwide network of national, regional and thematic BiodiversityInformation Facilities (http://www.gbif.org/governance/governing-board/current-participants/).

In addition to the search/browse interface on the GBIF Data Portal(http://data.gbif.org) which allows a user to construct and submit com-plex, filtered queries to the data cache, several REST-basedweb servicesfor machine to machine access are available (http://data.gbif.org/tutorial/services). For example, the Occurrence service (http://data.gbif.org/ws/rest/occurrence) can return records for a taxon occurringwithin a particular geographic bounding boxwhile the Occurrence den-sity web service (http://data.gbif.org/ws/rest/density) provides countsof occurrence records by one-degree cell. Output formats include theinternational KML (Keyhole Markup Language) OGC standard used bythe Google Earth application and other mapping systems. Retrievingcorrect information about a species is still a challenge because of thelack of standards in taxonomy but some notable progress has beenmade through the development of the Global Names Architecture(GNA, http://www.gbif.org/informatics/name-services/global-names-architecture/), an informatics infrastructure and associated standardsfor providing unified discovery and access to information about taxonnames, thereby enabling the development of a taxonomic “backbone”to underpin biodiversity informatics.

3.2. Climate data

The WorldClim data base (http://www.worldclim.org/) providesgridded maps of current and future climate variables at different lati-tude–longitude resolutions, i.e., 10 min, 5 min, 2.5 min and 30 s. Thelatter corresponds to grid cells of 0.86 km2 at equator, often referredto as the 1 km grid. The dataset for current climate (Hijmans et al.,2005) is the result of a spatial interpolation process using splinesapplied to measurements from climate stations. The forecasted dataset has been downscaled to the same resolutions from different scenar-ios of different global circulation models, assuming that the spatialpattern within each grid cell is constant (Ramirez and Jarvis, 2010).High resolution forecast climate data are available for the climatemodels HADCM3 (Gordon et al., 2000), CCCMA (McFarlane et al.,2005; Scinocca et al., 2008) and CSIRO (Watterson et al., 1998) andthe model scenarios A2a and B2a. From these datasets, we derived thevariables needed to reconstruct Holridge's life zones. More specifically,we created the following:

1. The mean annual precipitation (P)

2. The biotemperature (annual average)3. The ratio of mean annual potential evapotranspiration (PET) to P:

PETR.

The biotemperature is the annually averaged temperature afterreplacing all temperatures below the freezing point with zero values,assuming that plants are dormant at lower temperatures. The PET isobtained from Thorntwaite's equation (Thorntwaite, 1948):

E ¼ 16:010TI

� �a

ð4Þ

where E is monthly potential evapotranspiration (mm), T is themonth-ly mean temperature (°C) and I is a heat index for a given area which isthe sum of 12 monthly index values i, where i is given by:

i ¼ T5:0

� �1:514ð5Þ

and a is an empirically derived exponent which is a function of I:

a ¼ 6:75� 10−7� �

I3− 7:71� 10−5� �

I2 þ 1:792� 10−2� �

I þ 0:49239:

ð6Þ

The PETR is then found from PET/P, a dimensionless measure of thearidity. The values can be characterised from super-arid to super-humid. Thorntwaite's equation is one of the simplest of many differentmodels for computing PET, and is widely used for large scale computa-tions. To the best of our knowledge, these data were not availableelsewhere and we produced a 10 min, 5 min and 2.5 min global rastermaps of these variables available through a web coverage service(WCS). We also produced 30 s maps that will be made available tothe wider community in the near future.

3.3. Architecture of the Web Processing Service

eHabitat was conceived as Web Processing Service in view toallow end-users to interact via the world wide web with a servicefor modelling Mahalanobis distances (Dubois et al., 2011). The mainbenefit of such a modelling service is that end-users can use it witha wide range of data and for different purposes. The computation ofMahalanobis distances is also a relatively straightforward modelwhich can easily be linked with other data and modelling servicesand therefore provides a useful illustration of themain concepts behindthe Model Web.

eHabitat WPS follows a Service Oriented Architecture (SOA)approach, i.e. the underlying model and code used for computingthe Mahalanobis distances are made available as dispersed services/processes over a network. The process is exposed to the Web usingOGC's Web Processing Service standard. It consumes gridded data byaccessing Web Coverage Services (WCS) and vector data using OGCstandards like e.g. the Web Feature Service (WFS) or KML. An OpenSource software stack is used for the service components. The PyWPSimplementation was chosen as the service platform. R (R DevelopmentCore Team, 2012) builds the back end for the algorithms used to com-pute the Mahalanobis distances. Individual processes are Pythonmodules and use supporting libraries like Geospatial Data AbstractionLayer (GDAL) or OWSLib to access data, manipulate and send it to R.To perform the modelling, theWPS receives an XML request with refer-ences to the coverage services to use as input indicator datasets and aURL pointing to the geometries of interest (in this case locations ofspecies observations). When the computation is completed, the resultsreturned from R are processed to generate different output formatsdepending on the required use, e.g. raw data formats like GeoTIFF orNetCDF or WMS for visualisation purposes. Such an architecture,where a modelling back end written in R is independently developed

41J.O. Skøien et al. / Ecological Informatics 14 (2013) 38–43

from the web services, has already been successfully implemented inother contexts such as an automatic interpolation service for mappingenvironmental variables (Pebesma et al., 2011). A schema showingthe dataflow in eHabitat used for ecological niche modelling is shownin Fig. 1.

4. Case study: Climate change impact on the ecological niche ofBradypterus Graueri

In the following, we will illustrate the use of the above describedweb services for modelling the possible impact of climate change onan African bird. Grauer's Swamp-warbler (Bradypterus graueri) is amedium-sized warbler found in Rwanda, Burundi, eastern DemocraticRepublic of Congo (DRC) and south-western Uganda. Despite beinglocally common, this species has a very small and severely fragmentedarea of occupancy within its small overall range. Many sites are beingconverted to cultivation or pasture. Thus the species' area of occupancyis declining and, by inference, so is the number of mature individuals. Itis therefore classified as Endangered (BirdLife International, 2012).

We used the HADCM3 climate model and the eHabitat WPS forcomputing the Mahalanobis distances using the Holdridge dataobtained at the 3 locations where GBIF reported the bird species. Asexpected, a good match was obtained between the species distributionmodel and the map of probabilities to find similar bioclimatic condi-tions. This additionally showed that areas presenting bioclimatic simi-larities with the bird's habitat were also very limited in a spatial pointof view.

Repeating the previous exercise of modelling the bioclimatic areasof Grauer's Swamp-warbler using forecasted data for the years 2050and 2080, a clear reduction of the areas that are suitable in regardto the bioclimatic conditions was found (Fig. 2). By comparing withthe current situation it was possible to see that the areas of current

Fig. 1. Design of the web processing service for forecasting clim

habitat became less suitable between 2050 and 2080 for a given cli-mate change scenario.

By accessing additional data available through web services, moreinformation potentially useful for conservation decision making couldbe incorporated. For example, using a human population density mapit became clear that possiblemitigation strategies in response to climatechange will be difficult, as areas suitable in the future for Grauer'sSwamp-warbler overlap with areas indicated as having high humanpopulation density.

5. Discussion

This paper has demonstrated the feasibility of using a set of web ser-vices for sharing and modelling data to answer fundamental questionsregarding the climatic threat an endangered bird, following the approachdescribed by Geller and Turner (2007). Putting aside the issue of dataquality, we have shown how web based services can be connected tomodel and identify the ecological niche of a species using bioclimaticvariables and data on the species occurrences. In a second stage, forecast-ed climatic data and ancillary information on Important Bird Areas andhuman population density were used, again through web services, tohelp decision makers to assess areas where new protected areas couldbe created to mitigate the impact of climate change. The completemodelling process can be done within minutes by simply using a webbrowser to use the web client.

eHabitat has been developed as one of the building blocks of theDigital Observatory for Protected Areas (DOPA). Other services fromDOPA that are dealing with species (eSpecies, http://especies.jrc.ec.europa.eu/), ecosystem services (http://ges.jrc.ec.europa.eu/) or theautomatic processing of remote sensing data (eStation, http://estation.jrc.ec.europa.eu/) are in development or already available, like a web-based ool for mapping land-cover change (see Bastin et al., 2012, http://landcover-change.jrc.ec.europa.eu/). These fundamental components,

ate change impact on the ecological niche of bird species.

Fig. 2. Screen captures of the web client showing the current modelled ecological niche of Grauer's Swamp-warbler (left) and the one predicted for 2050 (right).

42 J.O. Skøien et al. / Ecological Informatics 14 (2013) 38–43

when linked with each other, should allow us to serve a broad rangeof end-users active in conservation, from park-managers to policymakers. While, ideally, one should be able to combine any of thesetools with other web services designed for conservation purposes(see e.g. Lifemapper for modelling species distributions, http://www.lifemapper.org/), most of the available services cannot be easily linkedwith each other when buildingmore complex modelling chains. There-fore, by orchestrating the developments and interactions of a largernumber of interoperable services managed by different institutions,the DOPA can be seen mainly as an initiative ensuring a minimum ofcoordination among key data and model providers to encourage ahigher reusability of the services proposed.

The Web Processing Service described in this paper is extremelyflexible and able to service most kinds of request related to speciesdistribution modelling. The communication with the service is rathercumbersome for most users though, as the request will have to be anXML document that properly defines which data sets to use and the re-sult is also an XML document. To simplify access to the WPS for moreoccasional users and for demonstration purposes, we implementedthe Web Client presented in this paper. As a front end, the proposedWeb Client is expected to help the user to select combinations of data,as well as additional thematic layers that can be useful for decision-making. This front end is on the one hand limiting the flexibility forthe users but, on the other hand, also dramatically simplifies the choiceof input data as well as of the climate change model. The possibility todevelop web based services with different front ends for people withdifferent skills is a huge asset for capacity building activities in conser-vation. One can indeed easily customise interfaces to provide toolsthat are tailored to the different end-users who will then not berequired to undergo any time consuming training, provided there isan adequate documentation available, or have toworry about program-ming and maintaining complex informatics infrastructures. The WPSallows anyone to develop their own client to access theWPSwith a dif-ferent combination of data, and we have ourselves already developedother clients that are consuming the same service to answer slightlydifferent questions (Dubois et al., 2011). This includes a client thataccepts anyWeb Coverage Service as an input. This option is importantconsidering the dramatic increase of available dataset over the internet(Group on Earth Observations, 2011). The number of data services intheir catalogue increased from less than 200 in 2010 to over 1500 inNovember 2011. Connecting Web based models with these data sets

means that a user will greatly reduce the necessary time to download,convert and use the data sets with different models on their own desk-top. On the other hand, the this architecture is still depending on areasonably good access to the internet, an issue that should not beneglected, in particular for conservation activities in developing coun-tries or in remote locations.

In terms of the drawbacks of the service oriented architecture, alimitation is that accessing the WPS without a Web Client may beseen as an obstacle at first sight by non experts. Many practitionersand researchers will probably need to collaborate with a programmer,either for writing the appropriate XML requests, or by writing a newWeb Client that would match the user's needs. The huge advantage ofthe latter approach is that reproducibility is guaranteed if all (test)datasets are available online. Another benefit is that the approach willalso be accessible to other potential users. And whereas computationalresources and network access can be restrictive for publishing WPSs,Web Clients will in this case only be responsible for communication ofmodel parameters and the address to Web Services with data. Thereare also many other initiatives currently ongoing that aim to simplifythis process, including the orchestration software Taverna (de Jesuset al., 2012).

The Mahalanobis method is one of several SDMs that can be appliedto model similarity between regions in space and time. We have forsimplicity chosen to use the Mahalanobis distance in this paper, butcould easily exchange this method with for example MaxEnt (Phillipset al., 2006). However, the effectiveness of these methods still hassome limitations (Sinclair et al., 2010). The most important one is thatthey predict potential range based on the variability of the environ-mental indicators at the locations. If this variability is low for a certainvariable, noneof thesemethods are able to recognizewhether this is be-cause a species is particularly sensitive to this variable, or if this variablejust happens to have a low variability and there is another variable thatactually limits the habitat.

The difference between potential and realized niche is even moreimportant when we apply SDM methods to conservation areas suchas PAs and IBAs (Hartley et al., 2007; Nelson et al., 2009). If the covari-ance matrix in the application of the Mahalanobis distance is onlydependant on the borders of the area, then it is likely that the tolerancefor change of the protected species for most conservation areas will beunderestimated. For these cases, an approach that also takes intothe account the occurrences or the predicted species maps should be

43J.O. Skøien et al. / Ecological Informatics 14 (2013) 38–43

considered. This involves only a small modification of the Web Client,whereas the sameWPS can be used.

In relation to the data used for climate change forecasting, thepresent study used the HADCM3 model for illustration purposes. Dif-ferent models, scenarios and resolutions can easily be implementedand used for comparison purposes. The uncertainties in these forecastsare of course high, but by comparing the results of different models andscenarios, the user may find here a simple way to assess model uncer-tainties. This last point is an important fundamental issue, but beyondthe scope of this paper. Uncertainties in data and models do propagatebetween chained services and there is a need to document these uncer-tainties at all stages. Skøien et al. (2011) discuss how uncertainties canbe described and quantified within eHabitat. This is not yet included inthe client described in this paper, but will be included in the near futureas uncertainties assessments are essential for decision-making and forresearch.

Acknowledgements

This work is partly supported by the European Commission, underthe 7th Framework Programme, by the EuroGEOSS project funded bythe DG RTD. The views expressed herein are those of the authors andare not necessarily those of the European Commission. We would alsolike to thank an anonymous reviewer and the editor Mark O'Connellfor useful comments and suggestions. More information about theDOPA and eHabitat can be found on the Internet, see http://dopa.jrc.ec.europa.eu/ and http://ehabitat.jrc.ec.europa.eu, respectively.

References

Bastin, L., Buchanan, G., Beresford, A., Pekel, J.-F., Dubois, G., 1993. Open-source mappingand services for Web-based land-cover validation. Ecological Informatics 14, 9–16.

BirdLife International, 2012. Species factsheet: Bradypterus graueri. Downloaded fromhttp://www.birdlife.org (on July 25, 2012).

Clark, J.D., Dunn, J.E., Smith, K.G., 1993. A multivariate model of female black bear habitatuse for a geographical information system. Journal of Wildlife Management 57,519–526.

de Jesus, J., Walker, P., Grant, M., Grool, S., 2012. WPS orchestration using the Tavernaworkbench: the eScience approach. Computers and Geosciences 47, 75–86.

Dubois, G., Clerici, M., Peedell, S., Mayaux, P., Grégoire, J.-M., Bartholomé, E., 2010a. ADigital Observatory for Protected Areas — DOPA, a GEO-BON contribution to themonitoring of African biodiversity. “Proceedings of Map Africa 2010”, 23–25November 2010, Cape Town, South Africa.

Dubois, G., Hartley, A., Peedell, S., de Jesus, J., Tuama, É.Ó., Cottam, A., May, I., Fisher, I.,Nativi, S., Bertrand, F., 2010b. DOPA, a Digital Observatory for Protected Areasincluding Monitoring and Forecasting Services. European Geosciences Union(EGU) 2010, Vienna, Austria, 2–7 May 2010.

Dubois, G., Skøien, J.O., De Jesus, J., Peedell, S., Hartley, A., Nativi, S., Santoro, M., Geller,G., 2011. eHabitat: a contribution to the model web for habitat assessments andecological forecasting. Proceedings of the 34th International Symposium onRemote Sensing of Environment, April 10–15, 2011, Sydney, Australia.

Geller, G., Turner, W., 2007. The Model Web: a concept for ecological forecasting. IEEEInternational Geoscience and Remote Sensing Symposium, Barcelona, Spain, 23–27July.

Gordon, C., Cooper, C., Senior, C.A., Banks, H., Gregory, J.M., Johns, T.C., Mitchell, J.F.B.,Wood, R.A., 2000. The simulation of SST, sea ice extents and ocean heat transportsin a version of the Hadley Centre coupled model without flux adjustments. ClimateDynamics 16, 147–168.

Group on Earth Observations, 2011. GEO VIII. Report of the Architecture and DataCommittee (ADC). Document 19.

Guisan, A., Zimmerman, N.E., 2000. Predictive habitat distribution models in ecology.Ecological Modelling 135, 147–186.

Hartley, A.J., Nelson, A., Mayaux, P., Grégoire, J.-M., 2007. The assessment of Africanprotected areas. JRC Scientific and Technical Reports, EUR 21296. Office for OfficialPublications of the European communities, Luxembourg, p. 70.

Hijmans, R., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolutioninterpolated climate surfaces for global land areas. International Journal of Clima-tology 25, 1965–1978.

Holdridge, L.R., 1947. Determination of world plant formations from simple climaticdata. Science 105, 367–368.

Knick, S.T., Dyer, D.L., 1997. Distribution of black-tailed jackrabbit habitat determinedby GIS in southwestern Idaho. Journal of Wildlife Management 61, 75–85.

McFarlane, N.A., Scinocca, J.F., Lazare, M., Harvey, R., Verseghy, D., Li, J., 2005. TheCCCma third generation atmospheric general circulation model. CCCma InternalRep . 25 pp.

Muñoz, M.E.S., Giovanni, R., Siqueira, M.F., Sutton, T., Brewer, P., Pereira, R.S., Canhos,D.A.L., Canhos, V.P., 2011. openModeller: a generic approach to species' potentialdistribution modelling. GeoInformatica 15, 111–135.

Nelson, A., Hartley, A., Dubois, G., Punga, M., 2009. Geoinformatics for the environmentalsurveillance of protected areas in Africa. StatGIS 2009, Geoinformatics for Environ-mental Surveillance, Milos, Greece.

Pebesma, E., Cornford, D., Dubois, G., Heuvelink, G.B.M., Hristopoulos, D., Pilz, J.,Stöhlker, U., Morin, G., Skøien, J.O., 2011. INTAMAP: the design and implementationof an interoperable automated interpolation web service. Computers & Geosciences37 (3), 343–352.

Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modelling ofspecies geographic distributions. Ecological Modelling 190, 231–259.

R Development Core Team, 2012. R: A Language and Environment for Statistical Com-puting. R Foundation for Statistical Computing, Vienna, Austria3-900051-07-0.

Ramirez, J., Jarvis, A., 2010. Disaggregation of Global Circulation Model Outputs, Inter-national Center for Tropical Agriculture. CIAT, Cali, Colombia.

Rotenberry, J.T., Knick, S.T., Dunn, J.E., 2002. A minimalist approach to mapping specieshabitat: Pearson's planes of closest fit. In: Scott, J.M., et al. (Ed.), Predicting SpeciesOccurences: Issues of Accuracy and Scale. Island Press, Washington, D. C., USA.

Rotenberry, J.T., Preston, K.L., Knick, S.T., 2006. GIS-based niche modelling for mappingspecies' habitat. Ecology 87, 1458–1464.

Schut, P., 2007. Opengis Web Processing Service. OGC Document 05-007r7. (URL:/http://portal.opengeospatial.org/files/?artifact_id=24151S, Accessed March 12, 2012.).

Scinocca, J.F., McFarlane, N.A., Lazare, M., Li, J., Plummer, D., 2008. The CCCma thirdgeneration AGCM and its extension into the middle atmosphere. AtmosphericChemistry and Physics 8, 7055–7074.

Sinclair, S.J., White, M.D., Newell, G.R., 2010. How useful are species distribution modelsfor managing biodiverstity under future climates. Ecology and Society 15, 8.

Skøien, J.O., Truong, P., Dubois, G., Cornford, D., Heuvelink, G.B.M., Geller, G., 2011. Un-certainty propagation in the Model Web: a case study with eHabitat. Proceedingsof the 34th International Symposium on Remote Sensing of Environment, April10–15, 2011, Sydney, Australia.

Thorntwaite, C.W., 1948. An approach toward a rational classification of climate. Geo-graphical Review 38, 55–94.

Watterson, I.G., Dix, M.R., Colman, R.A., 1998. A comparison of present and doubledCO2 climates and feedbacks simulated by three general circulation models. Journalof Geophysical Research 104, 1943–1956.