ECOINF-00363; No of Pages 6
Ecological Informatics xxx (2012) xxx–xxx
Contents lists available at SciVerse ScienceDirect
Ecological Informatics
journal homepage: www.elsevier.com/locate/ecolinf
A Model Web approach to modelling climate change in biomes of Important
Bird 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), Italy
Royal Society for the Protection of Birds, RSPB, The Lodge, Potton Road, Sandy, Bedfordshire, SG19 2DL, UK
BirdLife International, Cambridge, Wellbrook Court, Girton Road, Cambridge, CB3 0NA, UK
d
Global Biodiversity Information Facility (GBIF), Copenhagen, Denmark
b
c
a r t i c l e
Available online xxxx
Keywords:
Web services
Ecological forecasting
Model Web
Bird conservation
i n f o
a b s t r a c t
Protected Areas (PA) are designated to conserve species and habitats and protect against anthropogenic pressures. Park boundaries, however, offer no protection against climatic change and where boundaries are actual
constructions, they may also act as physical barriers to species movements to new suitable habitats. The
means for assessing the consequences of climate change on ecosystems and for identifying gaps in PA connectivity are therefore a conservation priority. The complexity of the scientific questions raised requires a multidisciplinary approach given the variety of the information required. This includes species observations and
their theoretical distributions, as well as ecosystem assessments and climate change models. Such complex questions can be more easily dealt with if there is appropriate access to data and models, a strategy endorsed by
GEO-BON, the Group on Earth Observations Biodiversity Observation Network. In this paper, we show how
data and models recently made available on the World Wide Web can be coupled through interoperable services
and used for climate change forecasting in the context of Important Bird Areas (IBAs) and how, for any bird species described in the databases, areas can be identified where the species may find a more suitable environment
in 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 identifying the suitability of habitats based on observations of the species and
a set of environmental indicators assumed to include the species'
niche. There are a range of such models used in ecology (Guisan and
Zimmerman, 2000). A relatively widely used method is based on the
Mahalanobis 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 is
based on the creation of pseudo-absence locations.
Common to these and other models is that they can be used to generate suitability or similarity maps which can help in identifying regions
where a given species is more likely to be observed. These models can
therefore be also used to define those locations where species could
migrate should their current habitats become unsuitable as a result of
human activities.
In addition to modelling the potential distribution of species, such
models can also be used for estimating similarities between ecosystems
found in protected areas and those observed in their surroundings. This
⁎ Corresponding author.
E-mail address: [email protected] (J.O. Skøien).
approach uses the size of the area with a similarity level above a certain
threshold 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 modelling for predicting the effects of climate change (Sinclair et al., 2010). In
this paper, we will focus on how to simplify the use of such models
through coupling of interoperable web services, a concept that is also
referred to as the Model Web (Geller and Turner, 2007). In the Model
Web, a simple interoperable web service which can be either offering
or processing data, and delivering the result in a standardized format,
can be reused by the next service. While many web services are already
available, only a few of these are sufficiently interoperable for the tasks
outlined in this paper. In the ecological world, many services are Web
Map Services, where maps can be overlaid in a web client. These
services are, however, usually deployed for improving the visualisation
of the environmental context of the analysed information rather than
for delivering new products which can be further processed.
It is the purpose of this paper to discuss how a number of interoperable web services can interact with each other and produce new information. The services described have been developed mainly in relation
to establishing the Digital Observatory for Protected Areas (DOPA), a
biodiversity information system (Dubois et al., 2010a, 2010b) developed by the Joint Research Centre of the European Commission in
1574-9541/$ – see front matter © 2012 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.ecoinf.2012.12.003
Please cite this article as: Skøien, J.O., et al., A Model Web approach to modelling climate change in biomes of Important Bird Areas, Ecological
Informatics (2012), http://dx.doi.org/10.1016/j.ecoinf.2012.12.003
2
J.O. Skøien et al. / Ecological Informatics xxx (2012) xxx–xxx
collaboration with other international organizations, including the
Global Biodiversity Information Facility GBIF, UNEP World Conservation
Monitoring Centre (WCMC), BirdLife International and the Royal Society for the Protection of Birds (RSPB). In particular, we will present an
application of a modelling component of the DOPA, the eHabitat WPS
(Dubois et al., 2011) designed as a flexible modelling service for identifying similarities in ecosystems with those found in a reference area.
Used in conjunction with other web services providing data on species
occurrences (GBIF), bird distribution maps and boundaries of Important
Bird Areas (IBAs), both developed by the RSPB and BirdLife International, we will show that eHabitat can easily be used for modelling ecological niche for birds and for assessing possible climate change impact
on IBAs. We will first introduce the use of a simple statistical function,
the Mahalanobis distance, for identifying ecosystems of similar properties. We will then describe how this function can be used for a broad
range of purposes, from ecological niche modelling to climate change
forecasting, by simply using different variables. We will go on to discuss
the benefits of making such models available via web based services, in
particular Web Processing Services, as this allows different models and
data services to build more complex modelling chains. Lastly, a case
study will be presented to illustrate how these various web services
can interact with each other and provide useful information to decision
makers. The example used will address the potential impact of climate
change on the habitat of an African bird, Grauer's Swamp-warbler. We
will conclude with a discussion on the advantages and limitations of
using such interacting services.
2. Methods
2.1. Mahalanobis distance and similarity index
For a set of environmental variables available for the region of
interest, there are different ways of modelling the environmental similarity between this region and a reference geometry, typically a set of
points referring to presence observations or the points within a
protected area. The Mahalanobis distance Di here is used as a measure
of the similarity of a set of environmental variables between a pixel i
and the averages of these environmental variables for the reference
geometry, and is defined as:
2
T
Di ¼ ½xi −μ ½C −1
½xi −μ ð1Þ
where xi is the vector of the values of the environmental variables for
pixel i, μ is the mean of the environmental variables for the reference
geometry, and [C] is the covariance matrix of the environmental variables for the reference geometry. The covariance matrix for n variables
is given by
2
COV ðx1 ; x1 Þ
6 COV ðx2 ; x1 Þ
6
½C 6
⋮
6
4
⋮
COV ðxn ; x1 Þ
3
COV ðx1 ; x2 Þ ⋯ ⋯ COV ðx1 ; xn Þ
COV ðx2 ; x2 Þ ⋯ ⋯ COV ðx2 ; xn Þ 7
7
7
⋮
⋱
⋮
7
5
⋮
⋱
⋮
COV ðxn ; x2 Þ
COV ðxn ; xn Þ
ð2Þ
COV ðxk ; x1 Þ ¼
j¼1
1
0
xkj −μ k xlj −μ l
@
A:
J
2.2. Ecological niche modelling & ecological forecasting
The traditional use of the Mahalanobis distance is for identifying habitats 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 the
ecological variables measured at all the locations where a species has
been observed. In this way, the Mahalanobis distance can be computed
to produce a map of probabilities of finding habitats that are specific to
the given species, a map that is thus a probabilistic representation of
the species' ecological niche.
Climatic factors are one of the typical parameters one would use
when modelling the ecological niche of a species, as these are determinant in species distributions. Three climatic variables, (mean annual
precipitation (P), annual average of the biotemperature (B), and the
ratio of mean annual potential evapotranspiration to precipitation
(PETR)), have been successfully used to generate a bioclimatic scheme
at the global level (Holdridge, 1947). This simple scheme can be reconfigured to identify the bioclimatic area in which a species would
fall and, using models for climate change, forecasting possible changes
to a species' ecological niche has become possible.
3. Establishing a Web Processing Service for ecological forecasting
and niche modelling
Web service standards (e.g. ISO TC211 and the Open Geospatial
Consortium OGC), are the basis for generic services to exchange geographic data and have become relatively straightforward to develop.
Less widespread are Web Processing Services (WPS) which provide
a standardised interface facilitating the publishing of geospatial processes including any algorithm, calculation or model that operates on
spatially referenced data (Schut, 2007). One example of such a WPS
where end-users can request from the service the computation of
modelling ecological niches is the openModeller WPS (Muñoz et al.,
2011). A limitation of the service is that it can use only data available
on the server. The means to access other sources of data and models
would dramatically extend the number of its potential applications. In
the following, we will describe eHabitat, a WPS designed for finding ecological similarities in datasets which can be used for ecological niche
modelling or ecological forecasting by accessing other data services.
3.1. Data and associated web services
and the covariance between any two variables, xk and xl, with means μk
and μl and number of points in the reference geometry J is given by
J
X
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 no
similarity to 1.0 for areas which are identical to the mean of the reference area. If the predictor variables are not normally distributed, the
conversion is still useful as it rescales the unbounded Di2 values to a
[0–1] range. This p-value can be seen as the probability that the pixel i
has a similar set of environmental variables as the ones found for the
reference area, or of the probability that a pixel in the future has a similar set of environmental variables.
ð3Þ
The use of the inverse of the covariance matrix makes the
Mahalanobis distance independent of the different scales of the variables. Highly correlated variables will have less effect on Di than
uncorrelated variables. When the environmental variables used to generate the mean vector and covariance matrix are normally distributed,
then Di2 is distributed approximately according to a χ2 distribution
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 are
vulnerable to global extinction or whose populations are otherwise
irreplaceable (http://www.birdlife.org/action/science/sites/). IBAs
are identified by BirdLife International, an international partnership
of conservation organizations focused on the protection of birds.
IBAs are not always parts of the existing network of protected
areas, but recognition of a non-protected area as an IBA will usually
increase its priority for being protected in future conservation
plans. By 2012 around 12,000 IBAs in more than 200 countries/territories had been identified. The boundaries of these IBAs are now accessible through a web service put in place by BirdLife International.
Please cite this article as: Skøien, J.O., et al., A Model Web approach to modelling climate change in biomes of Important Bird Areas, Ecological
Informatics (2012), http://dx.doi.org/10.1016/j.ecoinf.2012.12.003
J.O. Skøien et al. / Ecological Informatics xxx (2012) xxx–xxx
3
3.1.2. Species' ranges
In addition to the IBAs, BirdLife International also provides a collection of predicted species range maps. These have been developed from a
variety of data sources, including up-to-date and historic observations
and literature ranges. The species maps are offered as polygons that
are 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).
2. The biotemperature (annual average)
3. The ratio of mean annual potential evapotranspiration (PET) to P:
PETR.
3.1.3. Species occurrences
There are many organizations that are observing species around
the world, and BirdLife International partners play a key role in coordinating this work. For species observations, however, the best internet provider is the Global Biodiversity Information Facility (GBIF) to
which the RSPB and BirdLife International also provide their data.
GBIF was established and funded by governments in 2001 through an
OECD Global Science Forum recommendation making it, today, the
world's largest multilateral initiative for enabling free access to biodiversity data via the Internet to underpin scientific research, conservation and sustainable development. The GBIF Data Portal (www.gbif.
org) provides unified access to a continually expanding set of biodiversity data records. By 2012, there were over 323 million records
from some 8800 datasets from 370 data publishers, derived from a
worldwide network of national, regional and thematic Biodiversity
Information Facilities (http://www.gbif.org/governance/governingboard/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 complex, filtered queries to the data cache, several REST-based web services
for 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 occurring
within a particular geographic bounding box while the Occurrence density web service (http://data.gbif.org/ws/rest/density) provides counts
of occurrence records by one-degree cell. Output formats include the
international KML (Keyhole Markup Language) OGC standard used by
the Google Earth application and other mapping systems. Retrieving
correct information about a species is still a challenge because of the
lack of standards in taxonomy but some notable progress has been
made through the development of the Global Names Architecture
(GNA, http://www.gbif.org/informatics/name-services/global-namesarchitecture/), an informatics infrastructure and associated standards
for providing unified discovery and access to information about taxon
names, thereby enabling the development of a taxonomic “backbone”
to underpin biodiversity informatics.
ð4Þ
3.2. Climate data
The WorldClim data base (http://www.worldclim.org/) provides
gridded maps of current and future climate variables at different latitude–longitude resolutions, i.e., 10 min, 5 min, 2.5 min and 30 s. The
latter corresponds to grid cells of 0.86 km2 at equator, often referred
to as the 1 km grid. The dataset for current climate (Hijmans et al.,
2005) is the result of a spatial interpolation process using splines
applied to measurements from climate stations. The forecasted data
set has been downscaled to the same resolutions from different scenarios of different global circulation models, assuming that the spatial
pattern within each grid cell is constant (Ramirez and Jarvis, 2010).
High resolution forecast climate data are available for the climate
models HADCM3 (Gordon et al., 2000), CCCMA (McFarlane et al.,
2005; Scinocca et al., 2008) and CSIRO (Watterson et al., 1998) and
the model scenarios A2a and B2a. From these datasets, we derived the
variables needed to reconstruct Holridge's life zones. More specifically,
we created the following:
1. The mean annual precipitation (P)
The biotemperature is the annually averaged temperature after
replacing all temperatures below the freezing point with zero values,
assuming that plants are dormant at lower temperatures. The PET is
obtained from Thorntwaite's equation (Thorntwaite, 1948):
10T a
E ¼ 16:0
I
where E is monthly potential evapotranspiration (mm), T is the monthly mean temperature (°C) and I is a heat index for a given area which is
the sum of 12 monthly index values i, where i is given by:
i¼
T 1:514
5:0
ð5Þ
and a is an empirically derived exponent which is a function of I:
−7 3
−5 2
−2
I − 7:71 10
I þ 1:792 10
I þ 0:49239:
a ¼ 6:75 10
ð6Þ
The PETR is then found from PET/P, a dimensionless measure of the
aridity. The values can be characterised from super-arid to superhumid. Thorntwaite's equation is one of the simplest of many different
models for computing PET, and is widely used for large scale computations. To the best of our knowledge, these data were not available
elsewhere and we produced a 10 min, 5 min and 2.5 min global raster
maps of these variables available through a web coverage service
(WCS). We also produced 30 s maps that will be made available to
the wider community in the near future.
3.3. Architecture of the Web Processing Service
eHabitat was conceived as Web Processing Service in view to
allow end-users to interact via the world wide web with a service
for modelling Mahalanobis distances (Dubois et al., 2011). The main
benefit of such a modelling service is that end-users can use it with
a wide range of data and for different purposes. The computation of
Mahalanobis distances is also a relatively straightforward model
which can easily be linked with other data and modelling services
and therefore provides a useful illustration of the main concepts behind
the Model Web.
eHabitat WPS follows a Service Oriented Architecture (SOA)
approach, i.e. the underlying model and code used for computing
the Mahalanobis distances are made available as dispersed services/
processes over a network. The process is exposed to the Web using
OGC's Web Processing Service standard. It consumes gridded data by
accessing Web Coverage Services (WCS) and vector data using OGC
standards like e.g. the Web Feature Service (WFS) or KML. An Open
Source software stack is used for the service components. The PyWPS
implementation was chosen as the service platform. R (R Development
Core Team, 2012) builds the back end for the algorithms used to compute the Mahalanobis distances. Individual processes are Python
modules and use supporting libraries like Geospatial Data Abstraction
Layer (GDAL) or OWSLib to access data, manipulate and send it to R.
To perform the modelling, the WPS receives an XML request with references to the coverage services to use as input indicator datasets and a
URL pointing to the geometries of interest (in this case locations of
species observations). When the computation is completed, the results
returned from R are processed to generate different output formats
depending on the required use, e.g. raw data formats like GeoTIFF or
NetCDF or WMS for visualisation purposes. Such an architecture,
where a modelling back end written in R is independently developed
Please cite this article as: Skøien, J.O., et al., A Model Web approach to modelling climate change in biomes of Important Bird Areas, Ecological
Informatics (2012), http://dx.doi.org/10.1016/j.ecoinf.2012.12.003
4
J.O. Skøien et al. / Ecological Informatics xxx (2012) xxx–xxx
from the web services, has already been successfully implemented in
other contexts such as an automatic interpolation service for mapping
environmental variables (Pebesma et al., 2011). A schema showing
the dataflow in eHabitat used for ecological niche modelling is shown
in Fig. 1.
4. Case study: Climate change impact on the ecological niche of
Bradypterus Graueri
In the following, we will illustrate the use of the above described
web services for modelling the possible impact of climate change on
an African bird. Grauer's Swamp-warbler (Bradypterus graueri) is a
medium-sized warbler found in Rwanda, Burundi, eastern Democratic
Republic of Congo (DRC) and south-western Uganda. Despite being
locally common, this species has a very small and severely fragmented
area of occupancy within its small overall range. Many sites are being
converted to cultivation or pasture. Thus the species' area of occupancy
is declining and, by inference, so is the number of mature individuals. It
is therefore classified as Endangered (BirdLife International, 2012).
We used the HADCM3 climate model and the eHabitat WPS for
computing the Mahalanobis distances using the Holdridge data
obtained at the 3 locations where GBIF reported the bird species. As
expected, a good match was obtained between the species distribution
model and the map of probabilities to find similar bioclimatic conditions. This additionally showed that areas presenting bioclimatic similarities with the bird's habitat were also very limited in a spatial point
of view.
Repeating the previous exercise of modelling the bioclimatic areas
of Grauer's Swamp-warbler using forecasted data for the years 2050
and 2080, a clear reduction of the areas that are suitable in regard
to the bioclimatic conditions was found (Fig. 2). By comparing with
the current situation it was possible to see that the areas of current
habitat became less suitable between 2050 and 2080 for a given climate change scenario.
By accessing additional data available through web services, more
information potentially useful for conservation decision making could
be incorporated. For example, using a human population density map
it became clear that possible mitigation strategies in response to climate
change will be difficult, as areas suitable in the future for Grauer's
Swamp-warbler overlap with areas indicated as having high human
population density.
5. Discussion
This paper has demonstrated the feasibility of using a set of web services for sharing and modelling data to answer fundamental questions
regarding the climatic threat an endangered bird, following the approach
described by Geller and Turner (2007). Putting aside the issue of data
quality, we have shown how web based services can be connected to
model and identify the ecological niche of a species using bioclimatic
variables and data on the species occurrences. In a second stage, forecasted climatic data and ancillary information on Important Bird Areas and
human population density were used, again through web services, to
help decision makers to assess areas where new protected areas could
be created to mitigate the impact of climate change. The complete
modelling process can be done within minutes by simply using a web
browser to use the web client.
eHabitat has been developed as one of the building blocks of the
Digital Observatory for Protected Areas (DOPA). Other services from
DOPA that are dealing with species (eSpecies, http://especies.jrc.ec.
europa.eu/), ecosystem services (http://ges.jrc.ec.europa.eu/) or the
automatic processing of remote sensing data (eStation, http://estation.
jrc.ec.europa.eu/) are in development or already available, like a webbased ool for mapping land-cover change (see Bastin et al., 2012, http://
landcover-change.jrc.ec.europa.eu/). These fundamental components,
Fig. 1. Design of the web processing service for forecasting climate change impact on the ecological niche of bird species.
Please cite this article as: Skøien, J.O., et al., A Model Web approach to modelling climate change in biomes of Important Bird Areas, Ecological
Informatics (2012), http://dx.doi.org/10.1016/j.ecoinf.2012.12.003
J.O. Skøien et al. / Ecological Informatics xxx (2012) xxx–xxx
5
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).
when linked with each other, should allow us to serve a broad range
of end-users active in conservation, from park-managers to policy
makers. While, ideally, one should be able to combine any of these
tools 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 linked
with each other when building more complex modelling chains. Therefore, by orchestrating the developments and interactions of a larger
number of interoperable services managed by different institutions,
the DOPA can be seen mainly as an initiative ensuring a minimum of
coordination among key data and model providers to encourage a
higher reusability of the services proposed.
The Web Processing Service described in this paper is extremely
flexible and able to service most kinds of request related to species
distribution modelling. The communication with the service is rather
cumbersome for most users though, as the request will have to be an
XML document that properly defines which data sets to use and the result is also an XML document. To simplify access to the WPS for more
occasional users and for demonstration purposes, we implemented
the Web Client presented in this paper. As a front end, the proposed
Web Client is expected to help the user to select combinations of data,
as well as additional thematic layers that can be useful for decisionmaking. This front end is on the one hand limiting the flexibility for
the users but, on the other hand, also dramatically simplifies the choice
of input data as well as of the climate change model. The possibility to
develop web based services with different front ends for people with
different skills is a huge asset for capacity building activities in conservation. One can indeed easily customise interfaces to provide tools
that are tailored to the different end-users who will then not be
required to undergo any time consuming training, provided there is
an adequate documentation available, or have to worry about programming and maintaining complex informatics infrastructures. The WPS
allows anyone to develop their own client to access the WPS with a different combination of data, and we have ourselves already developed
other clients that are consuming the same service to answer slightly
different questions (Dubois et al., 2011). This includes a client that
accepts any Web Coverage Service as an input. This option is important
considering the dramatic increase of available dataset over the internet
(Group on Earth Observations, 2011). The number of data services in
their catalogue increased from less than 200 in 2010 to over 1500 in
November 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 desktop. On the other hand, the this architecture is still depending on a
reasonably good access to the internet, an issue that should not be
neglected, in particular for conservation activities in developing countries or in remote locations.
In terms of the drawbacks of the service oriented architecture, a
limitation is that accessing the WPS without a Web Client may be
seen as an obstacle at first sight by non experts. Many practitioners
and researchers will probably need to collaborate with a programmer,
either for writing the appropriate XML requests, or by writing a new
Web Client that would match the user's needs. The huge advantage of
the latter approach is that reproducibility is guaranteed if all (test)
datasets are available online. Another benefit is that the approach will
also be accessible to other potential users. And whereas computational
resources and network access can be restrictive for publishing WPSs,
Web Clients will in this case only be responsible for communication of
model parameters and the address to Web Services with data. There
are also many other initiatives currently ongoing that aim to simplify
this process, including the orchestration software Taverna (de Jesus
et al., 2012).
The Mahalanobis method is one of several SDMs that can be applied
to model similarity between regions in space and time. We have for
simplicity chosen to use the Mahalanobis distance in this paper, but
could easily exchange this method with for example MaxEnt (Phillips
et al., 2006). However, the effectiveness of these methods still has
some limitations (Sinclair et al., 2010). The most important one is that
they predict potential range based on the variability of the environmental indicators at the locations. If this variability is low for a certain
variable, none of these methods are able to recognize whether this is because a species is particularly sensitive to this variable, or if this variable
just happens to have a low variability and there is another variable that
actually limits the habitat.
The difference between potential and realized niche is even more
important when we apply SDM methods to conservation areas such
as PAs and IBAs (Hartley et al., 2007; Nelson et al., 2009). If the covariance matrix in the application of the Mahalanobis distance is only
dependant on the borders of the area, then it is likely that the tolerance
for change of the protected species for most conservation areas will be
underestimated. For these cases, an approach that also takes into
the account the occurrences or the predicted species maps should be
Please cite this article as: Skøien, J.O., et al., A Model Web approach to modelling climate change in biomes of Important Bird Areas, Ecological
Informatics (2012), http://dx.doi.org/10.1016/j.ecoinf.2012.12.003
6
J.O. Skøien et al. / Ecological Informatics xxx (2012) xxx–xxx
considered. This involves only a small modification of the Web Client,
whereas the same WPS can be used.
In relation to the data used for climate change forecasting, the
present study used the HADCM3 model for illustration purposes. Different models, scenarios and resolutions can easily be implemented
and used for comparison purposes. The uncertainties in these forecasts
are of course high, but by comparing the results of different models and
scenarios, the user may find here a simple way to assess model uncertainties. This last point is an important fundamental issue, but beyond
the scope of this paper. Uncertainties in data and models do propagate
between chained services and there is a need to document these uncertainties at all stages. Skøien et al. (2011) discuss how uncertainties can
be described and quantified within eHabitat. This is not yet included in
the client described in this paper, but will be included in the near future
as uncertainties assessments are essential for decision-making and for
research.
Acknowledgements
This work is partly supported by the European Commission, under
the 7th Framework Programme, by the EuroGEOSS project funded by
the DG RTD. The views expressed herein are those of the authors and
are not necessarily those of the European Commission. We would also
like to thank an anonymous reviewer and the editor Mark O'Connell
for useful comments and suggestions. More information about the
DOPA and eHabitat can be found on the Internet, see http://dopa.jrc.
ec.europa.eu/ and http://ehabitat.jrc.ec.europa.eu, respectively.
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Please cite this article as: Skøien, J.O., et al., A Model Web approach to modelling climate change in biomes of Important Bird Areas, Ecological
Informatics (2012), http://dx.doi.org/10.1016/j.ecoinf.2012.12.003