7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
1/15
1
An application of GIS modelling in assessing potential habitat
areas for wild boar, Sus scrofa (Linnaeus 1758)by Andrei Verdeanu
Abstract
This paper is an attempt to demonstrate a simple application of GIS modelling in the field of
biology, for establishing potential habitat areas of a certain species. I have selected wild boar because of
data availability and also because it could be considered a dominant species across the area where I was
about to apply the modelling my bachelor thesis area. The input data used for modelling consisted of
the geospatial layers representing the factors responsible for the species distribution (digital elevation
model, Corine Land Cover, hidrography, roads and railways network). The layers used were considered
to describe the ecological requirements of the species. I constructed 5 models/scenarios, each comprising
of unique combinations of the respective factors, with different influence percentages. Using the
suitability indexes obtained via the models, I devised a grading scale for the suitability of the areas. Once
the models were established, a validation of their efficiency and accuracy was needed. To do so, I used
two sets of points data, personal observations and random generated points. For each model, I measured
the number of points overlapping over each suitability class and expressed it in percentages. For
evaluating the models, the percentages were compared and the best model was selected considering
certain criteria.
Keywords: GIS modelling, potential habitat, suitability, land cover, wild boar.
Acknowledgements
The approach presented in this paper is inspired by the work of A. Belda, B. Zaragoz, J. E.
Martnez-Prez, V. Peir, A. Ramn, E. Seva & J. Arques (2011): Use of GIS to predict potential distribution
areas for wild boar (Sus scrofa Linnaeus 1758) in Mediterranean regions (SE Spain), Italian Journal of Zoology,
DOI:10.1080/11250003.2011.631944, which was mainly consulted in order to have some references
regarding the ecological requirements of the species, since this article contained specific data on that
topic. The article was used as an example and the copyright of the authors was fully respected. The
present paper does not attempt to replicate or copy any of the methods used in the article or results, any
similarities which may have arisen are purely coincidental or dictated by the standard GIS methodology
applied in the field of biology.
Also, I would like to show my gratitude to the following:
My project supervisor, Peder Klith Bcher, Senior Scientist, PhD, GIS Coordinator, Ecoinformatics
and Biodiversity Group, Dept. of Biological Sciences, Aarhus University , Jens-Christian Svenning, Professor,
PhD, Dept. of Biological Sciences, Aarhus University, Mihai Niculita, Teaching assistant, PhD, Faculty of
Geography and Geology, Dept. of Geography, University Al. I. Cuza, Iasi, Romania, for their help and input on
the project.
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
2/15
2
Introduction
Considering such a topic for a
biological project was much related to the
fact that my bachelor thesis is using
intensively GIS techniques and methods.This project was a good opportunity to use
all the data that I have been working on
already, as a basis over which to apply
certain methodological procedures and
derive from the existing digital layers even
more useful information. Since all the
available data that I have already worked
on was on a very detailed level, this was
even better to use for such an application.I have chosen the wild boar for this
project because of a few different reasons:
across my study area, it can be considered a
quite dominant and widespread species;
during the last 15 years or so, I actively did
hiking across the whole extent of my study
area, and I had numerous encounters with
the species, much more compared to other
ungulates inhabiting the area. I kept good
record of the areas of encounter and areas
where I was able to identify occurrence by
specific signs (hoof tracks, feces, tramping
and rooting of the soil litter); at the time, it
was the only species for which I have found
specific data regarding the ecological
requirements (see Acknowledgements);
since it is a game species, the project may
also have an outcome regarding possible
management and conservation strategies for
the wild boar.
The study area
The area - figure 01, is located in the
Eastern Carpathian Mountains, Romania.
The extent (373km), delineates the valley
Fig. 01 Geographical location of the study area
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
3/15
3
of the Bistrita river in the section between
the locality of Poiana Teiului (north-west)
and the city of Piatra Neam (south-east) -
which is the largest city contained in the
study area, along the city of Bicaz (south).
The area rests at the interference between
the low altitude hills and valley depressions
in the east, extreme south-east and the
medium-high mountains rising in the west
side. There are a few notable reservoirs on
the river, which were primarily constructed
for hydro-energy. The biggest of them all,
Izvorul Muntelui lake, constructed in the
1960s, has brought with it new land
characteristics and because of its
impressive size (length 34km, area
33km, med. depth - 36m, max. depth - 97m,
volume - 1,250mil m) a very specific
topoclimate in the surroundings. The valley
perimeter was extracted by automatically
generating watersheds in the area and
manual filtering by certain criteria of size.
After the main drainage area was
established, a buffer area of 1000m was
generated around it, this way obtaining the
river valley in the respective sector. The
altitude in the area ranges from 291m in the
south-east up to 1273m in the extreme
north-west. Since it encompasses a good
variety of landscape types and relief, the
area is even better suited as a background
for applying species habitat related
methodology. Also, the land cover is
diverse and well distributed in the area,
both attitudinally and longitudinally
figure 02 and 03 land cover percentages of
the area (as calculated from Corine Land
Cover 2006).
Fig. 02 Land cover percentages of the study area
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
4/15
4
Materials and methods
The main goals of this paper are toidentify, weigh and combine the factors
(variables) which dictate the habitat range
and distribution of the wild boar in the
study area. By using this technique, the end
result will be a map emphasizing the
potential habitat areas and their suitability
index. A basic workflow for such an
approach can be seen in figure 04 a HSI
model (Habitat Suitability Index) from theUnited States Environmental Protection
Agency.
The model I developed in this paper
follows pretty much this type of structure.
These are the steps I followed in my
approach:
Fig. 03 Land cover of the study area
Source: US Environmental Protection Agency -http://www.epa.gov/
Fig. 04 Basic Habitat Suitability Index model workflow (HSI)
http://www.epa.gov/http://www.epa.gov/http://www.epa.gov/http://www.epa.gov/7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
5/15
5
a) The purpose of the model: todetermine potential habitat areas
and assess their suitability;
b) Informational input: literature andinternet resources;
c) Determining and choosing thevariables:
- Elevation- Proximity to water resources- Proximity to road and railway
network
- Land cover- Topographic wetness index;
d) Introducing the variables into aGIS environment: ArcGIS 10, TNT
Mips 6.9;
e) Weighing of the variables:reclassification method;
f) Combining the variables: weightedsum and weighted overlay;
g) Generating multiple scenarios: 5models/scenarios;
h) Validating and evaluating themodels: random generated points
and personal occurrence data;
i) Choosing the best model: the onewhich emphasizes best the potential
habitat areas according to certain
criteria.
Further on I will discuss in detail
each of the above steps.
Determining the purpose of the model
Since I worked at such a detailed
scale and all the occurrence data available
on the web is at a much coarser resolution, I
didnt do the classic approach, where the
model is constructed starting with
occurrence data, and I preferred a model
which predicts habitat areas by referring
only to the environmental characteristics,
which dictate the species habitat areas. I
used, however, the few occurrence data I
had for the validation of the models.
Informational input
For determining the species
environmental characteristics and
requirements I made use of various
literature and web resources. For the
ecological requirements in particular, I used
as a reference and starting point, the data
series I found in the article A. Belda, B.
Zaragoz, J. E. Martnez-Prez, V. Peir, A.Ramn, E. Seva & J. Arques (2011): Use of
GIS to predict potential distribution areas for
wild boar (Sus scrofa Linnaeus 1758) in
Mediterranean regions (SE Spain). Since the
study refers to a mediterranean area, I
adapted the values found in the article
according to literature and in regard to my
study area, which is temperate.
Determining and choosing the variables
Considering the area of choice and
the species characteristic requirements
(literature consulted), I settled on five
factors/variables:
1) Elevation the altitude range in thearea is 291-1273m, and combined with the
slope and the other aspects of the terrain, it
has a significant impact on the species.
2) Proximity to water resources thehydrographic network is well developed
across the study area, and the fragmentation
it induces in the terrain could have
significant importance on the species
distributions. Being the single water
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
6/15
6
resource available for the species, their
presence in the model is mandatory as it
dictates many of the species behavioral
characteristics.
3) Proximity to road and railwaynetwork as with the hydrographic
network, the transportation network is an
important factor in the species distribution.
Mainly because it acts as a physical barrier
and divergence mechanism (since it
determines the overall movement pattern of
the species). However, in the present paper
I didnt dealt with the movement barrier
approach, but I used this variable taking
into account its anthropic nature and
repellent properties for the species.
4) Land cover probably the mostimportant factor of all, the species
distribution is directly related to the nature
of the topographical surface, but most
importantly the type of land cover. It affects
most of the sectors in the species life since it
is the basal layer over which all the
processes within the species life regime take
place. The nature of the land cover dictates
movement, feeding, resting, mating, etc. of
the species. Since I used Corine Land Cover,
it is more than a land use type of layer, and
it contains also the anthropic transformed
land types which have a great impact on the
species habitat.
5) Topographic wetness index although it may have the same output as the
hydrography factor, it however takes a
sensu lato approach by its nature, linking
the slope, soil characteristics, drainage
capacity and so on. It could have a more
detailed aspect than just using the
hydrographic network as a factor. Not only
it predicts the areas more prone to higher
levels of water abundance, but it does so by
linking it with the terrain which is a good
aspect considering that the terrain itself is
sometimes a limiting or advantageous
factor for the species. By combining the
terrain with the water availability the model
will be much more realistic, since the water
resources and the terrain will counter-
balance themselves and the final availability
output for the species will be different than
that of the hydrography itself.
Introducing the variables into a GIS
environment
The GIS software used in this paperwas mostly ArcMap 10 from ESRI and in a
few isolated instances TNT Mips 6.9 from
MicroImages (mainly for the manual vector
extraction). Next I will briefly present the
equivalent digital layers I used to represent
each of the variables in the model:
1) Elevation I used a digital elevationmodel which I had previously constructed
manually by extracting contours from a
topographical map of the area (1:25000). The
resolution of the raster cell was 4m. (Notice
in the final map layouts I overlaid both a
SRTM DEM with a 90m resolution and the
detailed DEM, only for display purposes).
2) Proximity to water resources forthis layer I used the hydrography of the
area which I had also previously manually
extracted as a vector file from the
topographical map. The drainage network
was not extracted.
3) Proximity to road and railwaynetwork the same as with the
hydrography, I used the vector files
manually extracted from the topographical
map.
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
7/15
7
4) Land cover for this layer I used theCorine Land Cover seamless vector data -
version 15 (08/2011) downloaded from the
European Environment Agency site.
5) Topographic wetness index fromthe DEM I manually constructed, I derived
with the help of my supervisor, a TWI raster
layer. The TWI was calculated with regard
to the slope, as the logarithm of the
slope/aspect ratio.
Weighing of the variables
Before going any further with the
explanations I must state that from this
point on all the examples of the GISprocedures applied in this paper will be
exemplified on a detailed portion of the
study area, from the lower end of the extent.
(see figure 01). This area was chosen since it
contains almost all the characteristics found
across the whole extent of the study area,
compressed into a small patch. Fair
altitudinal range, good development of the
hydrographic network (including areservoir) as well as the transportation
network, and a great variety of land cover.
Another reason for choosing this detailed
patch is the relative proximity to the biggest
city across the study area, which is located
just east of the reservoir. This could have an
interesting outcome in the final model.
As stated before, in order to create a
map that emphasizes the potential habitat
areas and their respective suitability index,
all the layers representing the variables
need to be combined, with different
influence percentages. But before this final
step, each of the variables needs to be
reclassified according to the species
ecological requirements. In figure 05 you
can see the representation of each of the
variables layer before, and after the
reclassification, and also the old and new
range of values.
The layers are from top to bottom
a) Digital elevation model, b) Hydrography,
c) Transportation network, d) Corine Land
Cover and e) Topographic wetness index.
All the new suitability values
attributed to the variables are integer
values, from 0 to 100 (percentile range). This
way I avoided the need for further data
standardization. Each of the variables was
reclassified using the Reclassify toolset
from ArcMap 10, and the Value field was
accessed. Further on I will explain the
reclassification process for each of the
variable:
1) Digital elevation model since thealtitudinal range in the area was 291-1273m,
I established a median habitat niche (band).
The altitude at which the urban structure
begins to disperse is ~400m and the altitude
at which the forest density decreases and
the vegetation is replaced by shrubs and
pastures was ~900m. This gives us an
optimum altitudinal range of 500m
(between 400-900m) which took the
maximal suitability value of 100 and the less
probable range which is either below 400m
or above 900m, took the value of 20.1
2) Hydrography for this layer I usedthe vector file and I constructed buffer areas
around the hydrographic network, in two
ranges, from
1 A. Belda, B. Zaragoz, J. E. Martnez-Prez, V. Peir, A.
Ramn, E. Seva & J. Arques (2011): Use of GIS to predict
potential distribution areas for wild boar (Sus scrofa Linnaeus
1758) in Mediterranean regions (SE Spain).
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
8/15
8
Fig. 05 The geospatial layers - a) Digital elevation model, b) Hydrography, c) Transportation
network, d) Corine Land Cover, e) Topographic wetness index, before and after reclassification
(left to right), exemplified on the detailed study area patch
a)
b)
c)
d)
e)
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
9/15
9
0-50m and 50-200m. The suitability values
were: 0-50m > 90, 50-200m > 60, over
200m > 30.2
3) Transportation network the sameas with the hydrography layer, I
constructed buffer areas around the roads
and railways network in two ranges, from
0-50m and 50-200m. The suitability values
were inverted in this case (since there is a
negative correlation between proximity to
the transportation network and the species
abundance) 0-50m > 30, 50-200m > 60,
over 200m > 90.2
4) Corine Land Cover this is one ofthe most important layer, if not the most
important one, because of the inherit impact
of the land characteristics to the species
distribution.
2 A. Belda, B. Zaragoz, J. E. Martnez-Prez, V. Peir, A.
Ramn, E. Seva & J. Arques (2011): Use of GIS to predict
potential distribution areas for wild boar (Sus scrofa Linnaeus
1758) in Mediterranean regions (SE Spain).
The land cover types (and their CLC code
equivalent and surface area) and the new
suitability values assigned are shown in
Table 1.2 All the values assigned were
adapted to my temperate area according to
literature.
5) Topographic wetness index theresulted TWI raster layer derived from the
DEM had the range of 1-31. This was
reclassified as follows: 1-7 > 10, 7-14 > 40,
14-21 > 70, 21-32 > 100.2 Although
presented here as an independent variable,
the TWI was used only in a single scenario
out of 5, for reasons I will detail later on.
Since all the variables were re-classifiedusing the same value scale, there is no need
for further data standardization, all values
being integers.
Table 1 Reclassification of the Corine Land Cover geospatial layer
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
10/15
10
Combining the variables
After all the layers were reclassified,
the next step was to combine them in
different ways to achieve the final potential
habitat map. All the combinations were
done in ArcMap 10, and, for better results,each of the combinations was done using
three different methods, this way, verifying
the accuracy of the results and guaranteeing
similar results.
First, the variables were combined
using a Raster Calculator by means of a
simple weighted sum. Then the combining
was done again using this time the
dedicated Weighted Sum toolset, andfinally, one more time using the Weighted
Overlay toolset. After comparing the
results, all of the methods gave the exact
same results. The Weighted Overlay tool
was chosen to be used for all of the
scenarios that were about to be generated.
Generating multiple scenarios
I decided to construct five different
scenarios, in which I tried to use unique
combination of the variables (chosen
randomly), this way ensuring that more
real-life situations were being covered. Also
by modifying the weight percentages of the
variables, their respective counter-balance
effect for the other variables was changed,
thus revealing possible singular effects
which could be quite relevant in the species
distribution. The first four models do not
incorporate the TWI. I included the
Topographic Wetness index only in the fifth
model since it induced great scatter in the
final output of the models (although being
displayed as classified and not stretched, its
very dispersed nature caused the final
output representation of the model to be
somewhat diffuse in some areas. Therefore I
used it only as a fail-safe test in the last
model, in order to have it accounted for in
at leas one model. At the previous tests I
made, the main effect of adding TWI to the
models, besides the diffuse display, was
emphasizing the river valleys as positive
areas which is quite redundant, as it is
overlapping the hydrography buffer areas
which are showing the same thing.
Further on I will present each of the
variable combination and their respective
influence percentages for each model:
Model 1
- Corine Land Cover 60%- Hydrography 15%- Transportation network 15%- Elevation 10%Model 2
- Corine Land Cover 50%- Hydrography 10%- Transportation network 10%- Elevation 30%Model 3
- Corine Land Cover 80%- Hydrography 10%- Transportation network 5%- Elevation 5%Model 4
- Corine Land Cover 30%- Hydrography 50%- Transportation network 10%- Elevation 10%Model 5
- Corine Land Cover 40%- Hydrography 15%- Transportation network 10%- Elevation 10%- Topographic wetness index 25%
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
11/15
11
Results
Each of the five resulted models had
different suitability index numerical ranges.
I classified each model into 6 suitability
index classes, keeping however, the
different numerical values for each of the
ranges. The different ranges appeared
because of the different variable
combinations. (i.e. for the first model it
ranged 11-97, for model 3 0-99, etc.). It is the
expression of each of the models and a
standardization of all the classes would not
bring justice to the realistic side of the
models. It was better to keep the numerical
accuracy, at least for the comparison of the
efficiency of the models.
Validating and evaluating the models
In order to choose the best model,
each of the model needed to be validated
and evaluated. To do so, I made use of two
sets of occurrence data, one made up of
random generated points and one with
personal occurrence observations.
To further explore the results, we
calculated a series of metrics that define the
distances between sites, and the area
occupied, in both environmental and
geographic space. [] We used the 10 000
random points and the presences in the
evaluation data set and calculated the
median of the minimum distances between
any one random point and all the presence
points.3
Since my model is not based on
presence-absence data, on the contrary, it
3 Elith, Jane, Graham, Catherine H., Anderson, [], (2006)
Novel methods improve prediction of species' distributions from
occurrence data. Ecography, 29 (2). pp. 129-151. ISSN 1600-
0587
tries to develop the habitat areas starting
with the environmental factors, I could not
use such an approach to validate the model.
However, I adapted the random points and
occurrence observations approach to a more
simple design, such as suggested here:Most habitat-association studies
use a very restricted set of error measures,
of which percentage overall accuracy is the
most common. (e.g., Brennan et al. 1986;
Capen et al. 1986; Verbyla & Litvaitis 1989;
Donzar et al. 1993)4
For each of the suitability classes, I
devised an equivalent grading scale to use
in the assessment of the efficiency of each of
the models:
- 1st class Very low probability- 2nd class Low probability- 3rd class Medium probability- 4th class Good probability- 5th class High probability- 6th class Very high probability
The two point datasets wereobtained as follows:
300 random points were generated
automatically using the Create Random
Points tool in ArcMap 10, across the whole
extent of the study area, with a conditional
distance of 400m between each of the
points.
The personal observations (200
points) were manually inserted using thetopographical map as a reference.
4Alan H. Fielding, John F. Bell, (1997) A review of methods for
the assessment of prediction errors in conservation
presence/absence models, Environmental Conservation 24 (1):
3849 1997
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
12/15
12
For the validation of the
models, I measured for each one, the
number of points overlapping each
suitability class, using the Extract
Multi Values to Point tool in
ArcMap 10, then derived percentages
for each one and compared the
models. This way, by knowing the
percentage of points from the total
number overlapping each of the
class, I could evaluate the efficiency
and accuracy of the models figure
06 the percentages for each model,
both personal observations and
random points.
Choosing the best model
For evaluating the models I
divided the grading scale into two
ends the positive end, which
includes the Very high probability,
High probability and Good
probability classes and the negative
end, which includes the remaining
three lower classes Medium
probability, Low probability and
Very low probability. The model
which had the best representation of
the positive end was designated as
being the best. Since we are
interested in a positive correlation of
the points and suitability areas, only
the top three classes would give us
the assessment of the correlation. In
figure 07 we can see the correlations
of the models plotted, for both data
sets and ends.Fig. 06 Percentages of the points data set overlapping each suitability class
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
13/15
13
What we need to see, in order to
identify a good model, is a high overall
expression of the positive end and the
lowest possible overall expression of the
negative end. If we analyze the graphs
above, we can observe the following:
For the personal observations
dataset, ~ Model 1 ~ is the best model,
because it has the highest expression in the
positive end and the lowest expression in
the negative end.
For the random points dataset, ~
Model 2 ~ is the best model, since it has the
highest expression of the positive end and
the lowest expression of the negative end.
The final maps for the two models,
as well as a detailed view of the study area
patch are shown in figure 08. Although their
suitability index range is slightly different,
their graphic expression is somewhat
similar. All the maps are projected in
Stereo70/Dealul Piscului 1970, 10km grid forthe large maps and 1km for the detailed
patch.
Discussion
As expected, the resulted models follow
quite well the characteristics of the land
contained in the study area. Nevertheless, this is
just a potential habitat map, and in certain
locations the criteria used in determining thesuitability of those areas remains hypothetical.
Take for instance the inland marshes (wet lands)
represented as having near optimal habitat
suitability center of the detailed patch figure
08. Assessing those areas with such a high
suitability index was based on the nature of the
land cover, and indeed there have been
0% 20% 40% 60% 80% 100%
Model 1
Model 2
Model 3
Model 4
Model 5
Personal observations - positive end
Very high probability High probability Good probability Total
0% 20% 40% 60% 80% 100%
Model 1
Model 2
Model 3
Model 4
Model 5
Random points - positive end
Very high probability High probability Good probability Total
0% 10% 20% 30% 40% 50%
Model 1
Model 2
Model 3
Model 4
Model 5
Personal observations - negative end
Medium probability Low probability Very low probability Total
0% 10% 20% 30% 40% 50%
Model 1
Model 2
Model 3
Model 4
Model 5
Random points - negative end
Medium probability Low probability Very low probability Total
Fig. 07 Expression of the models on the two suitability ends, both personal observations and random points
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
14/15
14Fig. 08 Model 1 and 2 maps, large and detailed patches
7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc
15/15
15
numerous wild boar occurrences in those areas.
However, the area is contained within
progressively denser urban surface and water
bodies. The circulation in and out of that area is
impaired, but still the species is present there.
This means that in order to develop even more
the model, we need to take into accountmovement patterns, anthropic barriers or transit
corridors and perform cost-distance analyses.
Since the present paper wants to be, at least in
this stage, a general theoretical example of
applying such modeling techniques, there is
room for improvements and refinements. For
instance, the fact that the influence percentages
for each of the model were chosen randomly,
with the purpose in mind to cover as many
aspects as possible. In a real life application,these percentages need to be scientifically
supported by certain clearly defined reasons.
Otherwise, the random approach would be to
generate much more models, in which to cover
almost all possible combinations, but that would
take a considerable amount of resources and
time; in the TWI reclassification, there was the
need to add a mask in the process, since the
water surface appears as having maximum
suitability, because it inherits it from the
neighboring areas; also, for the evaluation of the
models I used a very simple method to assess
their accuracy efficiency, but more often ROC
curve or a confusion matrix are being used in
such cases, but for now, my limited expertise
did not allowed me to apply such techniques.
The models can be enhanced and perfected in a
future, more developed attempt.
Overall, the goal set at the beginning of
the paper was achieved, producing the desired
maps using the materials imposed along the
way.
All the maps for each of the model are
available in high resolution as supplementary
paper information.
References
A. Belda, B. Zaragoz, J. E. Martnez-Prez, V. Peir,A. Ramn, E. Seva & J. Arques (2011): Use of GIS to
predict potential distribution areas for wild boar (Sus
scrofa Linnaeus 1758) in Mediterranean regions (SE
Spain), Italian Journal of Zoology,
DOI:10.1080/11250003.2011.631944
Bolstad Paul, (2007), GIS Fundamentals: A First Text on
Geographic Information Systems, Third Ed.
Chengzhi Qin, A-xing Zhu, Lin Yang, Baolin Li, Tao
Pei, (2010), Topographic Wetness Index Computed
Using Multiple Flow Direction Algorithm and Local
Maximum Downslope Gradient.
Elith, J., Graham, C. H., Anderson, R. P., Dudk, M.,
Ferrier, S., Guisan, A., Hijmans, R. J., Huettmann,F., Leathwick, J. R., Lehmann, A., Li, J., Lohmann,
L. G., Loiselle, B. A., Manion, G., Moritz, C.,
Nakamura, M., Nakazawa, Y., Overton, J. McC.,
Peterson, A. T., Phillips, S. J., Richardson, K. S.,
Scachetti-Pereira, R., Schapire, R. E., Soberon, J.,
Williams, S., Wisz, M. S. and Zimmermann, N. E.
2006. Novel methods improve prediction of species
distributions from occurrence data., Ecography 29:
129-151
Fielding Alan H., Bell John F., (1997) A review of
methods for the assessment of prediction errors in
conservation presence/absence models, Environmental
Conservation 24 (1): 3849 1997
R. Srensen, U. Zinko, J. Seibert, (2005), On the
calculation of the topographic wetness index:
evaluation of different methods based on field
observations.
Internet resources
Animal Diversity Web -
http://animaldiversity.ummz.umich.edu/site/accounts/in
formation/Sus_scrofa.html
CORINE Land Cover (2006) -
http://www.eea.europa.eu/data-and
maps/data#c12=corine+land+cover+version+13
Encyclopedia of Life
http://eol.org/pages/328663/details
The IUCN Red List of Threatened Species -
http://www.iucnredlist.org/technical-
documents/classification-schemes/habitats-classification-
scheme-ver3
ZipCodeZoo
http://zipcodezoo.com/Animals/S/Sus_scrofa/
US Environmental Protection Agency -http://www.epa.gov/
http://animaldiversity.ummz.umich.edu/site/accounts/information/Sus_scrofa.htmlhttp://animaldiversity.ummz.umich.edu/site/accounts/information/Sus_scrofa.htmlhttp://animaldiversity.ummz.umich.edu/site/accounts/information/Sus_scrofa.htmlhttp://www.eea.europa.eu/data-and%20maps/data#c12=corine+land+cover+version+13http://www.eea.europa.eu/data-and%20maps/data#c12=corine+land+cover+version+13http://www.eea.europa.eu/data-and%20maps/data#c12=corine+land+cover+version+13http://eol.org/pages/328663/detailshttp://eol.org/pages/328663/detailshttp://www.iucnredlist.org/technical-documents/classification-schemes/habitats-classification-scheme-ver3http://www.iucnredlist.org/technical-documents/classification-schemes/habitats-classification-scheme-ver3http://www.iucnredlist.org/technical-documents/classification-schemes/habitats-classification-scheme-ver3http://www.iucnredlist.org/technical-documents/classification-schemes/habitats-classification-scheme-ver3http://zipcodezoo.com/Animals/S/Sus_scrofa/http://zipcodezoo.com/Animals/S/Sus_scrofa/http://www.epa.gov/http://www.epa.gov/http://www.epa.gov/http://www.epa.gov/http://zipcodezoo.com/Animals/S/Sus_scrofa/http://www.iucnredlist.org/technical-documents/classification-schemes/habitats-classification-scheme-ver3http://www.iucnredlist.org/technical-documents/classification-schemes/habitats-classification-scheme-ver3http://www.iucnredlist.org/technical-documents/classification-schemes/habitats-classification-scheme-ver3http://eol.org/pages/328663/detailshttp://www.eea.europa.eu/data-and%20maps/data#c12=corine+land+cover+version+13http://www.eea.europa.eu/data-and%20maps/data#c12=corine+land+cover+version+13http://animaldiversity.ummz.umich.edu/site/accounts/information/Sus_scrofa.htmlhttp://animaldiversity.ummz.umich.edu/site/accounts/information/Sus_scrofa.htmlTop Related