andrei verdeanu (2012) - an application of gis modelling in assessing potential habitat areas for...

7/31/2019 Andrei Verdeanu (2012) - An Application of GIS Modelling in Assessing Potential Habitat Areas for Wild Boar, Sus Sc

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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.


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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


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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


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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/


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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


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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.


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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).


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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)


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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


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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 40%- Hydrography 15%- Transportation network 10%- Elevation 10%- Topographic wetness index 25%


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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


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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


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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


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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.html

andrei verdeanu (2012) - an application of gis modelling in assessing potential habitat areas for...

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