JOURNAL OF ENVIRONMENTAL GEOGRAPHY 

Journal of Environmental Geography 6 (1–2), 21–27. 

DOI: 10.2478/v10326-012-0003-3 

ISSN: 2060-467X 

 
 

 
 

 

 

MODELING THE IMPACTS OF CLIMATE CHANGE ON PHYTOGEOGRAPHICAL 

UNITS. A CASE STUDY OF THE MOESZ LINE 
 

 

 
Ákos Bede-Fazekas 

 

Department of Garden and Open Space Design, Faculty of Landscape Architecture, Corvinus University of Budapest,  

Villányi út 29-43, H-1118 Budapest, Hungary 

e-mail: bfakos@gmail.com 

 

Research article, received 29 January 2013, published online 15 April 2013 

Abstract 

Regional climate models (RCMs) provide reliable climatic predictions for the next 90 years with high horizontal and temporal resolu-

tion. In the 21st century northward latitudinal and upward altitudinal shift of the distribution of plant species and phytogeographical 

units is expected. It is discussed how the modeling of phytogeographical unit can be reduced to modeling plant distributions. Pre-

dicted shift of the Moesz line is studied as case study (with three different modeling approaches) using 36 parameters of REMO 

regional climate dataset, ArcGIS geographic information software, and periods of 1961-1990 (reference period), 2011-2040, and 

2041-2070. The disadvantages of this relatively simple climate envelope modeling (CEM) approach are then discussed and several 

ways of model improvement are suggested. Some statistical and artificial intelligence (AI) methods (logistic regression, cluster 

analysis and other clustering methods, decision tree, evolutionary algorithm, artificial neural network) are able to provide develop-

ment of the model. Among them artificial neural networks (ANN) seems to be the most suitable algorithm for this purpose, which 

provides a black box method for distribution modeling. 

Keywords: climate change, REMO, Climate envelope model, phytogeography, Moesz line, model improvement 

 

INTRODUCTION 

The latest regional climate models (RCMs) have high 

horizontal resolution and good reliability. They provide 

projections for the Carpathian Basin that are related to 

botany (Czúcz, 2010), landscape architecture (Bede-

Fazekas, 2012a), and forestry (Mátyás et al., 2010; Führer 

et al., 2010; Czúcz et al., 2011). Our future climate, which 

is likely to be warmer, dryer in summer, and have more 

extreme precipitations in the colder half-year term 

(Bartholy et al., 2007; Bartholy and Pongrácz, 2008), will 

enforce changes in the composition of the natural and the 

planted vegetation. The landscape architecture can have a 

significant role on the mitigation. We should note, 

however, the importance of adaptation, since climate 

change cannot be compensated by the intensive garden 

maintenance (Bede-Fazekas, 2011). One of the most 

important tools from the adaptation toolkit of landscapes 

architecture is the reconsideration of the ornamental plant 

assortment. There are some papers dealing with this issue 

(Schmidt, 2006; Szabó and Bede-Fazekas, 2012).  

Geographical visualization can be produced with 

GIS (Geographic Information System) software based on 

the large amount of tabulated data of the different climate 

models, which might be interpretable not only by experts. 

They are able to visualize the direction and the volume of 

climate change also for non-professionals (Czinkócky 

and Bede-Fazekas, 2012). This is true in case of different 

modeling themes, such as the distribution area of the 

Mediterranean plant species; the distribution area of the 

plant species migrating northwards from the Carpathian 

Basin; and the phytogeographical units and borders that 

may shift from or shift to the Carpathian Basin. 

Phytogeography, a branch of biogeography, is concerned 

with the distribution area of plant species, communities 

and floras. This paper summarizes the experiences gained 

by the model run on the Moesz line as a case study and 

highlights the possible improvements of the model, 

including the application of Artificial Intelligence (AI) 

algorithms. 

There are really few models that have studied the 

assortment of plants able to spread through or be 

introduced in the Carpathian Basin in the 21
st
 century. 

There are, however, numerous researches that have 

connection with these modeling approaches. The 

research of Horváth (2008a) about finding the territories 

having similar climate nowadays to the future climate of 

Hungary has high importance. He has found that these 



22 Bede-Fazekas (2013)  

 
spatially analogue territories are, for the next 60 years, 

the following: South Rumania, North Bulgaria, Serbia, 

and North Greece (Horváth, 2008b). By studying the 

vegetation and ornamental plant assortment of the 

analogue territories we can estimate the future vegetation 

and the possibilities of ornamental plant usage in the 

future in Hungary. 

Among forestry species the distribution of beech 

(Fagus sylvatica L.) has been modeled (Führer, 2008). The 

impacts of climate change on the natural vegetation and 

habitats were studied by Kovács-Láng et al. (2008) and 

Czúcz (2010). Artificial neural network, is one of the 

artificial intelligence methods described further as a 

recommended improvement of the model, was used for 

modeling the inland excess water by Van Leeuwen and 

Tobak (2008). 

Apart from Hungary, there can several researches 

be found using similar methods to that ones I suggest 

in the Discussion. One of the most significant is the 

work of Arundel (2005), which is about finding the 

climate envelope of five warm-demanding species of 

North America by significance analysis. Modeling 

was, however, not carried out by him. Berry et al. 

(2002) modeled the distribution of 54 species and the 

composition of 15 habitats of Ireland and Great 

Britain. Harrison et al. (2010) studied the potential 

composition change of the vegetation of Oregon. The 

distribution of 134 North American tree species was 

modeled by Iverson et al. (2008) with the use of 

regression trees. Stankowski and Parker (2010) found 

that regardless of distributional and environmental 

data, there is not any algorithm which could maximize 

model performance for all species; thus different spe-

cies demand different models. Guisan and Zimmer-

mann (2000) give full review of the methods that can 

be used for ecological modeling. 

METHODS OF MODELING 

The approach of modeling the shift of phytogeographical 

units can be reduced to modeling the potential 

distribution area of fictive or real species bound to the 

phytogeographical unit. The inputs of the model are the 

current distribution of the plant species, the climate date 

for the reference period, and the climate data for the 

future period(s). There are three main steps: 1) querying 

the climate demands/tolerance of the species; 2) 

validating the model (modeling the reference period); 3) 

predicting (modeling for the future period). The climate 

requirements of the species can be filtered based on the 

distribution and the climate data of the reference period, 

since the extremes of a certain climate parameter 

indicate the tolerance boundaries of the species. The 

selection of climate parameters, however, is subjective. 

Note that the model can fail if not enough or too much 

 

Fig. 1 Observed distribution, modeled potential distribution for the reference period, and predicted potential distribution for the 

future periods of cork oak (Quercus suber) 



 Modeling the impacts of climate change on phytogeographical units. A case study of the Moesz line 23 
 
climate parameters are selected (Bede-Fazekas, 2012a). 

The result of this phase is a list of climatic limits (a zero-

order logical formula in mathematical terms) per species, 

therefore the climate requirements of the species are 

written as equations. This is the mathematical basis of 

the prediction that used in the next phase. 

Based on the knowledge of the climate 

requirements, the territories providing suitable climatic 

environment for the plant can be filtered according to the 

climate data of the reference period. The sum of these 

territories is the potential distribution area. Modeling the 

potential distribution for the reference period is 

seemingly unnecessary and negligible, and it does not 

influence the result. This medial phase of modeling is, 

however, not to be omitted, since the result of this phase 

provides for the possibility of validating. The reliability 

of the future predictions (model results) can be 

concluded by comparing the observed distribution to the 

modeled potential distribution. In case of much greater 

area of distribution the model results are not to be 

reckoned as reliable results, irrespective from the known 

influence of anthropogenic, edaphic and competitive 

effects on the real distribution. Therefore, the similarity 

of the observed and modeled distribution can guarantee 

that the model is reliable enough. 

Based on the climate demands of a certain plant 

species and the climate data, the suitable territories can 

be filtered not just for the reference period but also for 

the future periods. This third phase is the 

modeling/prediction approach in the strictest sense; this 

is about finding the future potential distribution (Fig. 1). 

The method of modeling the future shift of Moesz 

line (also called as grape line) is going to be reviewed, 

which is appropriate example of modeling a 

phytogeographical unit based on modeling the 

distribution of separate species. Moesz (1911) observed 

that the northern borders of 12 plant species are highly 

correlated with each other, and this line is also the 

northern border of the vine cultivation area. This 

phytogeographical line, which is situated near the 

southern foot of the Western Carpathians, was later 

named after Moesz. There is hardly any international 

literature about the Moesz line, since it is of local 

importance. Note, that elongation of the grape line to the 

west and to the east results in an extended 

phytogeographical line which still correlate with the 

northern border of some species originally bound to the 

Moesz line (eg. Muscari botryoides – Somlyay, 2003). 

The extended line characterizes not only the flora and 

ornamental plant assortment of the Carpathian Basin, 

therefore modeling the Moesz line can have importance 

for entire continent. 

There are several approaches of modeling a 

phytogeographical line. Three different methods (called 

line modeling, distribution modeling, and isotherm 

modeling) are going to be discussed. The models were 

run by the Spatial Analyst module of the GIS software 

ESRI ArcGIS. All of them were based on the regional 

climate model REMO, which has a grid resolution of 25 

km. Although the entire European Continent is within 

the domain of REMO, we used only a part (25724 of the 

32300 points; Fig. 2) of the grid. The reference period 

was 1961-1990, while the future predictions were 

applied for the periods 2011–2040 and 2041–2070 based 

on the IPCC SPES scenario called A1B. 

Isotherm modeling among the three methods is the 

easiest to apply. It is based on finding that winter mini-

mum temperature isotherm that correlates with the 

phytogeographical line most of all. The predicted shift of 

the isotherm probably indicates the shift of the 

 
Fig. 2 The domain of regional climate model REMO (grid) and its part used in the study (within the rectangle) 



24 Bede-Fazekas (2013)  

 
phytogeographical line. The main disadvantage of this 

method is that the existence of this isotherm cannot be 

guaranteed in case of all phytogeographical borders (in 

case of the Moesz line the appropriate isotherm was 

found). Only one or a few climatic parameters are 

considered by this method, thus it is a rather inaccurate 

and not so reliable method. Moreover it can yield to a 

result that is hard to interpret (similar to the case of 

isotherm modeling of Moesz line). Nevertheless, it is a 

very fast method and does not require digitizing 

distribution areas. Line modeling is a somewhat 

complicated method. It is based on modeling the shift of 

the distribution area of a fictive species, whose northern 

distribution borders coincide with the phytogeographical 

line (the southern border is irrelevant). It is a slow but 

somewhat more accurate method. The most complicated 

method is called Distribution modeling, which is also the 

slowest one. The model is run on the distribution of 

numerous plant species bound to the phytogeographical 

line separately. Then the northern borders of the 

predicted potential distributions are merged. The method 

provides detailed result, but drawing the final line (the 

prediction) is still subjective. Detailed comparison of the 

three aforementioned modeling methods is published by 

Bede-Fazekas (2012b). Distribution modeling is, in 

methodical terms, similar to multiple Line modeling.  

Line modeling is a kind of Climate Envelope 

Modeling (CEM) which is about predicting responses of 

species to climate change by drawing an envelope 

around the domain of climatic variables where the given 

species has been recently found and then identifying 

areas predicted to fall within that domain in the future 

(Ibáñez et al., 2006, Hijmans and Graham, 2006). It 

assumes that (present and future) distributions are 

dependent basically on the climatic variables (Czúcz, 

2010) which is somewhat dubious (Skov and Svenning, 

2004). 

36 climatic variables were used for the modeling: 

monthly mean temperature (°C), monthly minimum 

temperature (°C), and monthly summarized precipitation 

(mm). All the climatic data were averaged in the periods 

of thirty years. 

RESULTS OF LINE MODELING 

The results of Line modeling is shown in Fig. 3. The 

method was visually validated (by the correlation of the 

Moesz line and the modeled line for the reference 

period). Some measurements are also known for model 

evaluating, eg. Cohen’s kappa (Cohen, 1960) and 

ROC/AUC (Hanley and Mcneil, 1982), they are, 

however, based on measuring areas instead of 

coincidence of curves. The observed precision is good 

enough despite of the relatively low horizontal resolution 

of the climate data. The modeled distribution of the 

 

Fig. 3 Observed distribution, modeled potential distribution for the reference period, and predicted potential distribution for the future 

periods of the fictive plant species bound to the Moesz line in case of the Line modeling method 



 Modeling the impacts of climate change on phytogeographical units. A case study of the Moesz line 25 
 
fictive species shows a northern border from Southern 

France, through the southern and eastern foot of the 

Alps, the southern foot of Western Carpathians, the wes-

tern foot of the Eastern Carpathians, the southern and 

eastern foot of the Southern Carpathians, to Southern 

Ukraine. The prediction for the period 2011–2040 shows 

not such a great shift in the Carpathians as it was 

expected. Remarkable shift can be seen in France and to 

the east of the Eastern Carpathians. For the far future 

period (2041–70) the model provides results that 

correspond with our preliminary expectations. The 

predicted line displays in three segments separately: 1) 

the Moesz line may shift upwards (and northwards) to 

the Carpathians; 2) in Poland, a new Moesz line may 

appear, which indicates the northern border of the 

distribution of species that can be established in Poland; 

3) and a southern border of the Polish territories of 

optimal climate (so called ‘anti-Moesz-line’) may appear 

in the northern side of the Carpathians. Besides the 

expansion in France, discrete territories in England, 

Belgium, Germany and Bohemia are also predicted for 

the far future period. Fig. 3. also points out that the 

Carpathians (and subordinately the Alps) will obstruct 

(as phytogeographical barrier) the expansion of the plant 

species bound to the Moesz line. 

DISCUSSION OF THE IMPROVEMENT OF 

MODEL WITH ARTIFICIAL 

INTELLIGENCE ALGORITHMS 

As the model results show, two of the three 

aforementioned modeling methods provide maps good 

enough (in terms of comparison of the observed and 

modeled distributions in the reference period) to display 

the impact of the climate change. Since only a few 

climatic parameters were applied, the accuracy and 

reliability of the model can be improved by using some 

other climatic parameters (eg. sum of heat, length of 

vegetation period, length of the period endangered by 

frost) and edaphic parameters (eg. alkalinity, quantity of 

lime). Although, more detailed and accurate inputs (eg. 

distribution maps, climate grid) could strengthen the 

model, it should also be noted, that the real improvement 

of the model can be reached only by the development of 

the modeling method. 

The cumulative distribution function should be 

calculated by statistical software to leave some 

percentiles from the minimum and maximum values of a 

certain climatic parameter. Hence, only the climatic 

values that are bound exactly to the studied distribution 

area will be considered, since climatic extremes are 

mainly found near the distribution border will be left. 

Further improvement of the abovementioned Line 

model can be applied by using statistical or artificial 

intelligence (AI) methods to select the appropriate 

parameters from the infinite combination of the 

numerous climate parameters objectively. Various ways 

can be used to determine the climate envelope, including 

simple regression, distance-based methods, genetic 

algorithms for rule-set prediction, and neural nets 

(Ibáñez et al., 2006). To reduce subjectivity of 

parameters’ choice, logistic regression can be applied, 

which specifies the linear combination of climate 

parameters that determines the likelihood of distribution. 

Another appropriate statistic method is cluster analysis, 

which explains the vector of climate parameters as points 

of a multidimensional space, and searches for a lower 

dimension which separates the distribution apart its 

surroundings. Other clustering methods can be used, too. 

In comparison to statistical methods, applying 

artificial intelligence algorithms may results in much more 

improvement of the model. Note, that some of them are 

black box methods, which can only answer the question 

what?/where?/when?, but not the question why?/how?. 

Several artificial intelligence methods can be used for 

modeling the distribution of plant species or 

phytogeographical units, such as decision tree, 

evolutionary algorithm, and artificial neural net (ANN). 

Hilbert and Ostendorf (2001) studied different forest types 

with ANN, and the research of Carpenter et al. (1999), 

Özesmi and Özesmi (1999), Hilbert and Van Den 

Muyzenberg (1999), Özesmi et al.(2006), Harrison et al. 

(2010), and Ogawa-Onishi et al. (2010) should be 

mentioned, since they modeled the distribution of species 

or communities with ANN. Evolutionary algorithm 

(which matches the climatic parameters with alleles and 

provides a process similar to natural selection with finite 

length) could conclude which parameters (and which 

extrema of them) are able to express the climate tolerance 

most of all. The result is therefore similar to the equations 

used in this research. This does not hold for ANN, since a 

complicated neural net cannot be reduced to linear 

mathematical expressions. ANN is similar to a real neural 

net densely furnished with axons, where the neurons are 

organized in layers. The algorithm has two main phases. 

Learning phase is the first, when the program builds up 

and balances the internal structure of the net in such a 

way, that it is adjusted to the distribution of the plant. 

After the learning phase the model could determine the 

likelihood of presence at all the points of Europe (for the 

reference period and the future periods, too). 

In contrary to ANN, the aforementioned statistical 

and AI methods are not able to result in a map showing 

the potential distribution area (which is still the aim of 

modeling). On the other hand ANN is the only method 

among them which is not able to separate the filtering of 

climate demands of species apart from the prediction. 

The essence of the learning phase is that based on the 

distribution and climate data the program forms a multi-

layered structure and it calculates the so called weights 

of every axon, iteratively. In the course of the time-

consuming, but finite learning phase the weights are 

continuously changed based on remodeling and error 

evaluation. 

A well parameterized ANN with appropriate 

topology could model the future potential distribution 

area in a much more reliable way. The feedforward 

neural network (with multilayer perceptron model) 

seems to be the most suitable for distribution modeling. 

An ANN with Backporpagation training method is now 

under development in Python programming language for 



26 Bede-Fazekas (2013)  

 
ArcGIS software. Input layer is connected to the climatic 

parameters (the number of neurons is determined by the 

number of parameters). The output layer has only one 

neuron which is able to predict a presence/absence data 

(1/0) or the likelihood of the absence (%). The training 

set is the part of the prediction set; the latter is the grid of 

the climate model (with more than 20,000 points). 

CONCLUSION 

The modeling approaches of the distribution of plant 

species and phytogeographical units were studied and 

the conspicuous deficiencies of them were discussed. 

Note, that in absence of AI supported modeling method, 

the used three simple models could provide spectacular 

results. Modeling the Moesz line yielded remarkable 

results, which are not perfectly the same as it was 

expected. It can be concluded that the Northern 

Carpathians will provide significant barrier for the plant 

species bound to the Moesz line. In harmony to the 

studies of Kovács-Láng et al. (2008) – who stated that 

the speed of ecological processes is not synchronous 

with the speed of the climate change and therefore the 

mountains with latitudinal direction may become natural 

barriers – we should note that without human aid some 

of these species will not be able to get as far as Poland. 

Hence there is a risk that the predicted shift of the Moesz 

line may be a prediction of the shift of only a virtual line. 

Acknowledgement 

The author would like to express his gratitude to the two 

anonymous peer-reviewers for their advices. Special 

thanks to Levente Horváth (Corvinus University of Bu-

dapest), Levente Hufnagel (Corvinus University of Bu-

dapest), Anna Czinkóczky (Corvinus University of Bu-

dapest), Villő Csiszár (Eötvös Loránd University), and 

Tibor Gregorics (Eötvös Loránd University) for their 

selfless assistance. The research was supported by the 

project TÁMOP-4.2.1/B-09/1/KMR-2010-0005. The 

ENSEMBLES data used in this work was funded by the 

EU FP6 Integrated Project ENSEMBLES (Contract 

number 505539) whose support is gratefully 

acknowledged. 

References 

Arundel, S.T. 2005. Using spatial models to establish climatic 

limiters of plant species’ distributions. Ecological Mo-

delling 182 (2), 159–181. 

Bartholy, J., Pongrácz, R. 2008. Regionális éghajlatváltozás 

elemzése a Kárpát-medence térségére (Analysis of 

regional climate change in the region of the Carpathian 

Basin). In: Harnos, Zs., Csete, L. Klímaváltozás: környe-

zet – kockázat – társadalom. Szaktudás Kiadó Ház, Bu-

dapest (in Hungarian) 

Bartholy, J., Pongrácz, R., Gelybó, Gy. 2007. A 21. század 

végén várható éghajlatváltozás Magyarországon (The 

predicted climate change in the end of 21
st
 century in 

Hungary). Földrajzi Értesítő 56 (3-4), 147–168. (in 

Hungarian) 

Bede-Fazekas, Á. 2011. Impression of the global climate 

change on the ornamental plant usage in Hungary. Acta 

Universitatis Sapientiae, Agriculture and Environment 3 

(1), 211–220. 

Bede-Fazekas, Á. 2012a. Melegigényes díszfák telepíthetőségi 

területének előrejelzése a 21. századra (Prediction of the 

area of introduction of ornamental trees for the 21
st
 

century). Thesis. Corvinus University of Budapest, Bu-

dapest (in Hungarian) 

Bede-Fazekas, Á. 2012b. Methods of modeling the future shift 

of the so called Moesz-line. Applied Ecology and 

Environmental Research 10 (2), 141–156. 

Berry, P.M., Dawson, T.P., Harrison, P.A., Pearson, R.G. 2002. 

Modelling potential impacts of climate change on the 

bioclimatic envelope of species in Britain and Ireland. 

Global Ecology and Biogeography 11 (6), 453–462. 

Carpenter, G.A., Gopal, S., Macomber, S., Martens, S., 

Woodcock, C.E., Franklin, J. 1999. A Neural Network 

Method for Efficient Vegetation Mapping. Remote 

Sensing of Environment 70 (3), 326–338. 

Cohen, J. 1960. A coefficient of agreement for nominal scales. 

Educational and Psychological Measurement 20 (1), 37–

46. 

Czinkóczky, A., Bede-Fazekas, Á. 2012. Visualization of the 

climate change with the shift of the so called Moesz-line. 

In: Buhmann, E., Ervin, S., Pietsch, M. Peer Reviewed 

Proceedings of Digital Landscape Architecture 2012 at 

Anhalt University of Applied Sciences. Herbert 

Wichmann Verlag, Berlin 

Czúcz, B. 2010. Az éghajlatváltozás hazai természetközeli 

élőhelyekre gyakorolt hatásainak modellezése (Modell-

ing the impact of climate change on natural habitats in 

Hungary). Dissertation, Corvinus University of Budapest, 

Budapest (in Hungarian) 

Czúcz, B., Gálhidy, L., Mátyás, Cs. 2011. Present and 

forecasted xeric climatic limits of beech and sessile oak 

distribution at low altitudes in Central Europe. Annals of 

Forest Science 68 (1), 99–108. 

Führer, E. 2008. Erdőgazdaság (Forestry). In: Harnos, Zs., 

Gaál, M., Hufnagel, L. Klímaváltozásról mindenkinek. 

Budapesti Corvinus Egyetem, Kertészettudományi Kar, 

Matematikai és Informatikai Tanszék, Budapest (in 

Hungarian) 

Führer, E., Rasztovits, E., Csóka, Gy., Lakatos, F., Bordács, S., 

Nagy, L., Mátyás, Cs. 2010. Current status of European 

beech (Fagus sylvatica L.) genetic resources in Hungary. 

Communicationes Instituti Forestalis Bohemicae 25 (1), 

152–163. 

Guisan, A., Zimmermann, N.E. 2000. Predictive habitat 

distribution models in ecology. Ecological Modelling 135 

(2-3), 147–186. 

Hanley, J.A., McNeil, B.J. 1982. The meaning and use of area 

under a receiver operating characteristics (ROC) curve. 

Radiology 143 (1), 29–36. 

Harrison, S., Damschen, E.I., Grace, J.B. 2010. Ecological 

contingency in the effects of climatic warming on forest 

herb communities. Proc Natl Acad Sci USA 107 (45), 

19362-19367. 

Hijmans, R.J., Graham, C.H. 2006. The ability of climate 

envelope models to predict the effect of climate change 

on species distributions. Global Change Biology 12, 

2272-2281. 

Hilbert, D.W., Ostendorf, B. 2001. The utility of artificial 

neural networks for modelling the distribution of 

vegetation in past, present and future climates. 

Ecological Modelling 146 (1-3), 311–327. 

Hilbert, D.W., Van Den Muyzenberg, J. 1999. Using an 

artificial neural network to characterize the relative 

suitability of environments for forest types in a complex 



 Modeling the impacts of climate change on phytogeographical units. A case study of the Moesz line 27 
 

tropical vegetation mosaic. Diversity and Distributions 5 

(6), 263–274. 

Horváth, L. 2008a. A földrajzi analógia alkalmazása klíma-

szcenáriók vizsgálatában (Use of spatial analogy for 

studying climate scenarios). In: Harnos, Zs., Csete, L. 

Klímaváltozás: környezet – kockázat – társadalom. Szak-

tudás Kiadó Ház, Budapest (in Hungarian) 

Horváth, L. 2008b. Földrajzi analógia alkalmazása klímaszce-

náriók elemzésében és értékelésében (Use of spatial 

analogy in analysis and valuation of climate scenarios). 

Dissertation, Corvinus University of Budapest, Budapest 

(in Hungarian) 

Ibáñez, I., Clark, J.S., Dietze, M.C., Feeley, K., Hersh, M., 

Ladeau, S., Mcbride, A., Welch, N.E., Wolosin, M.S. 

2006. Predicting Biodiversity Change: Outside the 

Climate Envelope, beyond the Species-Area Curve. 

Ecology 87 (8), 1896–1906. 

Iverson, L.R., Prasad, A.M., Matthews, S.N., Peters, M. 2008. 

Estimating potential habitat for 134 eastern US tree spe-

cies under six climate scenarios. Forest Ecology and Ma-

nagement 254 (3), 390–406. 

Kovács-Láng, E., Kröel-Dulay, Gy., Czúcz, B. 2008. The 

impacts of climate change on the wildlife and our 

protecting and researching tasks [Az éghajlatváltozás ha-

tásai a természetes élővilágra és teendőink a megőrzés és 

kutatás területén]. Természetvédelmi Közlemények 14 (1), 

5–39. (in Hungarian) 

Mátyás, Cs., Berki, I., Czúcz, B., Gálos, B., Móricz, N., 

Rasztovits, E. 2010. Future of beech in Southeast Europe 

from the perspective of evolutionary ecology. Acta Silv. 

Lign. Hung. 6 (1), 91–110. 

Moesz, G. 1911. Adatok Bars vármegye flórájához 

(Supplementary data of Bars county). Botanikai Közle-

mények 10 (5-6), 171–185. (in Hungarian) 

Ogawa-Onishi, Y., Berry, P.M., Tanaka, N. 2010. Assessing 

the potential impacts of climate change and their 

conservation implications in Japan: A case study of 

conifers. Biological Conservation 143 (7), 1728–1736. 

Özesmi, S.L., Özesmi, U. 1999. An artificial neural network 

approach to spatial habitat modelling with interspecific 

interaction. Ecological Modelling 116 (1), 15–31. 

Özesmi, S.L., Tan, C.O., Özesmi, U. 2006, Methodological 

issues in building, training, and testing artificial neural 

networks in ecological applications. Ecological Modell-

ing 195 (1-2), 83–93. 

Schmidt, G. 2006. Klíma- és időjárás-változás és a fás szárú 

dísznövények (Climate and weather change and the 

ornamental ligneous plants)] In: Csete, L., Nyéki, J. Klí-

maváltozás és a magyarországi kertgazdaság. „AGRO-

21” Kutatási Programiroda, Budapest (in Hungarian) 

Skov, F., Svenning, J.C. 2004. Potential impact of climatic 

change on the distribution of forest herbs in Europe. 

Ecography 27 (3), 366–380. 

Somlyay, L. 2003. A Muscari botryoides (L.) Mill. hazai alak-

körének rendszertani-chorológiai vizsgálata (Taxonomic 

and chorological study of the Muscari botryoides (L.) 

Mill. complex in Hungary). Dissertation, Corvinus Uni-

versity of Debrecen, Debrecen (in Hungarian) 

Stankowski, P.A., Parker, W.H. 2010. Species distribution 

modelling: Does one size fit all? A phytogeographic 

analysis of Salix in Ontario. Ecological Modelling 221 

(13-14), 1655–1664. 

Szabó, K., Bede-Fazekas, Á. 2012. A forgalomban lévő 

fásszárú dísznövénytaxonok szárazságtűrésének értékelé-

se a klímaváltozás tükrében (Evaluation of the impact of 

climate change on current drought-tolerant woody 

plants). Kertgazdaság 44 (4), 62–73. (in Hungarian) 

Van Leeuwen, B., Tobak, Z. 2008. GIS Solutions for Belvíz 

monitoring: A case study in Csongrád county, Hungary. 

XII. Symposium on Geomathematics, I. Croatian-

Hungarian Geomathematical Conference, Mórahalom