Anali PAZU - Letnik 1, leto 2011, številka 1
Vzorci razširjenosti invazivne vrste Robinia pseudacacia v
severovzhodni Sloveniji
Distribution patterns of the invasive species Robinia pseudacacia
in NE Slovenia
Daniela Ribeiro1, Andraž Čarni1,2 in Imelda Somodi3
1
Laboratorij za raziskave v okolju, Univerza v Novi Gorici / Vipavska 13, 5000 Nova Gorica, Slovenija
Biološki inštitut, Znanstvenoraziskovalni center Slovenske akademije znanosti in umetnosti / Novi trg 2, 1000 Ljubljana,
Slovenija
3
Inštitut za ekologijo in botaniko Madžarske akademije znanosti in umetnosti / 2-4. Alkotmány u., 2163 Vácratot, Madžarska
2
Povzetek: V prispevku se ukvarjamo z vrsto Robinia pseudacacia L., ki se je v Evropi pojavila na začetku 17.
stoletja in jo sedaj tudi v Sloveniji štejemo med invazivne vrste. Raziskava je potekala na območju
severovzhodne Slovenije, v Prekmurju. Njen namen je bil poiskati razloge za trenutni vzorec razširjenosti vrste
R. pseudacacia na tem področju. Najprej smo v letu 2009 na terenu skartirali vzorčno območje 4 x 3 km v
merilu 1:5.000, kjer se vrsta pojavlja na Ravenskem (nižinski predel Prekmurja). Nato smo analizirali različne
dejavnike, ki bi lahko vplivali na razširjenost vrste v regiji: oddaljenost od cestnega omrežja in vodnih teles,
nadmorsko višino, rabo zemljišč, vrsto in kakovost zemljišč. Izvedli smo prostorsko vzorčenje na naključno
izbranih 1800 točkah na tem območju. Na teh točkah smo ugotovili podatke o prisotnosti R. pseudacacia in
izračunali potencialne dejavnike, ki bi lahko vplivali na njeno prisotnost. Statistične odnose smo nato določili s
splošnim linearnim modelom (GLM). Ugotovili smo, da se R. pseudacacia največkrat pojavlja na travnikih in
pašnikih. Določen vpliv na njeno pojavljanje ima oddaljenost od cestnega omrežja, ki do neke mere ugodno
vpliva na pojavljanje vrste, medtem ko bližina vodnih teles zmanjšuje verjetnost njenega pojavljanja. Med
nadmorsko višino in prisotnostjo vrste nismo našli povezave, saj očitno ta dejavnik ne vpliva na razširjenost
vrste na raziskovanem območju.
R. pseudacacia se širi naravno, hkrati pa jo zasajajo tudi kmetje. Naši rezultati kažejo, da tudi človekove
odločitve vplivajo na njeno širitev.
Ključne besede: GLM; invazivna vrsta; Robinia pseudacacia; prostorska razširjenost
Abstract: Robinia pseudacacia L. was introduced into Europe at the beginning of the 17th century and is now
considered to be an invasive species also in Slovenia. Our study area was located in northeastern Slovenia,
within the Prekmurje region. The aim of our study was to find explanations for the current occurrence pattern
of the species in that location. Areas dominated by R. pseudacacia have been mapped in a scale of 1:5.000 in
the lowland area of Prekmuje, across a sample plot of 4 by 3km in 2009. We analyzed potential factors that can
influence the distribution of the species within the region: distance to the road network, distance to water
bodies, elevation, land use, soil type and soil quality. We performed a spatial randomized sampling technique
stratified for prevalence on the resulting maps in order to collect observations on the relationship between R.
pseudacacia presence and the potential influenting factors. The statistical relationships were then established
by a generalized linear model (GLM).
R. pseudacacia was found to occur mostly in parcels designated as meadows and pastures. Distance from the
road network seems to facilitate the occurrence of the species to a certain degree. Distance from water bodies
seems to decrease R. pseudacacia presence. We did not find a relative relationship between elevation and
species presence, this factor apparently does not influence the distribution of the species in this region.
R. pseudacacia expands naturally but it is also being planted by farmers, therefore, its expansion is directed as
well. Our results also show that human decisions affect the species expansion.
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Anali PAZU - Letnik 1, leto 2011, številka 1
Key words: GLM; invasive species; Robinia pseudacacia; spatial distribution
areas in order to find similar locations (Goslee et al.
2006). The prediction of the distribution patterns for
invasive species in locations outside their native range is
fundamental for early warning systems (Zhu et al. 2007).
The aims of this study were to develop a model to
identify the critical influencing factors for the current
occurrence pattern of the species in the study site and to
predict the spatial distribution of R. pseudacacia for
invaded areas. The regional distribution pattern of
Robinia pseudacacia was studied in NE Slovenia, within
the Prekmurje region. It is important to focus on
understanding the distribution and quantitative prediction
of the invasion processes in order to develop preventive
policies.
Introduction
The migration of species has always existed, however
the rate of human-assisted introductions of new species is
currently significantly higher than previously.
Globalization has greatly increased the speed in the
spread of harmful species through travel and tourism, the
agricultural, horticultural and pet industries. Due to
socioeconomic and environmental effects worldwide
biological invasions have been increasingly recognized
as a major problem. Therefore, efforts have been made to
map and predict the spatial distribution of invasive
species.
Many authors have frequently used terms such as
“alien”, “naturalized” and “invasive” in an imprecise and
erroneous way. This has resulted in confusion within
English literature on plant invasions. Richardson et al.
(2000), have made a literature review and defined a set
of key terms which conceptualizes the naturalization/
invasion process of plant species. In this paper we have
adopted their term for “invasive” plants, as naturalized
alien plants that produce reproductive offspring, often in
large numbers, at considerable distances from parent
plants and have a potential to spread over a considerable
area.
The species chosen for this study was Robinia
pseudacacia L. (Black locust). R. pseudacacia is a
deciduous tree that belongs to the Fabaceae family. The
species produce white flowers and smooth legumes that
make it easier to distinguish this species from other
species in the Robinia genus. In forests stands it can
grow up to 30-35 m in height, in juvenile trees the bark is
smooth with suberous lenticels, while in older trees the
bark is very thick and grey-brown, yellowish in the
cracks. R. pseudacacia, is native to North America, was
introduced into Europe at the beginning of the 17th
century and is now being considered to be an invasive
species also in Slovenia (Bartha et al. 2008).
Although R. pseudacacia is considered as invasive
(Morimoto et al. 2009; Kleinbauer et al. 2010), it is still
being planted in many European countries due to its
economic importance, such as timber (Huntley 1990;
Rudolf & Brus 2006), honey production (Huntley 1990;
Rudolf & Brus 2006), soil erosion control (Torelli 2002),
ornamental use (Huntley 1990; Rudolf & Brus 2006),
and homeotherapy uses due to essences derived from its
flowers (Bartha et al. 2008).
The most common approach to identify potential
invaded areas is to extrapolate from already invaded
Methodology
Study area
This research was conducted in the Northeast of
Slovenia, in the Prekmurje region. This region lies at low
altitudes (from 150 to 400 m), is open towards the
Pannonian plain and has the most continental climatic
features in Slovenia (Ogrin 2009). This region is
considered one of the most important agricultural areas
in Slovenia (Gabrovec & Kladnik 1997), being half of
the territory occupied by agricultural land, the rest of the
territory is occupied by forests, vineyards and orchards
(Petek 2009). The study region can be divided into three
geographical areas; the hilly area of Goričko, the
floodplains of the Mura River, known as Ravensko, and
the lowlands known as Dolinsko. In the lowland part of
this region a sample plot of 12km2 was chosen for this
research.
Field survey
The data was collected through qualitative methods
such as mapping of the invaded areas. A map was
generated by manually drawing polygons around the
areas where the species was known to occur and also in
areas where the species was not present. Orthophotographs of the study area were used as a backdrop.
The polygons were then digitized in ArcGIS software.
A total dataset of 310 R. pseudacacia polygons were thus
mapped in the field.
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Anali PAZU - Letnik 1, leto 2011, številka 1
variable was represented by the presence/absence of the
species and environmental and socioeconomic factors
were used as explanatory variables. This model enabled
us to model species response to landscape variables using
a logit link function between the response and predictor
variables. Non-linear effects of the explanatory variables
were tested by means of second-order polynomials
(Dullinger et al. 2009). The Chi2 test was performed to
identify which of the explanatory variables had a
significant contribution to the model.
The final model was then used to assess the
occurrence probability of the species at the study site. A
prediction map was prepared in ArcGIS 9.3, based on the
best predictor variables. The “area under the receiver
operating characteristic (ROC) curve” (AUC) was used
to measure the accuracy of the model (Somodi et al.
2010). Results from the spatial prediction were compared
to the known R. pseudacacia locations.
Sampling
A spatially random sample of three times the number
of recorded polygons with presence of the study species
were used to establish statistical relationships. The same
number of points were settled for the areas where the
species was absent. The sampling was generated in
ArcGIS 9.3, and was used to model the species
distribution. In order to predict the species distribution
within the study area a 6 m sized mesh was produced.
Potential factors
Spatial data such as distance to the road network,
distance to water bodies, elevation, soil type, soil quality
and land use (orchard, meadow, forest, field, urban,
pasture) were used for analysing the R. pseudacacia
distribution.
Distances to roads and water bodies were calculated
from the road network and water bodies’ data layers
from the Surveying and Mapping Authority of the
Republic of Slovenia. Euclidean distances were
calculated from each point to the nearest road and water
body. Elevation values were masked from the Digital
Elevation Model (DEM) to the study site. DEM with
12.5 m x 12.5 m resolution, acquired from the Surveying
and Mapping Authority of the Republic of Slovenia was
used.
Land use types were taken from the Land Cadastre
Map from the Surveying and Mapping Authority of the
Republic of Slovenia. Due to the rarity of some land use
types within the study area we grouped the land use types
in six land categories, “field”, “orchard” (“vineyards”
were included into this category), “pasture”, “meadow”,
“forest” and “urban” (this category included all the
building areas and areas that are occupied by human
activities, e.g. playground, courtyard...).
From the same dataset information about the soil
quality was taken, based on taxation of real estate. The
soil type information was extracted from the Slovenian
Pedological Map from the Ministry of Agriculture,
Forestry and Food. All the data layers were projected in
the Slovenian Coordinate System (D48_Slovenia_TM).
Figure 1: Overview of study area.
Results and discussion
The model was run with all six variables (elevation,
distance to the road network, distance to water bodies,
soil type, soil quality and land use); however elevation
was dropped in the course of model selection. Details of
the best model can be seen in the table 1. The AUC value
obtained was 0.8912, this indicated spatial agreement
between the model prediction and actual R. pseudacacia
sites from the training data.
Meadows and pastures are significantly more likely
to be invaded by R. pseudacacia than other land use
types (fields, forests, orchards and urban). We suggest
that areas with highest agricultural potential as fields are
still maintained for agricultural purposes so they do not
contribute to R. pseudacacia occurrence.
Dystric Fluvisols are significantly less susceptible to
R. pseudacacia than Dystric Cambisol and they also
differ in this manner from Urban Soil.
Model creation and accuracy assessment
Statistical methods were used to relate the
distribution of the study species with the environmental
and socioeconomic predictors. A generalized linear
model (GLM), an advanced and extended method
stemming from linear regression, was fitted using the R
statistical environment (R development team 2008) to
explain the R. pseudacacia distribution. The response
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Anali PAZU - Letnik 1, leto 2011, številka 1
Close to the roads there is a higher probability of
species occurrence and this probability decreases when
the distance to the road network ranges between 100-300
meters. When the distance from the road network is
higher than 300m the probability of the species
occurrence increases. The reason for this is that they
planted R. pseudacacia near to the roads, on strips along
road banks and the most propitious surfaces used for
agriculture are near to the roads. Here R. pseudacacia
does not invade or is not being planted. It appears on less
fertile soils, where the road network is not so dense. The
distance to water bodies has a negative influence on the
species occurrence in lowland Prekmurje, in contrast to
what was showed by other studies (Bartha et al. 2008;
Akamatsu 2008).
As it can be seen in Figure 2 that the predictions
resulting from the predictive model correlate well to the
field observations, most observations fall into the
predicted category with a higher probability than 0.68.
Some field observations which were outside this
probability were included in the next category (0.360.68).
Table 1: GLM results showing the importance of the variables in R. pseudacacia presence Asterisks
refer to significance level: '***' - p< 0.001, '**' - p<0.01, '*' - p< 0.05, '.' - p<0.1.
Variable
Estimate value
Pr(>|z|)
(Intercept)
-1.715e+02
0.62536
Land Use: Forest
1.873e+01
0.99681
Land Use: Meadow
2.295e+00
1.01e-08
Land Use: Orchard
9.872e-02
0.94301
Land Use: Pasture
4.934e+00
1.70e-07
***
Land Use: Urban
2.133e+00
0.05461
.
Dist_water
6.151e-05
0.94773
Dist_road
-5.100e-03
0.02569
*
Soil type: Dystric Fluvisol
-1.548e+00
0.00148
**
Soil type: Urban Soil
-1.782e+01
0.98327
1.295e-05
0.09009
.
-2.323e-06
0.04251
*
2
I(Dist_road )
2
I(Dist_water )
Figure 2: Comparison between result of predictive model and field map.
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Sig. Level
***
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Conclusion
Our results indicate that land use plays an important
role in the distribution of the species across the study
area. This approach permits us to analyze the impact of
different factors in the distribution of R. pseudacacia.
The species is spreading in the study region and we
expect that areas with lower quality for agricultural
production are the most prone to future invasion.
Prediction can thus be useful in order to avert future
invasions and appears as a valuable tool for landscape
management.
7.
8.
9.
Acknowledgements
The fieldwork conducted for this paper was
supported by the TransEcoNet project implemented
through the CENTRAL EUROPE Program and cofinanced by the ERDF. The authors acknowledge
financial support from the state budget through the
Slovenian Research Agency (project No. L1-9737 and
P1-0236). We wish to extend special thanks to Julia Ellis
Burnet for her careful review of the English content.
10.
11.
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