Predicting trends of invasive plants richness using local socio



Predicting trends of invasive plants richness using local socio
Environmental Research ] (]]]]) ]]]–]]]
Contents lists available at ScienceDirect
Environmental Research
journal homepage:
Predicting trends of invasive plants richness using local socio-economic data:
An application in North Portugal$
Mário Santos a,n, Raul Freitas b, António L. Crespı́ b, Samantha Jane Hughes c, Joa~ o Alexandre Cabral a
Laboratory of Applied Ecology, CITAB—Centre for the Research and Technology of Agro-Environment and Biological Sciences,
University of Trás-os-Montes e Alto Douro, 5000-911 Vila Real, Portugal
Herbarium, UTAD Botanical Garden, CITAB—Centre for the Research and Technology of Agro-Environment and Biological Sciences,
University of Trás-os-Montes e Alto Douro, 5000-911 Vila Real, Portugal
Department of Forest and Landscape, CITAB—Centre for the Research and Technology of Agro-Environment and Biological Sciences,
University of Trás-os-Montes e Alto Douro, 5000-911 Vila Real, Portugal
a r t i c l e i n f o
Stochastic dynamic methodology
Invasive plant richness
Geophysical parameters
Socio-economic trends
Disturbance ecology
a b s t r a c t
This study assesses the potential of an integrated methodology for predicting local trends in invasive
exotic plant species (invasive richness) using indirect, regional information on human disturbance. The
distribution of invasive plants was assessed in North Portugal using herbarium collections and local
environmental, geophysical and socio-economic characteristics. Invasive richness response to anthropogenic disturbance was predicted using a dynamic model based on a sequential modeling process
(stochastic dynamic methodology—StDM). Derived scenarios showed that invasive richness trends
were clearly associated with ongoing socio-economic change. Simulations including scenarios of
growing urbanization showed an increase in invasive richness while simulations in municipalities
with decreasing populations showed stable or decreasing levels of invasive richness. The model
simulations demonstrate the interest and feasibility of using this methodology in disturbance ecology.
& 2011 Elsevier Inc. All rights reserved.
1. Introduction
The invasion of habitats by non-native species is a major cause
of ecosystem homogenization and biodiversity loss, with serious
deleterious consequences for economic and social systems
(Mooney, 2005; Hobbs et al., 2006; Keller et al., 2006; Cuneo
et al., 2009). Although the major drivers of invasibility are well
known, studies tend to give inconsistent results on the influence
of the number of propagules entering a ‘‘new’’ environment, the
characteristics of exotic species and the susceptibility of the
environment to invasion (e.g. Davis et al., 2000; Stohlgren et al.,
2002; Meiners et al., 2008). The cross-scale interactions that
characterise invasions present a challenge in understanding
system behavior and effects at scales that differ from those where
information was obtained (Doren et al., 2009a). Some studies
demonstrate that a few selected characteristics appear to determine the success of invasion in disturbed systems and that in
undisturbed areas invasive species are absent or within tolerable
critical limits for their ecological integrity (Alpert et al.,
2000; Lake and Leishman, 2004). As human activities increase,
No funding supported the present manuscript.
Corresponding author. Fax: þ351 259 350 480.
E-mail addresses: [email protected] (M. Santos),
[email protected] (R. Freitas), [email protected] (A.L. Crespı́),
[email protected] (S.J. Hughes), [email protected] (J.A. Cabral).
alteration and degradation of autochthonous ecosystems creates
new opportunities for invasive species coupled with the massive
transport of propagules between regions, resulting in many new
introductions of species in natural, semi-natural and artificial
ecosystems (Pino et al., 2005; Chyron et al., 2009). Although many
colonisations fail for stochastic reasons (Alpert et al., 2000; Taylor
and Irwin, 2004; Chyron et al., 2009), increasing levels of
accidental or premeditated introductions have amplified opportunities for successful colonisations and play a major role in the
growing numbers of successful species (Williamson, 1996;
Arévalo et al., 2005; Leprier et al., 2008).
Species invasions are regarded as enormously complex processes (e.g. Alpert et al., 2000; Vila and Pujadas, 2001; Richardson
et al., 2005; Chytrý et al., 2005; Meiners et al., 2008; Doren et al.,
2009b). A test for using invasive plants as indicators of ecosystem
disturbances is to increase the ability to predict the invasiveness
of species and the invasibility of habitats and landscapes
(Peterson, 2003; Crossman et al., 2011). The most popular tools
for assessing ecosystem disturbance to date are biological indices,
which reduce the dimensionality of complex ecological data sets
to a single univariate statistic or 2 to 3 dimensional ordination
plots (Santos, 2009). None of these methods adequately express
temporal patterns of structural change when habitat conditions
are also substantially changing (Cabral et al., 2007). This lacuna
can be overcome by creating dynamic models that capture
both structural and composition patterns in systems affected by
0013-9351/$ - see front matter & 2011 Elsevier Inc. All rights reserved.
Please cite this article as: Santos, M., et al., Predicting trends of invasive plants richness using local socio-economic data: An
application in North Portugal. Environ. Res. (2011), doi:10.1016/j.envres.2011.03.014
M. Santos et al. / Environmental Research ] (]]]]) ]]]–]]]
long-term environmental disturbances (e.g. Higgings et al., 1999;
Peterson, 2003; Peterson et al., 2003; Thuiller et al., 2006; Doren
et al., 2009a). This approach requires the identification of causal
factors that best explain identified ecological trends (Santos,
2009). Stochastic dynamic methodology (StDM) meets this criterion, providing a mechanistic understanding of integral, interrelated ecological processes, based on a statistical parameter
estimation method (Santos and Cabral, 2004; Cabral et al., 2008)
that combines data-driven and mechanistic modeling techniques
(Aumann, 2007).
Recent research using StDM has been based on the premise that
statistical patterns of ecological phenomena are emergent indicia of
complex ecological processes that reflect universal law-like mechanistic processes (Santos and Cabral, 2004; Cabral et al., 2008; Santos,
2009). The main objective of this study was to assess the effectiveness of a StDM model in predicting the effects of anthropogenic
disturbance on invasive plant richness using local information.
Despite the fact that the richness concept has several limitations
(Fleishman et al., 2006), many authors believe that the sum of all
invasive exotic species present in a particular region (i.e. invasive
species richness) encompasses several useful characteristics for
detecting specific ecological consequences in affected ecosystems
(e.g. Kim 2005; Crossman et al., 2011). We made predictions using
local general socio-economic data of several types of anthropogenic
disturbances, with potential influence on invasive species dynamics
that are otherwise difficult or impossible to estimate.
2. Methods
2.1. Study area
The study was carried out in an area of 2,464,200 ha of Northern Portugal
(Fig. 1) (S limit, 401150 24.4700 N; N limit, 421090 15.1700 N; W limit, 81540 47.6000 W;
and E limit, 61110 23.2100 W). Even though it is a small area, it contains a
representative sample of geophysical and socio-economic gradients (INE, 2009).
The western limit of the area is an industrial region with a high, increasing
population density while the eastern area is a rural region (Ribeiro and Lovett,
2009) with low, decreasing population density. Bio-climatologists classify the
study area as a transition between the Eurosiberian region (west) and the
Mediterranean region (east). Mild climatic conditions, characterized by some
flatlands and many hills predominate in the west while more extreme climatic
conditions, plateaux and deep river valleys predominate in the east, (RivasMartı́nez et al., 1987; Molina et al., 1992; Pinto-Correia, 2000; INE, 2009).
2.2. Database construction
Sixty eight species of invasive exotic plants (common species in North Portugal and
with referenced information) were considered for this study (Appendix A of
Supplementary data), obtained from national lists (Marchante et al., 2008; ICNB,
2009). The following herbaria were inventoried for invasive species distributions: the
University of Aveiro (AVE, Department of Biology), the University of Coimbra (COI,
Botanic Institute ‘‘Júlio Henriques’’), Braganc- a Polytechnic Institute (BRESA), University
of Trás-os-Montes e Alto Douro (HVR), National Agronomic Station (LISE), University of
Lisbon (LISI, Faculty of Science, Superior Institute of Agronomy) and Oporto University
(PO, Faculty of Science, Botanic Institute ‘‘Gonc- alo Sampaio’’). These herbaria represent
the entire holdings for collections in Northern Portugal. Invasive plants species richness
in the study area was drawn up by superimposing species distribution onto the spatial
limit of local municipalities. Time of collection was not considered, although doubtful
records and ancient collections were confirmed by supplementary field work.
The basic territorial unit of each of the 89 municipalities was characterized
using twelve socio-economic and geophysical variables obtained from specific
databases (INE, 2009). With the exception of geophysical variables and total
population, all remaining variables were transformed into densities, allowing
comparison of heterogeneous data from municipalities with different areas/
populations (Table 1).
2.3. Data analysis
2.3.1. Analysis of the database
StDM performance depends largely on the quantity and quality of available
data (parameters result from a previous statistical estimation using specific
Fig. 1. Location of the study area in northern Portugal (shaded).
databases), which determines both the type of statistical analysis adopted for
parameter estimations and the limitations of subsequent applications (Santos,
2009). An essential requirement of StDM is that the database includes representative, relevant gradients of change (Santos and Cabral, 2004; Steele et al., 2005;
Cabral et al., 2008). Sets of continuous variables that depict gradients that can be
applied to scenarios are vital in StDM modeling (Cabral et al., 2008; Santos, 2009).
Although the regional municipalities can be classified as rural or urban based on
the dataset (Ribeiro and Lovett, 2009), they should reveal a much more complex
combination of characteristics along the rural–urban gradient (comprising internal combinations and regional locations). Invasive richness should show significant differences between municipality types (Santos, 2009).
Using geophysical and socio-economic variables (explanatory variables), we
tested between urban and rural municipality types using Discriminate Canonical
Analysis (DCA). Invasive richness, the response variable, was tested with One Way
Analysis of Variance (ANOVA) (Hair et al., 1995; Zar, 1996). The explanatory
variables were considered to be representative of a gradient when (a) spatial
distribution in the ordination plot revealed a fairly disperse distribution and
(b) the two groups were considered dissimilar (F-remove onumber of variables,
and p-levelo 0.01). Response variables should be significantly different for each
category of municipality considered. Analysis of invasive richness distribution was
carried out to avoid significant bias in StDM estimates following subsequent data
transformation (Krebs, 1999; Ver Hoef and Boveng, 2007; O’Hara and Kotze, 2010).
2.3.2. Effects of geophysical and socio-economic variables on the invasive richness
Prior to StDM dynamic model construction, a conventional multivariate
statistical procedure for parameter estimation was carried out. Explanatory
variables were divided, taking into account differences in ‘‘scale’’ between
geophysical and socio-economic variables (geophysical variables were considered
locally time-stationary and the socio-economic were considered time-shifting).
However, not all variables had a significant effect on the magnitude of the
Please cite this article as: Santos, M., et al., Predicting trends of invasive plants richness using local socio-economic data: An
application in North Portugal. Environ. Res. (2011), doi:10.1016/j.envres.2011.03.014
M. Santos et al. / Environmental Research ] (]]]]) ]]]–]]]
Table 1
Description of the key variables considered in this study. The explanatory variables selected by the best models, geophysical (GEO) and socio-economic (POP), according to
the Akaike information criteria (AIC) are listed under the heading ‘‘Environmental variables included in the models’’. The explanatory variables excluded from the models
(based on the AIC) are listed under the heading ‘‘Environmental variables excluded from the models’’. For details read Sections 2.3.2 and 3.2.
Invasive species richness (response variables)
Richness of species predicted by geophysical characteristics (GEO)
Richness of species predicted by socio-economic characteristics
Richness of invasive species
Model codes
Number of species
Number of species
Richness Geo
Richness Pop
Averaged number of species ((Richness Geo þ Richness Pop)/ Estimated invasive
Environmental variables included in the models (explanatory variables)
Maximum altitude (GEO)
Meters above sea level
Minimum altitude (GEO)
Meters above sea level
Maximum longitude by municipality (GEO)
Maximum longitude (W–E) in km
Population by municipality (POP)
Number of inhabitants
Density of population by municipality (POP)
Density of inhabitants by area
Energy consumption by municipality (POP)
Kilowatts by inhabitant
Variation in the number of buildings by municipality (POP)
Difference between the number of constructions by area
in 2001 and 2004
Environmental variables excluded from the models (explanatory variables)
Area (GEO)
Total area by municipality (km2)
Perimeter (GEO)
Perimeter by municipality (km)
Maximum latitude by municipality (GEO)
Maximum latitude (N–S) in Km
Population growth by municipality (POP)
Rate of population growth (percentage/year)
Concluded constructions by municipality (POP)
Concluded constructions by inhabitant
Max altitude
Min altitude
Longitude wide
Total population
Total population density
Building variation
response variable. Therefore, regression models with maximum likelihood were
selected using the Akaike Information Criteria (AIC, Akaike, 1974). The AIC is a
measure of a trade-off between a small residual sum of squares (goodness-of-fit)
and model complexity (number of parameters). The fit of each candidate model
was assessed using the value of AIC corrected for small sample bias (AICc, Hurvich
and Tsai, 1989) and the models, in all possible combinations, were compared using
the Akaike weights (AiCc wi, Anderson et al., 2000). The selected regression
models were analyzed using ANOVA; each variable was assessed for significance.
We then generated 1000 bootstrapped samples from the dataset (resampling the
data with replacement) to check for deviations of the best model regression
coefficients (previously selected by the AIC).
Selected models were considered supported when the deviations (Bias) of the
bootstrap coefficients (Bo Co) from the regression coefficients (Coef) were o 0.1
and the standard errors of the coefficients (Se Co) were similar to the standard
errors of the bootstrap coefficients (Se Bo) (Sokal and Rohlf, 1995). Although the
lack of normality distribution of the dependent variables was not solved by any
transformation (Kolmogorov–Smirnov test), linearity and homoscedasticity of
residuals were achieved by using logarithmic transformations (X¼ log[X þ1]) on
either side of the equation, i.e. on both the dependent and independent variables
(Zar, 1996). All statistical analyses were carried out using Systat statistical
software (version 8.0s, Cranes Software International), STATISTICA (version 9.0,
Statsoft Ltd.) and SAM 4.0s (Rangel et al., 2010).
2.4. Conceptualisation of the dynamic model
Significant partial regression coefficients were assumed to represent relevant
ecological parameters and used to construct the dynamic model. The partial
regression coefficients represent the global influence of selected environmental
variables of significant importance in complex ecological processes. They are not
included explicitly in the model, but are statistically related with the non-native
plant invasion response (Santos and Cabral, 2004). The software STELLA (version
9.0.3s, Isee Systems, Inc.) was used for dynamic model development.
2.5. Invasive richness response to the simulated scenarios
Trends of invasive richness were simulated using realistic scenarios of socioeconomic change in the municipalities (INE, 2007). A possible pathway of socioeconomic evolution was used for expected scenarios by extrapolating current
trends over a 20 year period (INE, 2007). The considered scenarios were (a) a
western, lowland suburban municipality with increasing population and clear
signs of socio-economic development and (b) an eastern highland, rural municipality characterized by an ageing population and emigration, resulting in agricultural and associated socio-economic abandonment.
Fig. 2. Discriminate Canonical Analysis used to assess environmental gradients.
Asterisks represent rural municipalities and black circles represent urban municipalities. Results are given in Appendix B of Supplementary data.
3. Results
3.1. Analysis of the database
The DCA results (Fig. 2 and Appendix B of Supplementary data)
show a separation of geophysical and socio-economic variables
between urban and rural groups of municipalities (F(Tf)¼ 6.06,
p-level ¼0.004). Sites distribution in the ordination space revealed
a fairly disperse gradient (Fig. 2), a requirement for the subsequent StDM application. ANOVA results of the response variables
indicated a significant difference in species richness between the
two categories of municipalities (F1,88 ¼7.14, p ¼0.009).
The response variable distribution closely resembles a negative binomial distribution (S2/x¼ 7.44; U test¼0.0; S.E. (U)¼5.6;
x¼3.9; S.D. ¼5.4; k ¼0.6068, calculated using the maximum likelihood estimator). Comparing this distribution (average¼3.9 and
14k40.5) with O’Hara and Kotze (2010) theoretical results,
Please cite this article as: Santos, M., et al., Predicting trends of invasive plants richness using local socio-economic data: An
application in North Portugal. Environ. Res. (2011), doi:10.1016/j.envres.2011.03.014
M. Santos et al. / Environmental Research ] (]]]]) ]]]–]]]
Table 2
Explanatory variable coefficients (Coef), standard error (SE Co), T-value (T), significance (p), the bootstrap coefficients (Bo Co) and bootstrap coefficients standard error (SE
Bo) for the best models, geophysical (GEO) and socio-economic (POP), selected by the AIC criteria (all possible combinations were tested). Model descriptors:
GEO—degrees of freedom 88, coefficient of determination—0.279, Akaike AICc—70.795, Delta AICc—0, AICc wi—0.162 and the F-value (ANOVA)—10.79 (po 0.001).
POP—degrees of freedom 88, coefficient of determination—0.314, Akaike AICc—68.907, Delta AICc—0, AICc wi—0.119 and the F-value (ANOVA)—9.604 (p o 0.001). Details
of all variables are given in Table 1.
Bo Co
Log longitude wide
Log max altitude
Log min altitude
Log total population density
Log total population
Log building variation
Log energy
mean bias (Bias, approximately 0.2) and root mean-square error
(RMSE, approximately 0.2) using a log(xþ1) transformation are
low and performance is very similar that using the negative
binomial distribution to fit the data. Since the model aims to
produce a response based upon the influence of multi-scale
factors, the log(x þ1) transformation was considered suitable for
including cause–effect relationships between variables and coefficients in system dynamics software.
3.2. Effects of geophysical and socio-economic variables on invasive
A total of six geophysical and six socio-economic variables
(Table 1) were included in the multiple-regression analysis to
detect significant relationships between the two groups of explanatory variables and invasive richness for all municipalities in the
study area. Regressions with the smallest AICc were considered as
models that best fitted the data (Table 2). Although other regressions (all possible combinations were tested) had similar Akaike
weights, equations with the highest AICc wi (parsimonious
models) were considered as accurate for standardizing methodology (Santos and Cabral, 2004) and minimizing subjectivity in
method selection. Assessment of regression model F-values,
individual variable significance and comparison between the
model coefficients and the bootstrapped coefficients were used
to corroborate this approach (Table 2).
3.3. Model construction and equations
The conceptual diagram of the model (Fig. 3) is based on
relationships detected in the multiple regression analyses
(Table 2). The influence of each parameter on partial invasive
richness estimates was set in accordance with its coefficient sign
(Table 2, Fig. 3, Appendix C and D of Supplementary data). The
final simulation for invasive plant richness (Estimated Invasive
Richness) used the arithmetic mean of partial contributions from
geophysical parameter estimated richness (Richness Geo) and
from socio-economic variable estimated richness (Richness Pop)
(Fig. 3, Appendix D of Supplementary data). A probable temporal
scenario was implemented for the socio-economic trends
expected to occur in the study region (each parameter value
and trends were introduced in the model, based on actual
available socio-economic indicators), and that were highly influenced by the regional location of each municipality (INE, 2007,
Appendix D of Supplementary data). Geophysical parameters
were considered to be constant for each municipality (introduced
in the model as constants, Appendix C and D of Supplementary
data). Explanations of full models and source equations are given
in Appendix C, D and E of Supplementary data.
Fig. 3. Conceptual diagram of the model used to predict the response of invasive
plant richness to geophysical and socio-economic factors. Estimated invasive plant
richness is the arithmetic mean of the contributions of the richness predicted
from the geophysical parameters (Richness Geo) and socio-economic variables
(Richness Pop). The signs ( þ) and ( ) indicate positive and negative statistical
influences, respectively. The codes for each variable are listed and explained in
Table 1. The model details are given in Appendix C, Appendix D and Appendix E of
Supplementary data.
3.4. Invasive richness response to the simulated scenarios
We chose the year as an acceptable temporal unit for detecting
change in municipality characteristics over a total simulation
time of 20 years. The model was run taking into account different
paths of socio-economic evolution (see Appendix C, Appendix D
and Appendix E of Supplementary data). The two considered
scenarios (Fig. 4) were based on changes that could occur in two
municipalities in the study area. Geophysical parameters for both
municipalities were considered to be constant during the simulation period (area, 62.24 and 228.62 km2, longitude (wide), 11.2
Please cite this article as: Santos, M., et al., Predicting trends of invasive plants richness using local socio-economic data: An
application in North Portugal. Environ. Res. (2011), doi:10.1016/j.envres.2011.03.014
Socio-economic variables
Socio-economic variables
M. Santos et al. / Environmental Research ] (]]]]) ]]]–]]]
Population density
Building variation
Energy consumption
Population density
Building variation
Energy consumption
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Richness Geo
Richness Pop
Estimated Richness
Invasive species richness
Invasive species richness
Richness Geo
Richness Pop
Estimated Richness
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Fig. 4. Computer simulations over a 20 year period for invasive plant species richness response to different geophysical scenarios with divergent socio-economic trends:
(a, b) a lowland urbanizing municipality (Matosinhos) and (c, d) a highland depopulating municipality (Sernancelhe). Socio-economic scenarios (a, c): (&) energy
consumption per capita; (J) population density; (X) building variation. Invasive plant richness response (b, d): (K) predicted by the geophysical model; (.) predicted by
the socio-economic model; (’) average response.
and 24.1 km, max altitude, 132 and 962 m, min altitude 0 and
475 m, for Matosinhos and Sernancelhe municipalities, respectively). A possible scenario was run for Matosinhos (Fig. 4a),
a western, lowland urbanized municipality based on specific information for this area (Carvalho, 2004) and national reports giving
expected trends (INE, 2007; INE, 2009). Both energy consumption
(5.35–7.85 kWh/inhabitant) and population density (from 2722 to
4820 inhabitants/km2) are expected to increase, while the rate of
building construction is expected to remain constant (7.6% over the
last 3 years). The simulations of invasive richness (Fig. 4b), resulted
in a similar first value of the simulation (data from year 2004, 10
species) for both model predictors, geophysical parameters (Richness Geo) and dynamic socio-economic variables (Richness Pop).
The subsequent increase in the prediction of richness is based solely
on socio-economic variable dynamics (with a clear increase in
invasive richness along the simulation period reaching a maximum
of 20 species). The average result (Estimated Invasive Richness),
calculated from previous static and dynamic estimates, predicts a
50% increase in richness (10–15 species) in 20 years.
A scenario was also run for Sernancelhe, an eastern, upland
depopulating municipality (Fig. 4c) based on specific information
for this area (Concelho Local de Acc- a~ o Social de Sernancelhe,
2004) and INE reports (INE, 2007; INE, 2009). Energy consumption per capita is expected to remain constant (2.3 kWh/inhabitant) while population density is expected to continue decrease at
a constant rate (from 27 to 15 inhabitants/km2 over the last
decade). The rate of building construction is expected to decrease
in tandem with economic activity (from 0.63 to 0.28%). The
average invasive richness response to this scenario (Fig. 4d)
shows a dynamic oscillation, with a slight decrease at the end
of the simulation period induced by socio-economic influences
around a steady state resulting from the static geophysical
contribution. In this scenario a decrease in socio-economic
activities does not seem to affect invasive richness variation.
4. Discussion
Our findings indicate that simple socio-economic tendencies can
predict regional invasive plant trends. Simulations showed that
invasive plant species richness was affected by anthropogenically
induced change in ecological conditions/opportunities. Results concur with several studies that identify correlations between socioeconomic activities and the richness and distribution of plant
Please cite this article as: Santos, M., et al., Predicting trends of invasive plants richness using local socio-economic data: An
application in North Portugal. Environ. Res. (2011), doi:10.1016/j.envres.2011.03.014
M. Santos et al. / Environmental Research ] (]]]]) ]]]–]]]
invaders (e.g. Vila and Pujadas, 2001; Sharma et al., 2005; Zhu et al.,
2007; Chyron et al., 2009; Andreu and Vila , 2010). Model results
demonstrate that increasing urbanization, using socio-economic
variables as a proxy, is a possible determinant in the naturalization
of invasive species. These findings are consistent with other studies
(Kean et al., 2007; Blanchet et al., 2009; Pitt, 2009). In contrast, the
depopulating scenarios bring about more resilient behavior in the
model simulations.
The eradication of a newly established alien plant species is
considered almost impossible, highlighting the urgent need for
effective early warning systems (Andreu and Vila , 2010). Since the
intentional introduction of most invasive species in the study area
is localized (e.g. Eucalyptus globulus for paper pulp production),
the range in the extent and increase in species richness produced
by the model may result from other casual factors associated with
socio-economic trends such as direct disturbance, structural
habitat changes and other anthropogenic activities (Fergunson
et al., 2008; Andreu and Vila , 2010; Chyron et al., 2009). Modeled
invasive richness appears to be determined by socio-economic
and geophysical characteristics for each municipality (Fergunson
et al., 2008), indicating that our StDM simulations provided
improved acuity of potential structural consequences for ecological communities and habitats invaded by non-native species,
due to environmental change and disturbance associated with
land use. Species richness is considered a consistent indicator for
assessing structural and functional change in ecosystems
(Fleishman et al., 2006) and is used in contemporary modeling
applications (Santos and Cabral, 2004; Santos et al., 2010).
The ecological consequences of anthropogenic disturbance can
be only partly assessed by the occurrence of invasive plant
richness. This approach provides a useful starting point, allowing
the precise development of more complex models, where interactions and interferences such as climate change, the resilience of
local plant communities and potential pathways for species
introductions and dispersal can be introduced. Invasive species
are potential ecosystem engineers, affecting factors such as
biodiversity, geomorphology, biochemistry, hydrology and competition. These in turn alter patterns in richness, dominance and
community composition, creating distinct systems with novel
characteristics (Doren et al., 2009b). Despite the important role of
anthropogenic influences, model simulations also show that
invasive richness in a specific location is also highly influenced
by climate, geology, topography and landscape use (Arévalo et al.,
2005; Chyron et al., 2009; Doren et al., 2009b).
5. Conclusion
The StDM approach proposed in this study contributes to the
development of rapid, standardized, cost-saving assessment
methodologies, essential tools in conservation and environmental
management studies (Santos and Cabral, 2004; Yost, 2008). The
application of StDM in environmental systems is a recent development. StDM has been successfully applied, tested and validated
in several types of scenarios such as streams, reservoirs
(Cabecinha et al., 2004, 2009a), Mediterranean agro ecosystems
(Santos and Cabral, 2004; Cabral et al., 2007) and for simulating
the impact of socio-economic trends on threatened species (e.g.,
Santos et al., 2007). StDM is more mathematically intuitive than
other modeling methodologies such as Artificial Intelligence
(Džeroski et al., 1997; Mendonza and Prabhu, 2005), providing
easy explanations for underlying relations between independent
and dependent variables based on conventional linear methods
that allow more direct development of testable hypotheses.
Džeroski et al. (1997) state that rule based models using machine
learning approaches are more accessible and easily understood by
experts. StDM exhibits these structural qualities but also provides
simple, suitable and intuitive outputs that can be easily interpreted by non-experts such as resource users and policy makers.
The StDM model developed in this study captures the complexity of key ecological trends such as relevant temporal and
spatial gradients of environmental characteristics, allowing the
simulation of structural modifications when habitat and environmental conditions are affected by anthropogenically induced
change. Other goals, when methodologies for assessing ecosystem
change are developed, are the feasibility of application and if
results can be applied in other areas (Andreasen et al., 2001; Dale
and Beyeler, 2001; Duelli and Obrist, 2003; Lancelot et al., 2009).
StDM is applicable to data from systems affected by gradients of
change (e.g. Cabecinha et al., 2004; Santos and Cabral, 2004) and
we believe that our approach will pave the way for the development of more global techniques in this research area. The
ultimate goal of StDM studies is to produce simulation models
that permit the creation of landscape patterns resulting from
changes in ecosystems patterns, forming the basis of spatially
precise ecological models (Costanza and Voinov, 2004; SoaresFilho et al., 2006; Cabecinha et al., 2009b), providing an intuitive
and credible tool for decision-makers and environmental managers (Bolliger et al., 2005; Cushman et al., 2008).
The authors are indebted to all field collaborators and taxonomists who made this work possible. We are also grateful to
the reviewers’ comments and suggestions to improve our
Appendix A. Supplementary materials
Supplementary data associated with this article can be found
in the online version at doi:10.1016/j.envres.2011.03.014.
Akaike, H., 1974. A new look at the statistical model identification. Autom. Control,
IEEE Trans. 19, 716–723.
Alpert, P., Bone, E., Holzapfel, C., 2000. Invasiveness, invasibility and the role of
environmental stress in the spread of non-native plants. Perspect. Plant Ecol.
Evol. Syst. 3, 52–56.
Anderson, D., Burnham, K., Thompson, W., 2000. Null hypothesis testing: problems. Prevalence altern. J. Wildl. Manage. 64, 912–923.
Andreasen, J.K., O’Neill, R.V., Noss, R., Slosser, N.C., 2001. Considerations for the
development of a terrestrial index of ecological integrity. Ecol. Indic. 1, 21–35.
Andreu, J., Vila, M., 2010. Risk analysis of potential invasive plants in Spain. J. Nat.
Conserv. 18, 34–44.
Arévalo, J.R., Delgado, J.D., Otto, R., Naranjo, A., Salas, M., Férnandez-Palacios, J.M.,
2005. Distribution of alien vs. native plant species in roadside communities
along an altitudinal gradient in Tenerife and Gran Canaria (Canary Islands).
Perspect. Plant Ecol. Evol. Syst. 7, 185–202.
Aumann, C.A., 2007. A methodology for developing simulation models of complex
systems. Ecol. Model. 202, 385–396.
Blanchet, S., Leprier, F., Beauchard, O., Stae, J., Oberdorff, T., Brosse, S., 2009. Broadscale determinants of non-native fish species richness are context-dependent.
Proc. R. Soc. B 276, 2385–2394.
Bolliger, J., Lischke, H., Green, D.G., 2005. Simulating the spatial and temporal
dynamics of landscapes using generic and complex models. Ecol. Complex 2,
Cabecinha, E., Cortes, R., Cabral, J.A., 2004. Performance of a stochastic-dynamic
modelling methodology for running waters ecological assessment. Ecol.
Model. 175, 303–317.
Cabecinha, E., Pardal, M.A., Cortes, R., Cabral, J.A., 2009a. A stochastic dynamic
methodology (StDM) for reservoir’s water quality management: validation of a
multi-scale approach in a south European basin (Douro, Portugal). Ecol. Indic.
9, 329–345.
Cabecinha, E., Martinho, L., Moura, J.P., Pardal, M.A., Cabral, J.A., 2009b. A multiscale approach to modelling spatial and dynamic ecological patterns for
reservoir’s water quality management. Ecol. Model. 220, 2559–2569.
Please cite this article as: Santos, M., et al., Predicting trends of invasive plants richness using local socio-economic data: An
application in North Portugal. Environ. Res. (2011), doi:10.1016/j.envres.2011.03.014
M. Santos et al. / Environmental Research ] (]]]]) ]]]–]]]
Cabral, J.A., Cabecinha, E., Santos, M., Travassos, P., Silva-Santos, P., 2008.
Simulating the ecological status of changed ecosystems by holistic applications of a new stochastic dynamic methodology (StDM). In: Alonso, M.S.,
Rubio, I.M. (Eds.), Ecological Management, New Research. NovaScience Publishers, New York, pp. 123–141.
Cabral, J.A., Rocha, A., Santos, M., Crespı́, A.L., 2007. A stochastic dynamic
methodology (SDM) to facilitate handling simple bird indicators in the scope
of the agri-environmental measures problematics. Ecol. Indic. 7, 34–47.
Carvalho, A., 2004. Súmula para a caracterizac- a~ o sócio-económica de Matosinhos.
Câmara Municipal de Matosinhos.
Chyron, F., Shirley, S., Kark, S., 2009. Human-related processes drive the richness of
exotic birds in Europe. Proc. R. Soc. B 276, 47–53.
Chytrý, M., Pyšek, P., Tichý, L., Knollová, I., Danihelka, J., 2005. Invasions by alien
plants in the Czech Republic: a quantitative assessment across habitats. Preslia
77, 339–354.
Concelho Local de Acc- a~ o Social de Sernancelhe, 2004. Diagonóstico Social do
Concelho de Sernancelhe. Seguranc-a Social.
Costanza, R., Voinov, A., 2004. Introduction: spatially explicit landscape simulation
models. In: Costanza, R., Voinov, A. (Eds.), Landscape Simulation Modelling: A
Spatial Explicit, Dynamic Approach. Springer-Verlag, New York.
Crossman, N.D., Bryan, B.A., Cooke, D.A., 2011. An invasive plant and climate
change threat index for weed risk management: integrating habitat distribution pattern and dispersal process. Ecol. Indic. 11, 183–198.
Cuneo, P., Jacobson, C.R., Leishman, F.R., 2009. Landscape-scale detection and
mapping of invasive African Olive (Olea europaea L. ssp. cuspidata Wall ex G.
Don Ciferri) in SW Sydney, Australia using satellite remote sensing. Appl. Veg.
Sci. 12, 145–154.
Cushman, S.A., McGarigal, K., Nell, M.C., 2008. Parsimony in landscape metrics:
strength, universality and consitency. Ecol. Indic. 8, 691–793.
Dale, V.H., Beyeler, S.C., 2001. Challenges in the development and use of ecological
indicators. Ecol. Indic. 1, 3–10.
Davis, M.A., Grime, J.P., Thompson, K., 2000. Fluctuating resources in plant
communities: a general theory of invisability. J. Ecol. 88, 528–534.
Doren, R.F., Richards, J.H., Volin, J.C., 2009a. A conceptual ecological model to
facilitate understanding the role of invasive species in large-scale ecosystem
restoration. Ecol. Indic. 9s, s150–s160.
Doren, R.F., Volin, J.C., Richards, J.H., 2009b. Invasive exotic plant indicators for
ecosystem restoration: an example from the Everglades restoration program.
Ecol. Indic. 9s, s29–36.
Duelli, P., Obrist, M.K., 2003. Biodiversity indicators: the choice of values and
measures. Agric. Ecosyst. Environ. 98, 87–98.
Džeroski, S., Grbovic, J., Walley, W.J., Kompare, B., 1997. Using machine learning
techniques in the construction of models. 2. Data analysis with rule induction.
Ecol. Model. 95, 95–111.
Fergunson, C.A., Carvalho, L., Scott, E.M., Bowman, A.W., Kirika, A., 2008. Assessing
ecological responses to environmental change using statistical models. J. Appl.
Ecol. 45, 193–203.
Fleishman, E., Noss, R.F., Noon, B.R., 2006. Utility and limitations of species
richness metrics for conservation planning. Ecol. Indic. 6, 543–553.
Hair, J.E., Anderson, R.E., Tatham, R., Black, W.C., 1995. Multivariate Data Analysis
with Readings, 4th edn. Prentice Hall International Editions, New Jersey.
Higgings, S.I., Richardson, D.M., Cowling, R.M., Trinder-Smith, T.H., 1999. Predicting the landscape distribution of alien plants and their treat to plant diversity.
Conserv. Biol. 13, 303–313.
Hobbs, R.J., Arico, S., Aronson, J., Baron, J.S., Bridgwater, P., Cramer, V.A., Epstein,
P.R., Ewel, J.J., Klink, C.A., Lugo, A.E., Norton, D., Ojima, D., Richardson, D.M.,
Sanderson, E.W., Valladares, F., Vilá, M., Zamora, R., Zobel, M., 2006. Novel
ecosystems: theoretical and management aspects of the new ecological world
order. Global Ecol. Biogeogr. 15, 1–7.
Hurvich, C., Tsai, C., 1989. Regression and time series model in small samples.
Biometrika 76, 297–307.
ICNB, 2009. URL / þICNB/Estudos/base
þtécnicaþ 565.htmS (as posted on 18-9-2009).
INE, 2007. Retrato Territorial de Portugal, Instuto Nacional de Estatı́stica.
INE, 2009. URL: /
(as posted on 18-9-2009).
Kean, J.M., Overton, J., Williams, P., Buxton, R., 2007. Modelling weed spread in
heterogeneous landscapes. NZIMA Weeds Workshop.
Keller, M.S., Chew, F.S., Goodale, B.C., Reed, J.M., 2006. Modelling the impacts of
two exotic invasive species on a native butterfly: top-down vs. bottom-up
effects. J. Anim. Ecol. 75, 777–788.
Kim, K.D., 2005. Invasive plants on disturbed Korean sand dunes. Estuar. Coast.
Shelf Sci. 62, 353–364.
Krebs, C.J., 1999. Ecological Methodology, 2nd ed. Addison-Wesley Educational
Publishers, Inc..
Lake, J.L., Leishman, M.R., 2004. Invasion success of exotic plants in natural
ecosystems: the role of disturbance, plant attributes and freedom from
herbivores. Biol. Conserv. 117, 215–226.
Lancelot, C., Rousseau, V., Gyphens, N., 2009. Ecologically based indicators for
Phaeocystis disturbance in eutrophied Belgian coastal waters (Southern North
Sea) based on field observations and ecological modelling. J Sea Res 61, 44–49.
Leprier, F., Beauchard, O., Blanchet, S., Oberdorff, T., Brosse, S., 2008. Fish invasions
in the world’s river systems: when natural processes are blurred by human
activities. PLoS Biol. 6, e28. doi:10.1371/journal.pbio.0060028.
Marchante, E., Freitas, H., Marchante, H., 2008. Guia prático para a identificac- a~ o de
Plantas Invasoras de Portugal Continental. Coimbra. Imprensa da Universidade
de Coimbra.
Mendonza, G.A., Prabhu, R., 2005. Combining participatory modelling and multicriteria for community based forest management. For. Ecol. Manag. 207, 145–156.
Meiners, S.J., Rye, T.A., Klass, J.R., 2008. On a level field: the utility of studying native
and non-native species in successional systems. Appl. Veg. Sci. 12, 45–53.
Molina, R.T., Téllez, T.R., Alcaraz, J.A.D., 1992. Aportación a la bioclimatologia de
Portugal. An. Jard. Bot. Madrid 49, 245–264.
Mooney, H.A., 2005. Invasive species: the nature of the problem. In: Mooney, H.A.,
Mack, R.N., McNeely, J.A., Neville, L.E., Schei, P.J., Waage, J.K. (Eds.), Invasive
Alien Species: A New Synthesis. Island Press, Washington, pp. 1–15.
O’Hara, R.B., Kotze, D.J., 2010. Do not log-transform count data. Methods Ecol. Evol.
1, 118–122.
Peterson, A.T., 2003. Predicting the geography of species invasions via ecological
niche modelling. Q. Rev. Biol. 78, 419–433.
Peterson, A.T., Papes, M., Kluza, D.A., 2003. Predicting the potential invasive
distributions of four alien plant species in North America. Weed Sci. 51,
Pino, J., Font, X., Carbó, J., Jove, M., Pallare’s, L., 2005. Large-scale correlates of alien
plant invasion in Catalonia (NE of Spain). Biol. Conserv. 122, 339–350.
Pinto-Correia, T., 2000. Future development in Portuguese rural areas: how to
manage agricultural support for landscape conservation? Landscape Urban
Plann. 50, 95–106.
Pitt, J.P.W., 2009. Modelling the spread of invasive species across heterogeneous
landscapes. Ph.D. Thesis. Lincoln University.
Rangel, T.F.L., Diniz-Filho, J.A.F., Bini, L.M., 2010. SAM: a comprehensive application for spatial analysis in macroecology. Ecography 33, 45–60.
Ribeiro, S.C., Lovett, A., 2009. Associations between forest characteristics and
socio-economic development: a case study from Portugal. J. Environ. Manage.
90, 2873–2881.
Richardson, D.M., Rouget, M., Ralston, S.J., Cowling, R.M., Rensburg, B.J.V., Thuiller,
W., 2005. Species richness of alien plants in South Africa: environmental
correlates and the relationship with indigenous plant species richness.
Ecoscience 12, 391–402.
Rivas-Martı́nez, S., Gandullo, J.M., Allué, J.L., Montero, J.L., González, J.L., 1987.
Memoria del mapa de Series de Vegetación de España. ICONA, Madrid.
Santos, M., Cabral, J.A., 2004. Development of a stochastic dynamic model for
ecological indicators prediction in changed Mediterranean agroecosytems of
north-eastern Portugal. Ecol. Indic. 3, 285–303.
Santos, M., 2009. Simplifying Complexity: Applications of Stochastic Dynamic
Methodology (StDM) in Terrestrial Ecology. Ph.D. thesis. University of Trás-osMontes e Alto Douro.
Santos, M., Travassos, P., Repas, M., Cabral, J.A., 2010. Modelling the performance
of bird surveys in non-standard weather conditions: general applications with
special reference to mountain ecosystems. Ecol. Indic. 10, 192–215.
Santos, M., Vaz, C., Travassos, P., Cabral, J.A., 2007. Simulating the impact of socioeconomic trends on threatened Iberian wolf populations Canis lupus signatus in
north-eastern Portugal. Ecol. Indic. 7, 649–664.
Sharma, G.P., Singh, J.S., Ragubanshi, A.S., 2005. Plant invasions: emerging trends
and future implications. Curr. Sci. 88, 726–734.
Soares-Filho, B.S., Nepstad, D.C., Curran, L.M., Cerqueira, G.C., Garcia, R.A., Ramos,
C.A., Voll, E., McDonald, A., Lefebvre, P., Schlesinger, P., 2006. Modelling
conservation in the Amazon basin. Nature 440, 520–523.
Sokal, R.R., Rohlf, F.J., 1995. Biometry, 3 edn. W.H. Freeman and Company,
New York.
Steele, B.M., Reedy, K., Nemani, R.R., 2005. A regression strategy for analyzing
environmental data generated by spatio-temporal processes. Ecol. Model. 181,
Stohlgren, T.J., Chong, G.W., Schell, L.D., Rimar, K.A., Otsuki, Y., Lee, M., Kalkhan,
M.A., Villa, C.A., 2002. Assessing vulnerability to invasion by nonnative plant
species at multiple spatial scales. Environ. Manage. 29, 566–577.
Taylor, B.W., Irwin, R.E., 2004. Linking economic activities to the distribution of
exotic plants. Proc. Nat. Acad. Sci. 101, 17725–17730.
Thuiller, W., Midgley, G.F., Rouget, M., Cowling, R.M., 2006. Predicting patterns
of plant species richness in megadiverse South Africa. Ecography 29,
Ver Hoef, J.M., Boveng, P.L., 2007. Quasi-Poisson vs. negative binomial regression:
how should we model overdispersed count data? Ecology 88, 2766–2772.
Vila , M., Pujadas, J., 2001. Land use and socio-economic correlates on plant
invasions in European and North African countries. Biol. Conserv. 100,
Williamson, M., 1996. Biological Invasions. Chapman & Hall, London.
Yost, A.C., 2008. Probabilistic modelling and mapping of plant indicator in a
Northeast Oregon industrial forest, USA. Ecol. Indic. 8, 45–56.
Zar, J.H., 1996. Biostatistical Analysis. Prentice-Hall, Englewood Cliffs, NJ.
Zhu, L., Sun, O.J., Sang, W., Li, Z., Ma, K., 2007. Predicting the spatial distribution of
a species (Eupatorium adenoforum) in China. Landscape Ecol. 22, 1143–1154.
Please cite this article as: Santos, M., et al., Predicting trends of invasive plants richness using local socio-economic data: An
application in North Portugal. Environ. Res. (2011), doi:10.1016/j.envres.2011.03.014