Predicting trends of invasive plants richness using local socio
Transcrição
Predicting trends of invasive plants richness using local socio
Environmental Research ] (]]]]) ]]]–]]] Contents lists available at ScienceDirect Environmental Research journal homepage: www.elsevier.com/locate/envres 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 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 b 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 c 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 Keywords: 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). n 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. doi:10.1016/j.envres.2011.03.014 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 2 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 ] (]]]]) ]]]–]]] 3 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. Variables Invasive species richness (response variables) Richness of species predicted by geophysical characteristics (GEO) Richness of species predicted by socio-economic characteristics (POP) Richness of invasive species Specification Model codes Number of species Number of species Richness Geo Richness Pop Averaged number of species ((Richness Geo þ Richness Pop)/ Estimated invasive 2) richness 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 Energy Building variation Not Not Not Not Not applicable applicable applicable applicable applicable 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 4 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. MODEL Variable Coef SE Co T p Bo Co SE Bo GEO GEO GEO GEO Constant Log longitude wide Log max altitude Log min altitude 1.090 0.879 0.472 0.218 0.537 0.274 0.218 0.063 2.03 3.203 2.159 3.451 0.045 0.002 0.034 0.001 1.044 0.904 0.469 0.213 0.534 0.265 0.226 0.062 POP POP POP POP POP Constant Log total population density Log total population Log building variation Log energy 1.616 0.403 0.338 1.040 0.632 0.450 0.166 0.148 0.275 0.383 3.592 2.428 2.285 3.782 1.649 0.001 0.017 0.025 0.000 0.103 1.538 0.402 0.316 1.001 0.627 0.382 0.163 0.130 0.236 0.322 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 richness 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 9 3.0 8 2.5 Socio-economic variables Socio-economic variables M. Santos et al. / Environmental Research ] (]]]]) ]]]–]]] 7 6 5 4 Population density Building variation Energy consumption 3 1.5 Population density Building variation Energy consumption 1.0 0.0 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 Years Years 1.25 22 Richness Geo Richness Pop Estimated Richness 1.20 Invasive species richness Invasive species richness 2.0 0.5 2 20 5 18 16 14 12 1.15 1.10 1.05 1.00 0.95 10 Richness Geo Richness Pop Estimated Richness 0.90 8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Years 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Years 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 6 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). 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