What drives invasibility? A multi-model inference test and spatialmodelling of alien plant species richness patterns in northernPortugal

Joana Vicente, Paulo Alves, Christophe Randin, Antoine Guisan and Joao Honrado

J. Vicente (jsvicente@fc.up.pt), P. Alves and J. Honrado, Centro de Investigacao em Biodiversidade e Recursos Geneticos (CIBIO) and Fac. deCiencias da Univ. do Porto, Dept Biologia, Edifıcio FC4, sala 1.29, Rua do Campo Alegre, S/N, PT-4169-007 Porto, Portugal. � C. Randin,Inst. of Botany, Univ. of Basel, Schonbeinstrasse 6, CH-4056 Basel, Switzerland. � A. Guisan, Dept d’Ecologie et d’Evolution (DEE), Univ.Lausanne, Batiment de Biologie, CH-1015 Lausanne, Switzerland.

Understanding and predicting biological invasions can focus either on traits that favour species invasiveness or on featuresof the receiving communities, habitats or landscapes that promote their invasibility. Here, we address invasibility at theregional scale, testing whether some habitats and landscapes are more invasible than others by fitting models that relatealien plant species richness to various environmental predictors. We use a multi-model information-theoretic approach toassess invasibility by modelling spatial and ecological patterns of alien invasion in landscape mosaics, and by testingcompeting hypotheses of environmental factors that may control invasibility. Because invasibility may be mediated byparticular characteristics of invasiveness, we classified alien species according to their C-S-R plant strategies. We illustratethis approach with a set of 86 alien species in northern Portugal. We first focus on predictors influencing species richnessand expressing invasibility, and then evaluate whether distinct plant strategies respond to the same or different groups ofenvironmental predictors. We confirmed climate as a primary determinant of alien invasions, and as a primaryenvironmental gradient determining landscape invasibility. The effects of secondary gradients were detected only whenthe area was sub-sampled according to predictions based on the primary gradient. Then, multiple predictor typesinfluenced patterns of alien species richness, with some types (landscape composition, topography and fire regime)prevailing over others. Alien species richness responded most strongly to extreme land management regimes, suggestingthat intermediate disturbance induces biotic resistance by favouring native species richness. Land-use intensificationfacilitated alien invasion, whereas conservation areas hosted few invaders, highlighting the importance of ecosystemstability in preventing invasions. Plants with different strategies exhibited different responses to environmental gradients,particularly when the variations of the primary gradient were narrowed by sub-sampling. Such differential responses ofplant strategies suggest using distinct control and eradication approaches for different areas and alien plant groups.

Biological invasions constitute the second most severe threatto biodiversity around the world, after habitat destruction,and represent a global problem causing biotic homogenisa-tion along with enormous ecological and economical losses(Theoharides and Dukes 2007). Invasion can be defined asthe emergence and geographical expansion of a species in anarea where it was previously absent (Vermeij 1996).Invasion of (semi-) natural ecosystems by alien species isknown to induce changes in the composition, structure andfunction of those ecosystems, with important consequencesfor both the conservation of native biodiversity and theprovision of ecosystem services (Vitousek 1990, Le Maitreet al. 2004). Some of the most negative ecological impactsof invasive alien species are related to competition withnative species, which can lead to the invasive species

occupying a dominant position in their new environmentor even to the replacement of the native residents. Suchreplacement is one of the key threats to biodiversity andecosystem function in large parts of the world (McKinneyand Lockwood 1999, Pauchard and Shea 2006).

Many pathways for the introduction of alien species arelinked to, or caused by, human activities at local, regionaland continental scales. Ongoing climate and land-usechanges are expected to boost invasion in a number ofhabitats (Theoharides and Dukes 2007). Ecosystem inva-sion by alien species is still increasing and is a majorcontributor to the loss of biodiversity at the habitat andlandscape levels. Control and eradication planning areexpensive, and their cost-effectiveness remains to beevaluated. Therefore, anticipating future invasions is a

Ecography 33: 1081�1092, 2010

doi: 10.1111/j.1600-0587.2010.6380.x

# 2010 The Authors. Ecography # 2010 Ecography

Subject Editor: Catherine Grahm. Accepted 5 August 2010

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major task in conservation biology, since it is crucial toforecast accurately where expansions or new invasions willmost likely take place (Theoharides and Dukes 2007).

Understanding and anticipating invasions can be ap-proached from two perspectives, depending on whether thefocus is on traits of species that enable them to invade(‘invasiveness’), or on traits of the receiving communities,habitats or landscapes (‘invasibility’; Richardson and Pysek2006). Here, we focus mostly on invasibility to test whethersome habitats or landscapes are more invasible than others(‘differential invasibility’). Invasibility can be assessed athabitat or landscape levels (Richardson and Pysek 2006),and at both local and regional scales (Pauchard and Shea2006). In this context, habitat distribution models (HDMs)derived from spatially explicit information on habitats andlandscapes can be used to predict invasibility by testing orquantifying relationships of invasive species richness withvarious characteristics of the habitats and/or landscapes.The rationale is that distinct habitats and landscapes willshow different susceptibility to invasion, which can then beused to assess invasion risk (Chytry et al. 2008). Commonpredictors used to model invasive plant species richness anddistributions at the regional scale include topography,climate and geology/substrate (Holmes et al. 2005, Pinoet al. 2005). However, human disturbance also plays animportant role as a determinant of biological invasionsthrough the introduction and dispersion of new propagules.This may be reflected by distances from human settlementsand infrastructure, as well as by disturbance regimes, whichin turn can determine landscape composition, fire regime,and landscape fragmentation (Le Maitre et al. 2004).

Even though invasibility is thought to be primarilydetermined by habitat suitability and propagule pressure(Williams et al. 2005, Brooks 2007, Kowarik and Lippe2007), it is also expected to be further mediated by plantstrategies, life forms, and the region of origin of invaders(i.e. invasiveness of individual plant species; Pysek andRichardson 2007). A commonly cited stabilising mechan-ism for invasion resistance is an optimal partitioning ofavailable resources (Chesson 2000), and thus maximumniche complementarity, by diverse plant community assem-blages. This supports the assumption that the diversity ofplant functional groups in receiving communities is amechanism for resisting invasion (Theoharides and Dukes2007). Many functional classifications have been proposedfor exploring invasion biology, including those based on lifestrategies, growth forms and reproductive strategies. Forplant invasion, the integrative power of the ‘C-S-Rfunctional signature’ (i.e. the relative abundance of Com-petitors, Stress-tolerants and Ruderals in a given speciespool), which is based on Grime’s (1977) classification and isrelated to gradients of stress and disturbance, can be used toindicate levels of resistance, resilience, eutrophication anddereliction (Hunt et al. 2004). In addition, using functionalgroups instead of individual species can be helpful tounderstand community dynamics (Caccianiga et al. 2006).

One approach to assessing the role of the environment incontrolling biological invasions (i.e. invasibility) is to fitstatistical models relating alien species distribution anddiversity to various environmental predictors that areexpected to affect invasibility (Thuiller et al. 2005). Thesemodels have been increasingly used to predict the geo-

graphic distribution of taxa and biodiversity (Guisan andZimmermann 2000), or to test hypotheses about whichenvironmental variables (hereafter predictors) determinedistributions (Guisan and Thuiller 2005, Austin 2007).Statistical predictor selection procedures often result in theretention of a single best model with only one set ofpredictor variables (Guisan and Zimmermann 2000).However, in recent years, modern statistical science hasbeen moving away from traditional methodologies basedsolely on such null hypothesis testing (Stephens et al. 2007).Instead, it has been suggested that single, best-fit modelsshould be replaced by information-theoretic approaches(Burnham and Anderson 2002, Lavoue and Droz 2009).One of the most commonly used information metrics is theAkaike information criterion (AIC; Akaike 1973, Reinekingand Schoder 2006) and its derivatives. AIC is an estimatorthat quantifies the information lost when a model is fittedto approximate the ‘truth’ (i.e. predefined real distribution;Burnham and Anderson 2002). Hence, it is an estimate ofthe formal strength of evidence (support) for each hypoth-esis and its related model. By repeating this process formany models of different predictors selected from a fixedpool of predictors (hypotheses), multi-model selection andinference ranks the statistical support for each of thecompeting hypotheses, resulting in multiple models thatbest explain the ecological system. Unfortunately, fewexamples exist of such a multi-model information-theoreticapproach implemented in a predictive species and diversitydistribution modelling framework (Dormann et al. 2008,Gray et al. 2009, Wisz and Guisan 2009).

Determining whether single or multiple environmentalinfluences control invasibility and how invasiveness canmodulate this response would support the use of universalor specific controls and eradication strategies for differenthabitats and/or alien plant groups. Here we use aninformation-theoretic approach to assess habitat and land-scape invasibility at the regional scale by: 1) modelling thespatial and ecological patterns of alien plant invasions inlandscape mosaics; and 2) testing competing hypotheses ofwhich environmental predictors control invasibility. Weillustrate the approach with a set of 86 alien species innorthwestern Portugal, classified into plant functional typesaccording to their C-S-R life strategies. We first focus onpredictors influencing total alien species richness andexpressing invasibility. We then evaluate whether richnessof the distinct C-S-R groups responds to the same ordifferent groups of environmental predictors in order tofurther assess the possible influence of species invasiveness.

Methods

Analytical framework: questions, hypotheses andcompeting models

Analyses were organised according to two major researchquestions. The first addressed total alien species richness,and the second addressed species richness for distinct alienplant strategies. For each of these two questions, we testedthree general invasibility hypotheses using combinations ofcompeting models related to specific hypotheses (Fig. 1,Table 1, 2). A total of 13 competing models and related

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specific hypotheses was developed from combinations ofpredictor types or sets of predictors of different types (seebelow, Table 3). Competing models and their underlyingspecific hypotheses and principles for our four responsevariables are given in Table 2. Details of the variablesincluded in each model are given in Table 4.

The three general hypotheses are related to: 1) the effectsof single-type invasibility controls; 2) the influence of

multi-type invasibility controls; and 3) the role of land-useintensity. Land-use intensity is a recognized determinant ofalien invasion, with areas under extensive managementbeing less prone to invasion but more challenging in termsof alien control than areas where intensive land use isthe norm (Brooks 2007). Therefore, besides assessing theinfluence of single-type and multi-type controls, we wereinterested in testing more specifically whether land-use intensity was relevant to landscape invasibility, particu-larly when a large set of alien invasive species exhibitingdistinct plant strategies was analysed. By comparing theweights of competing models and associated hypothesesexpressing distinct land-use intensities, we expected to gainfurther insight into the specific responses of alien speciesand strategies to features of land management regimes. Ourstudy area is ideal for assessing the role of land-use in plantinvasions because it contains land management regimesranging from wilderness and low-intensity cattle grazing inthe mountains, to urbanised land with well-developedinfrastructures and intensive agriculture at lower altitudes.To formulate general hypotheses related to land-useintensity (both for species richness and richness of distinctplant strategies), we used Brooks’ (2007) framework, inwhich four major land-use types are recognised along agradient of impacts of land management on ecosystems. Wecombined Brooks’ ‘utilization’ and ‘replacement’ areas,since they represent intermediate land-use intensities whencompared to the extreme ‘conservation’ and ‘removal’management types (Table 2).

Nested environmental gradient approach

Because most alien invasive plant species in our study areaare known to be frost-sensitive, we expected climate to actas a strong primary gradient determining the spatial patternof our response variables and masking the effect of othergradients. Therefore, we used a spatially nested approach to

Table 1. Questions and general hypotheses related to controls of alien invasion (for details on more specific hypotheses, see Table 2).

Questions General hypotheses Description

Question 1. What controlsalien species richness?

Simple-type controls Species richness is explained mainly by one or several variables within a group,with an expected prevailing role of climate, landscape composition, andregional corridors promoting dispersal (H1 to H7 in Table 2).

Multi-type controls Species richness is determined by combinations of variables selected from thepredictor groups, with the prevalence of benign environmental conditionsexpected to be the most important (H8 to H10 in Table 2).

Land-use intensity Areas under intermediate management regimes host the highest numbers ofalien species, followed by areas under more intensive management, and then bythose under less intensive management (H11 to H13 in Table 2).

Question 2. Do differentplant strategies yielddistinct models?

Simple-type controls Patterns of different plant strategies are explained by multiple environmentalpredictors, with a prevailing role of climate and geology for stress tolerantspecies (S-strategists), climate for competitor species (C-strategists), and land-scape composition for ruderal species (R-strategists) (H1 to H7 in Table 2).

Multi-type controls The prevalence of benign environmental conditions constrains S-strategists andpromotes C-strategists, the presence of regional dispersal corridors mostlypromotes R-strategists, and environmental heterogeneity promotes the presenceof all three strategies (H8 to H10 in Table 2).

Land-use intensity Areas under intensive management regimes host the highest numbers of R- andS-strategists, followed by areas under intermediate management regimes andthen those under less intensive management. C-strategists exhibit an inversepattern, with areas under less intensive management hosting the highestnumbers of species and areas under intensive management hosting fewer species(H11 to H13 in Table 2).

Figure 1. Conceptual framework of the nested approach used toassess the effects of multiple environmental gradients on plantinvasion ecology in northwestern Portugal. The primary gradientmodel was related to climate in all cases (Step 1). Using the spatialpredictions of that model, three different areas were selected, ‘fullarea’; ‘second, third, and fourth quartiles’ (referred to as ‘areaabove the first quartile’ hereafter) and ‘third and fourth quartiles’(referred to as ‘area above the second quartile’ hereafter), and thenwere used to fit models with the primary and secondary gradientsas predictors. For the three geographical areas and for each of thefour response variables (total and C, S, and R species richness),hypotheses were tested using multi-model inference (Step 3) toanswer our research questions (Step 4).

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Table 2. Specific hypotheses with their ecological rationale (see Table 1 for details of general hypotheses).

Specific hypotheses Name Rationale

H1 Climate Minimum temperatures control habitat invasibility by frost-sensitive alien invaders (Pino et al. 2005), and summer drought stress controlsalien invasion in Mediterranean ecosystems (Godoy et al. 2008).

H2 Landscape composition Land cover and land-use controls alien invasion because they determine suitable habitat availability, and because anthropogenic habitatshave been shown to provide suitable conditions for more invasive species (Chytry et al. 2008). Also, more alien invaders can find suitableconditions in landscapes with greater compositional diversity (Pino et al. 2005)

H3 Landscape structure and function Landscape invasibility is controlled by patch shape and size, which determine ecotone density and diversity (Le Maitre et al. 2004, Dufouret al. 2006). The density of the local hydrographic network is related to landscape fragmentation, which provides more opportunities forlocal survival and dispersal (Foxcroft et al. 2007). Primary productivity is correlated with both native and alien plant species richness(Williams et al. 2005).

H4 Fire regime Fire is a common source of disturbance in Mediterranean areas and influences population dynamics of invasive plants (Keeley et al. 2005).H5 Geology Different bedrock types support distinct soil properties, which affect alien invasion (Rose and Hermanutz 2004), and also support distinct

landscape mosaics in the region, thus providing different sets of habitats for alien invaders. Also, more alien invaders can find suitableconditions in landscapes with greater soil diversity.

H6 Topography The local diversity of terrain morphology controls species richness, since more complex terrain usually provides a wider diversity of habitattypes (Dufour et al. 2006). Topographic diversity is also related to local hydrographic networks, thus controlling alien invasion in riparianhabitats (Holmes et al. 2005).

H7 Regional dispersal corridors Many invasive species use large corridors (e.g. forests or floodplains along rivers) as preferential dispersal routes (Pysek and Prach 1993,Pauchard and Shea 2006).

H8 Benign environmental conditions Benign climate conditions and moist, nutrient rich soils promote species richness (Rose and Hermanutz 2004, Pino et al. 2005, Williamset al. 2005).

H9 Environmental heterogeneity The variability of environmental conditions (land-use types, topography, soils and landscape configuration) promotes species richness(Dufour et al. 2006).

H10 Total dispersal corridors The presence of corridors controls alien invasion, since many invasive species use corridor networks as dispersal routes (Riffell andGutzwiller 1996, Parendes and Jones 2000, Foxcroft et al. 2007).

H11 Utilization/replacement areas ‘Utilization’ and ‘Replacement’ land uses (Brooks 2007), with intermediate disturbance regimes, host the highest numbers of alien species.H12 Removal areas ‘Removal’ land uses (Brooks 2007), with more intensive disturbance regimes, host an intermediate number of alien species.H13 Conservation areas ‘Conservation’ land uses (Brooks 2007), with less intensive disturbance, host the lowest number of alien species.

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assess the relative importance of secondary environmentalgradients (Fig. 1). In such an approach, referred to hereafteras the nested environmental gradient approach, predictionsfrom a species richness model calibrated with the environ-mental predictors related to climate (Fig. 1 � Step 1) wereused to sub-sample the study area. This sub-sampling wasdone by using the quartiles of predictions from the climaticGLM and resulted in areas that are progressively morehomogeneous regarding the primary gradient and withgreater predicted alien species richness (Fig. 1 � Step 2). Inthis way, we focused on those areas that are moreproblematic in terms of invasion and conservation andthat provide a suitable setting to address the effects ofmultiple environmental gradients on alien species richnessin the more invasible areas. These areas were then used asthe extent for model selection based on the informationcriterion to test research hypotheses, allowing secondarygradients to be more easily detected (Fig. 1 � Step 3).

Study area

The study area is located in the northwestern Portugal(8852?�882?W, 41824?�4289?N; Fig. 2). It covers an area of3462 km2 size at the transition between the Atlantic andMediterranean biogeographic regions. Elevation rangesfrom sea level to 1540 m in the eastern mountains, withvalleys of major rivers running from east to west. Theannual mean temperature ranges from ca 98C to 158C,and the mean total annual precipitation varies between ca1200 mm in the lowlands to 3000 mm in the easternmountain summits.

Sampling strategy

The study area was first stratified based on the mean annualtemperature (climate), bedrock type (geology) and percen-tage of forest cover, thus reflecting the major environmentalgradients within the geographic region. The mean annualtemperature and percentage of forest cover were each splitinto three classes by identifying the natural breaks in theirdistributions in ArcGIS (ESRI 2008). Bedrock types were

reclassified qualitatively as granitic rocks, schistose rocks,and all other types that have a limited distribution in thearea (Table 5). The study area was then stratified bycombining these classes to generate 27 strata, of which 23were represented in the area. This stratification wasperformed using the ArcGIS spatial analyst extension(ESRI 2008).

We then used an equal-stratified sampling design (Hirzeland Guisan 2002) to randomly select four plots of 1 km2

size (corresponding to the 1�1 km cells of the GIS layers;hereafter cells) in each stratum, except in one stratum thatwas represented by only three cells. All three cells wereselected for sampling in this stratum. The 91 selected cellswere surveyed between April and May of 2008, and theoccurrence of alien plant species was recorded using fixedsampling effort (one hour per cell) while visiting all habitattypes on a targeted, non-systematic approach. The samplingeffort was distributed according to the relative cover ofhabitat types in each landscape mosaic.

Response variables

Alien plant species richness was estimated by combining allalien plant species occurring in at least one of the 91surveyed cells. We did not measure or include in ouranalysis native plant richness. Alien species were thenclassified according to the C-S-R plant strategy classificationof Grime (1977) by relating each species to one of the sevenstrategies (Supplementary material Appendix 1). Becauseinformation about the abundance of alien plants was notavailable to compute a functional signature (as proposed byHunt et al. 2004), we calculated the frequency of eachextreme strategy (C, S, and R) in each cell. Speciesbelonging to intermediate strategies were used to computethe frequency of both corresponding extreme strategies (e.g.a species classified as CS was used to estimate the richness ofboth C-strategists and S-strategists because it exhibits traitsrelated to both strategies). Four response variables were thusused for model fitting: total species richness (SR), compe-titor species richness (SRC), stress tolerant species richness(SRS), and ruderal species richness (SRR).

Table 3. Predictors used in the models, grouped into environmental types that reflect their ecological meaning, and the correspondingscientific references.

Environmental types Predictors References

Climate TMN (minimum temperature of the coldest month) Arevalo et al. 2005SPRE (summer precipitation) Pino et al. 2005

Godoy et al. 2008Landscape composition pNFo (% cover of natural forest) Pino et al. 2005

pUrb (% cover of urban areas) Chytry et al. 2008pAFo (% cover of forest stands)SWIlu (local diversity of land cover types)

Landscape structure and function MSI (mean shape index � average perimeter-to-area ratio for allpatches reflecting complexity)

Le Maitre et al. 2004

dHNe (density of local hydrographic network) Williams et al. 2005GPP (mean gross annual primary productivity)

Fire regime NFir (total number of fire occurrences) Keeley et al. 2005Geology pGra (percentage of granite) Rose and Hermanutz 2004

SWIso (local diversity of soil types) Dufour et al. 2006pFlu (percentage of fluvisols)

Topography SWIsl (local variation of slope) Holmes et al. 2005Regional dispersal corridors disH (distance to main rivers) Pauchard and Shea 2006

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Predictors

We selected 15 literature-based predictors to explain alienspecies richness (Table 3). These predictors were thengrouped into seven environmental types reflecting direct,indirect and disturbance variables (sensu Austin andHeyligers 1989, Guisan and Zimmermann 2000; Table 3).Depending on a set of a priori hypotheses, predictors wereused either by types or independently in multivariate models(Table 4). To reduce multicolinearity, only predictors with aSpearman correlationB90.7 and a generalized varianceinflation factor VIFB5 (Neter et al. 1983) were used duringmodelling.

Model calibration and model selection

We fit a set of competing models and applied multi-modelinference (MMI; Burnham and Anderson 2002) to assesshow well each model was supported by the data. We usedthe corrected Akaike information criterion (AIC; Akaike1973) for small sample sizes (AICc, Shono 2000), asrecommended when the ratio between n (the number ofobservations used to fit the model) and K (the number ofparameters in the largest model) isB40 (Shono 2000,Burnham and Anderson 2002). We also limited themaximum number of predictors per model to four becauseof small sample sizes. For comparing among models wecalculated the AICc difference, where ^i�AICc initial �AICc minimum (Burnham and Anderson 2002). Thesedifferences (^i) were used to derive Akaike weights (wi),which represent the probability that a candidate model willbe the best approximating and most parsimonious modelgiven the data and set of models. Akaike weights are scaledbetween zero and one and represent the proportionalsupport for a particular model given all models. Finally,we averaged all competing models weighted by their wi andused the averaged model for spatial prediction.

All models were fit using GLMs in R (R 2.9.2, RDevelopment Core Team 2008) and associated packagesavailable in CRAN (http://cran.r-project.org). Species rich-ness was used as the response variable in GLMs withPoisson variance and log link function (Vincent andHaworth 1983, Guisan and Zimmermann 2000). Second-order polynomials (linear and quadratic terms) were

Figure 2. Study area (a) and its location in the Iberian Peninsula(b) and in Europe (c).

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allowed for each predictor in the GLMs, with the linearterm being forced in the model each time the quadraticterm was retained (adapted from Burnham and Anderson2002, Wisz and Guisan 2009). The logistic regressionequations were spatially implemented using the rastercalculator in the ArcGIS 9.3 spatial analyst extension(ESRI 2008).

Results

The results of the Akaike weights (wi) for all the hypothesesrelated to alien species richness and alien plant strategiesrichness are summarised in Table 6. Note that the Akaikeweights (wi) always sum up to 1.

Hypothesis testing � alien plant richness

Single-type controlsThe best approximating and most parsimonious specifichypothesis given the data and all competing specifichypotheses was based on climate variables (H1, wi�0.855), and reflects the string influence of climate in ourstudy area. When applying the nested gradient approach,specific hypotheses related to climate (H1) and landscapecomposition (H2) were the best supported for the areaabove the first quartile; however, the landscape compositionspecific hypothesis had substantially less adjustment to thedata (wi�0.007, compared to wi �0.833 for climate).The nested approach provided evidence of effects of all thesecondary environmental gradients for the area above thesecond quartile. All seven competing specific hypothesesshowed high wi when compared to the full area, and thearea above the first quartile. In addition to the hypothesisbased on the primary gradient (H1, wi�0.521), otherspecific hypotheses that represent secondary gradients werealso important, including geology (H5, wi�0.096) and fireregime (H4, wi�0.077).

Multi-type controlsComparing the results of the three competing specifichypotheses representing multi-type controls, i.e. benignenvironmental conditions (H8), environmental heterogene-ity (H9) and dispersal corridors (H10), the model reflectingbenign environmental conditions was best for all test areas(Table 6). In contrast, specific hypotheses related to

Table 5. Variables used in the equal-stratified sampling design.

Variabletype

Variable Breaks Classes

Climate Mean annualtemperature (8C)

Natural breaks 8.1�11.2

11.2�13.313.3�15.0

Geology Bedrock type Qualitativebreaks

granites

schistothers

Landscape Percentage of naturalforests

Natural breaks 0�39.6

39.6�72.572.5�100

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environmental heterogeneity and dispersal corridors pre-sented some weight only when the area above the secondquartile was used.

Land-use intensityWhen competing specific hypotheses reflecting differentland-use intensities (H11�H13) were compared, modelselection for the whole area and for the area above the firstquartile showed very low values of wi for all three specifichypotheses. When the area above the second quartile wasused, the best models were those including removal areas(H12, wi�0.061) and conservation areas (H13, wi�0.059),suggesting that alien species richness responds more tovariations within the two extreme land-use intensities thanto variations within areas with intermediate managementregimes.

Hypothesis testing � alien plant strategies

Single-type controlsAmong competing hypotheses related to different types ofenvironmental variables (H1 to H7; Table 3), the bestapproximating and most parsimonious specific hypothesisto explain the species richness of all three strategies wasclimate (SRC, SRS, and SRR: H1 wi�0.8; Table 6), onceagain highlighting the role of climate as a primaryenvironmental gradient in the study area. When analyzingthe area above the first quartile, climatic (H1) was againselected as the best-supported specific hypothesis for allthree strategies. The landscape composition hypothesis wasalso selected as best supported for S-strategists (H2 wi�0.663) and as less supported for R-strategists (H2 wi�0.007). For S-strategists, fire (H4, wi� 0.003) andtopography (H6, wi� 0.004) also showed some impor-tance. When the area above the second quartile wasconsidered, all specific hypotheses were selected for at leastone of the three plant strategies, with a prevailing role forclimate (H1), fire regime (H4), geology (H5), and topo-graphy (H6; Table 6). The landscape composition hypoth-esis (H2) was most relevant for S-strategist richness. Thespecific hypothesis expressing regional dispersal corridors(H7) was selected for all three strategies, but with bettersupport for C- and S-strategists.

Multi-type controlsAmong the three competing specific hypotheses represent-ing multi-type controls (H8�H10) for the whole area, andfor the area above the first quartile, only the one expressingbenign environmental conditions was selected as bestsupported (H8; Table 4). When the area above the secondquartile was considered, all three specific hypotheses wereselected for all three strategies, with benign environmentalconditions (H8) being the best supported, followed byenvironmental heterogeneity (H9), and by total dispersalcorridors (H10).

Land-use intensityAlien plant strategies exhibited contrasting responses tospecific hypotheses expressing land-use intensity (H11�H13;Table 6). For all three strategies, specific hypothesisselection for the whole area showed very low values of wi

for all three competing hypotheses. In the area above thefirst quartile, only the specific hypothesis related to removalareas (H12) had any weight, and only for S-strategists (wi�0.018). Finally, in the area above the second quartile, allthree competing specific hypotheses showed some weightfor all three strategies. The conservation areas specifichypothesis (H13) was the best fit for C-strategists (wi�0.213), followed by H12 (removal areas; wi�0.015) andH11 (utilization/replacement areas; wi�0.009). For S- andR-strategists, the best specific hypothesis was the one relatedto removal areas (H12), followed by conservation areas(H13) and finally by utilization/replacement areas (H11;Table 6).

Spatial predictions from average models

Spatial predictions from average models for each combina-tion of response variable and test area (nested gradientapproach) are presented in Fig. 3. These spatial predictionsare progressively more detailed regarding patterns of speciesrichness in the more invasible areas as we move from thewhole area to the areas above the first and second quartiles.Spatial predictions for C- and R-strategists are very similar,e.g. regarding the location of richness hotspots. On theother hand, predictions for S-strategists reflect the fact thatthey are more responsive to the presence of urbanised areas(or ‘removal areas’; Table 6), particularly for the area abovethe first quartile (Fig. 3h).

Discussion

Primary and secondary environmental determinantsof alien species richness

Climate was the prevailing determinant of alien speciesrichness in our study area, representing a strong primarygradient that masks the effects of other variables at theregional scale. This agrees with other reports of theprevailing influence of climate, particularly frost and lowtemperatures, on alien invasion (Walther 2002, Waltheret al. 2007). The effects of secondary gradients wereidentifiable only when the area above the second quartileof the climatic predictions was tested. When this morespecies-rich and more homogeneous area was sub-sampled,all seven specific hypotheses were selected, with thoserelated to climate, fire regime and geology being the bestsupported. Our general hypothesis that alien speciesrichness is explained by multiple predictor types is thereforeonly partially supported given that effects of secondarygradients emerged only in the sub-sampled areas withhighest predicted species richness.

Our multi-type controls hypothesis that combinations offactors control alien species richness, was mostly supportedwhen the area above the second quartile was tested (Table6). The second part of the hypothesis, that benignenvironmental conditions prevail over environmental het-erogeneity and density of dispersal corridors as determi-nants of total species richness (Riffell and Gutzwiller 1996,Williams et al. 2005, Dufour et al. 2006), was fullysupported for all three test areas. Becuase this specifichypothesis is the only one including climatic predictors, this

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further confirmed the dominant effects of climate as theprimary regional environmental gradient related to alienspecies richness.

The strong influence of climate on alien invasion hasbeen frequently reported and has received much attentiondue to its implications for climate change ecology (Pinoet al. 2005, Walther et al. 2007, Godoy et al. 2008).Moreover, other variables such as land cover or land-use areknown to be correlated with climate at different scales(Thuiller et al. 2004, Randin et al. 2009). In our study area,

extreme climatic conditions (namely low winter tempera-tures in mountains) seem to inhibit alien species richness,since most alien plant species that are invasive in the regionare frost-sensitive due to their sub-tropical and/or lowlandorigins. For this reason, mountain landscapes in the area aretoday mostly devoid of alien invaders. However, they maybecome prone to invasion if climate warming reduces thenumber of frost days in the future, particularly if landscapecomposition also changes (driven by land-use change)towards facilitating invasions. Because most mountains

Figure 3. Spatial predictions from average models for the four response variables (horizontally: (a) to (c)�species richness; (d�f)�competitor species richness; (g�i)�stress tolerant species richness; (j�i)�ruderal species richness) in the three test areas obtained from thenested gradient approach (vertically: (a), (d), (g) and (j)� full area; (b), (e), (h); (k)�area above the first quartile and (c), (f), (i) and (l)�area above second quartile).

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within our study area are included in nature reserves(including the Peneda-Geres National Park), our resultshave evident implications for conservation planning inorder to prevent new invasions in areas with the greatestconservation value.

Sub-sampling our study area based on areas of highestpredicted species richness eliminated mountain areas, whichare, as discussed above, mostly devoid of alien invaders dueto cold climate conditions and isolation from othermountain ranges by wide lowland areas (Pauchard et al.2009). This allowed us to focus on areas of greaterconservation and management concerns. In these areas, alarger diversity of alien species, potentially occurring in ahigher diversity of habitat types, raises more complexchallenges regarding not only species control and eradica-tion but also landscape management aimed at reducing newinvasion likelihoods. For these areas, in the predictionsabove the first quartile, climate was joined by landscapecomposition as a major secondary gradient, supportingprevious reports on the importance of landscape traits indetermining alien invasion (Pino et al. 2005, Chytry et al.2008). A large number of gradients emerged only for thearea above the second quartile, confirming the additionalimportance of secondary controls, such as geology (Roseand Hermanutz 2004), fire regime (Keeley et al. 2005),regional dispersal corridors (Pysek and Prach 1993,Pauchard and Shea 2006), and topography (Dufour et al.2006) in explaining alien invasions.

Responses of plant strategies to environmentaldeterminants of landscape invasibility

Functional traits of individual species act as controls ofcommunity assembly because they influence recruitmentfrom the regional species pool. Species richness for distinctplant functional types has been shown to respond differ-ently when modelled against a common set of environ-mental gradients (Steinmann et al. 2009). We confirmedthis for alien invaders by showing that all three tested plantstrategies are explained by multiple predictor types, and thatdifferent sets of predictors are relevant for distinct strategies.When the variation of the dominant primary gradient(climate) was narrowed, all models were selected for thethree strategies, with a particular emphasis on geology (C-strategists), topography (C- and S-strategists), and fireregime (S- and R-strategists).

The prevailing role played by climate for all threestrategies supported our working hypothesis of single-typecontrols for C- and S-strategists but contradicted it for R-strategists, for which a prevailing role of climate was a priorinot expected but observed. This may be related to the factthat most ruderal species in the study area have tropical andsubtropical origins. Also, the CR secondary strategy is mostcommon among species with R traits in the area; becausethese species are capable of colonising a wider local diversityof habitats than pure R-strategists, they are less dependenton disturbance regimes and may thus become moreresponsive to climate (Pysek et al. 1995). This prevalenceof climate as a control for all three strategies was furtherconfirmed by the dominant role played by benign environ-

mental conditions among multi-type controls. Landscapecomposition was also important for S- and R-strategists,given these species tend to occur in specific habitats, eitherrelated to regular disturbance (e.g. crop fields; R-strategists)or characterised by chronic stress (e.g. rocky environments;S-strategists; Pysek et al. 1995). For C-strategists, environ-mental heterogeneity and benign environmental conditionswere the most important controls, confirming the ability ofC-strategists to occur in a wide range of habitats if nolimiting factors constrain primary production (Pysek et al.1995). In the more homogeneous test area above the secondquartile, variations in benign conditions and in the presenceof dispersal corridors were the most important controls forS-strategists, whereas R-strategists mostly responded tobenign environmental conditions (prevalent in lowlandswith higher values of primary production) and were onlymarginally influenced by environmental heterogeneity andavailability of dispersal corridors.

Human disturbance, land-use intensity, and alieninvasion

Human disturbance has been reported as a major determi-nant of alien invasion (Le Maitre et al. 2004, Brooks 2007).In our study, land-use intensity was an important determi-nant of landscape invasibility when the variation of theprimary climatic gradient was narrowed (Table 6). Modelsrepresenting removal areas and conservation areas fit thedata better than the model representing intermediate land-use intensity, showing that alien species richness, regardlessof plant strategy, responds more to extreme land manage-ment regimes than to variations in the area characterised byintermediate human disturbance. This may be explained bythe fact that intermediate disturbance regimes favour nativespecies richness (Huston 1994), with fewer niches becom-ing thus available for alien invaders (‘biotic resistance’;Theoharides and Dukes 2007). Overall, the highest alienspecies richness values coincide with areas subject toremoval uses, suggesting that land-use intensification facil-itates alien invasion and highlighting the importance ofurban areas and other heavily managed habitats inpromoting alien species invasions (Brooks 2007).

Alien plant strategies exhibited a relationship to land-useintensity when climatic variation was narrowed through thenested sub-sampling approach. Alien species richness for allthree plant strategies was controlled by the existence ofconservation areas, but S- and R-strategists responded morestrongly to heavily managed areas (here represented byurban areas; Pysek et al. 1995). Positive effects of land-useintensity on alien species richness were found for all threeplant strategies. In fact, the areas of highest species richnessfor all three strategies tend to coincide with removal areas,where human disturbance often causes the decline or evenextinction of native species, with alien invaders capitalisingon the newly available resources (Le Maitre et al. 2004). Atthe other extreme, conservation areas usually host feweralien species, independently of strategy type, which may bedue to ecosystem stability related to infrequent disturbance(Pokorny et al. 2005).

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Limitations and further prospects

Interactions among variables have been a major topic ofdiscussion in ecological modelling (Austin 2007), but theycan be difficult to implement in a multi-model inferenceframework. Only interactions among variables included in agiven model can be evaluated, which could have con-sequences on the support provided to models sinceinteractions may occur between variables that are nevershared in a same model. This would create confoundingeffects difficult to handle when testing among competinghypotheses. We argue that, for testing our hypotheses,interactions are not formally needed, but could improve thefit of some models and the support of some hypotheses. Yet,the trends observed are real and the exclusion of interactionsdoes not diminish their importance. Nonetheless, thedevelopment of an adequate framework for integratingthem should be a research priority in the near future.

Testing spatial autocorrelation in model residuals couldalso reveal additional insights (Austin 2007, Dormann et al.2007). To be addressed properly, spatial autocorrelationanalyses should be conducted for all competing models,which could increase the complexity of multi-modelanalyses and hypotheses testing. In addition, such testswould rather address quite different hypotheses and shouldthus support future developments of the frameworkpresented here.

A third issue for future development of the framework ismodel validation. Cross validation could be a validalternative to using information-theoretic approaches buthas three major disadvantages: first, in algorithm compar-ison tests (Burnham and Anderson 2002), it was concludedthat AICc was selecting the most parsimonious model whencompared to cross validation; second, when using eitherlarger sample sizes or large number of variables, the cross-validation approach becomes much more computer-intensive than the information-theoretic approach; andthird, the cross validation approach does not allow framinga hypothesis testing framework as MMI does (i.e. there isno need to relate groups of variables to hypotheses in crossvalidation, as we did here). Nonetheless, in future develop-ments AICc weights may be complemented with cross-validated or (rather) bootstrapped goodness-of-fit measures(e.g. D2 or AUC) of each competing model, to assess itsstability to data resampling and associated uncertainty.

Conclusions

Our results confirm that in our study area: 1) climate is themain determinant of alien invasions, and can thus beconsidered a primary environmental gradient determininglandscape invasibility. Effects of secondary gradients aredetected only when the study area is sub-sampled accordingto predictions based on primary gradients. 2) Oncesecondary environmental gradients are revealed, multipletypes of predictors are involved in determining patterns ofalien species richness and thus landscape invasibility. Sometypes of predictors (e.g. landscape composition, topogra-phy, fire regime) prevail over others in explaining theseobserved patterns. 3) Alien species richness responds most

to extreme land management regimes, regardless of plantstrategy, suggesting that intermediate disturbance maycontrol alien invasion by promoting native species richnessand thus biotic resistance to invasion processes. Overall,land-use intensification facilitates alien invasion, whereasconservation areas usually host few alien species, high-lighting the importance of ecosystem stability in preventingalien invasions. 4) Distinct alien plant strategies showdifferent responses to environmental gradients, particularlywhen the variation in the dominant primary gradient isnarrowed by sub-sampling the study area. The differentialresponses of distinct plant strategies to environmentalpredictors in different subareas suggest using distinctcontrol and eradication approaches for different areas andalien plant groups.

Acknowledgements � This study was financially supported by FCT(Portuguese Science Foundation) through PhD grant SFRH/BD/40668/2007 to JV. AG received support from the Swiss NCCRfor ‘Plant survival in natural and agroecological landscapes’(<www.unine.ch/nccr>). This paper is a contribution of the 3rdInternational Riederalp Workshop: ‘The Utility of Species Dis-tribution Models as Tools for Assessing Impacts of GlobalChange’. We would like to thank Nicklaus Zimmermann,Catherine Graham, Peter Pearman, Thomas Edwards and Jens-Christian Svenning, guest editors of this special issue in Ecogra-phy.

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Supplementary material (Appendix E6380 at <www.oikos.ekol.lu.se/appendix>. Appendix 1�2.

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