roux et al 2013 ecography

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Geomorphological disturbance is necessary for predicting ne-scalespecies distributions

Peter C. le Roux , Risto Virtanen and Miska Luoto

P. C. le Roux (peter.leroux@helsinki.) and M. Luoto, Dept of Geosciences and Geography, Univ. of Helsinki, FI-00014 Helsinki, Finland.– R. Virtanen, Dept of Biology, Univ. of Oulu, FI-90014 Oulu, Finland.

Disturbances related to geomorphological processes are frequent, widespread and often intense at high latitudes and alti-tudes, affecting the ne-scale distribution of many plant species. While the inclusion of physical disturbances into modelsof species geographic ranges is widely recommended, no studies have yet tested the utility of eld-quantied geomorpho-logical disturbances for terrestrial species distribution modelling. Here we apply generalized additive models and boostedregression trees to examine if the explicit inclusion of terrestrial and uvial geomorphological variables alters species dis-tribution models for 154 vascular plant, bryophyte and lichen species in north European mountain tundra. Te inclu-sion of these disturbances signicantly improved both the explanatory and predictive power of distribution models, withconsistent results for all three species groups. Spatial distribution predictions changed considerably for some species afterthe inclusion of disturbance variables, with uvial disturbances generating strongly linear features for species inuenced byerosion or sediment deposition. As a consequence, models incorporating geomorphological variables produced markedlymore rened distribution maps than simpler models. Predictions of species distributions will thus benet strongly fromthe inclusion of ne-scale geomorphological variables, particularly in areas of active earth surface processes, enabling moreaccurate forecasting of future species ranges under changing conditions.

A wide variety of community properties are inuencedby disturbance events, including species richness (Connell1978, Huston 1994), abundance (Sousa 1984) and ecosys-tem productivity and functioning (Cardinale and Palmer2002, Kreyling et al. 2008). Disturbances can stronglyalter the spatial and temporal heterogeneity of conditions within landscapes, favouring some species and excludingothers (Huston 1994). As a result, explicitly includingvariables that reect disturbance processes in species distri-bution models should improve model performance (Guisanet al. 1998, Dirnböck and Dullinger 2004, Guisan andTuiller 2005), and thus make a step towards the modellingof realistic species distributions. However, despite the clear

link between disturbance regimes and spatial variation inspecies abundance and occurrence, few studies incorporatene-scale disturbances of any kind when modelling thedistribution of individual species (Austin and Van Niel 2011).

Disturbance regimes at high latitudes and altitudes maybe dominated by earth surface (i.e. geomorphological)processes, which are known to strongly affect the ecologicalconditions in these environments (Sigafoos 1952, Körner2003, Nagy and Grabherr 2009) and have been hypothesizedto be important determinants of species distributions(Randin et al. 2009). Water- (uvial erosion and sedimen-tation), frost- (cryoturbation and soliuction), snow-(nivation) and wind-related (deation) processes can reduce

the biomass of individual plants and the size of populationsand alter the competitive environment (Sigafoos 1952,Grime 1977, Nagy and Grabherr 2009). Tese geomorpho-logical processes also affect a diversity of environmentalcharacteristics, including soil stability, chemistry and nutri-ent availability (Jonasson and Sköld 1983, Jonasson 1986,Kreyling et al. 2008), and temperature and moisture regimes(Körner 2003, Walker et al. 2004, Malanson et al. 2012). As a result, these processes may exert strong control overplant performance, as well as the nature of interactionsbetween species (Grime 1979). For example, uvial pro-cesses, including erosion, sedimentation and ooding,can cause partial or total destruction of vegetation (Hupp

1988, Wohl 2000). Indeed, at high latitudes and altitudesthe landforms associated with these geomorphologicalprocesses essentially characterise the micro- and mesoscaleenvironmental heterogeneity of landscapes (Dahl 1956,Hupp and Osterkamp 1996), and therefore, can be expectedto be drivers of ne-scale species distributions.

Several studies have examined the inuence of bioticdisturbances (e.g. herbivory, digging, trampling) and infre-quent, large-scale disturbances (e.g. res, storms, ooding)on species geographic distributions (e.g. Moretti et al.2006), but the inuence of geomorphological processeson species distribution patterns has received much lessattention. Te study by Randin et al. (2009) was the rst to

Ecography 36: 800–808, 2013doi: 10.1111/j.1600-0587.2012.07922.x

© 2013 Te Authors. Ecography © 2013 Nordic Society OikosSubject Editor: Catherine Graham. Accepted 27 November 2012


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attempt to explicitly include geomorphological processesin species distribution modelling. However, that study didnot show improvement in models’ predictive power, proba-bly, as highlighted by the authors, due the semi-quantitativedisturbance variable being insuffi ciently precise and toocoarse-scaled to be truly ecologically meaningful. Similarly,Mellert et al. (2011) developed distribution models incor-porating estimates of uvial activity based on soil maps,but found this variable to be largely redundant for modelperformance. Terefore, the potential for ne-scale eld-quantied geomorphological variables to contribute to spe-cies distribution models remains poorly understood.

Tere are broad generalities in species responses to distur-bances (and environmental factors in general; Grime 1979)but only limited consistency in their specic reactions tochanges in environmental conditions (Chapin and Shaver1985, Dormann and Woodin 2002). Geomorphologicalfactors are unlikely to be an exception to this trendand, therefore, markedly idiosyncratic responses may beexpected. It is possible, for example, that frost-heave maypreclude some species from habitats (Pérez 1987), butsimultaneously be essential for the occurrence of others(Nyléhn and otland 1999). Tese potentially importantindividualistic relationships between species distributionsand geomorphological processes remain essentially uninves-tigated. Tus, it is necessary to examine the inuence ofgeomorphological variables on a large and representativeset of species if community-level responses are to be esti-mated. Furthermore, modelling the distribution of multiplespecies offers the possibility of identifying general patternsthat can be applied to taxonomical, biogeographic or func-tional categories (Guisan and Zimmermann 2000).

Here we examine if the incorporation of eld-measuredgeomorphological variables can signicantly improve theaccuracy and predictive power of species distribution models

for 154 bryophytes, lichens and vascular plant species innorth-western Finland and Norway. Tis is done by applyinggeneralized additive models and boosted regression trees toa unique dataset comprising ne-scale vegetation surveys ofvascular plants, bryophyte and lichen species and eld-based quantication of active geomorphological processes,recorded in 1080 survey plots spanning a wide range ofenvironmental conditions characteristic of mountain sys-tems in high northern latitudes. Additionally, the impactof geomorphological disturbance on species richness pat-terns is examined by summing individual species distri-bution predictions from models with and without thedisturbance variables included.

Methods

Data collection

Data were collected from 270 sites in north-westernFinland and Norway, along 18 altitudinal transects in thesub-continental northern Scandes (ca 69° N, 20° 50′ E).Four 1 m2 plots, located 5 m from the centre of each site, were surveyed at each site, resulting in a total of 1080plots for analysis. Te cover and identity of all vascularplants, bryophytes and lichens in each plot were recorded

(see Virtanen et al. 2010, le Roux et al. in press for moredetails). Species were classied into two broad categoriesby biogeographic distribution: arctic-alpine (main distribu-tion in cold arctic-alpine areas) or boreal (having a broader,often Holarctic, distribution).

Environmental variables for modeling species distribu-tions were selected from three different groups of commonlyused variables (macro-climate, soil resources and solarradiation), after a priori exclusion of strongly collinear vari-ables. Tree climate variables were calculated for each plot:mean temperature of the hottest month (July), meantemperature of the coldest month (January), and totalannual precipitation. Climatic data were obtained from theFinnish Meteorological Inst. data set (Venäläinen andHeikinheimo 2002), with average values from 1971 to2000 downscaled from the original 10-km grids followingthe methodology of Vajda and Venäläinen (2003; linearregression using latitude, longitude and altitude terms).Tis approach accounts for adiabatic lapse and hasperformed well in this region (Sormunen et al. 2011). Asall three downscaled climate variables were strongly corre-lated (|r| 0.75, p 0.001), only the mean temperatureof the hottest month (hereafter ‘emp July ’) was used in sub-sequent analyses.

Soil quality was estimated from the proportion ofcalcareous (nutrient-rich) and silicaceous (nutrient-poor)bedrock at plots. Bedrock data was extracted from Korsmanet al. (1997) at a resolution of 100 m and interpolatedusing the ArcGIS interpolation tool (ESRI 1991). Analysesacross a subset of the plots show that soil quality is signi-cantly correlated with soil nutrient content (Vuollet andLuoto unpubl.). Relative soil moisture at each plot wasestimated by calculating the topographic wetness index( WI; Beven and Kirkby 1979) from a 10 m resolutiondigital elevation model (DEM; National Land Survey of

Finland) using the ArcView Spatial Analyst extension (ESRI1991). WI was calculated as:

WI ln (upslope catchment area per unit widthorthogonal to the ow direction tan[slope angle])

with slope angle measured in radians. Potential solar radia-tion (Mj/cm2 /a; ‘Radiation’) for each plot was calculatedfrom the DEM using Solar Analyst extension in ArcView(ESRI 1991, McCune and Keon 2002). Tis value reectsthe maximum potential radiation, based on slope aspectand angle, assuming clear skies. Plot slope and soil moisturebalance (i.e. the difference between precipitation andpotential evapotranspiration; calculated following Magginiet al. 2006) were both considered as potential explanatoryvariables, but were not included in analyses due to beingstrong correlated with WI (r 0.89, p 0.001) andclimate variables (|r| 0.81, p 0.001) respectively.

Geomorphological activity was estimated for each siteby recording the cover of two sets of variables at each site(10 10 m). Te cover of active terrestrial (‘Disturbterr ’:including frost-related cryoturbation and soliuction, and wind-related nivation and deation) and uvial (‘Disturbuv ’:uvial erosion and deposition) geomorphological processes was estimated in situ during the summers of 2009–2011 aspercentage cover following the methodology of Hjort and


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Luoto (2009, 2011) and Virtanen et al. (2010). All surveys were performed by the same geomorphologist to excludevariation resulting from observer differences, with theinvestigator focusing only on geomorphological variablesto ensure independence of the botanical and geomorpho-logical data. Te activity of features was dened basedon observations of topsoil material (e.g. frost heaving andcracking, mass wasting, soil displacement, as well as uvialerosion and sedimentation). Tis geomorphological distur-bance measure thus combines many physical disturbancefactors potentially causing biomass loss, excluding thebiotic disturbances caused by grazing animals (Virtanenet al. 2010). Disturbance values were log-transformedprior to analysis to reect the non-linear relationshipbetween the cover and the intensity of geomorphologicalactivity (i.e. a doubling of the cover of a geomorphologicaldisturbance was assumed to result in more than a two-foldincrease in its effect on the vegetation). In this system we consider geomorphological processes to be chiey distur-bances (i.e. processes removing biomass; sensu Grime 1979), with any additional effects on resource availability assumedto have only a secondary impact relevant to a subset ofspecies that tolerate disturbance. We did not investigate resas an additional physical disturbance in this study as wildres are very infrequent in the northern European mountaintundra.

Statistical analysis

Since different species distribution models may potentiallyproduce different predictions (Franklin 2009), two alter-native state-of-the-art methods were used: generalized addi-tive models (GAMs) and boosted regression trees (BR s).Both techniques are data-driven, capable of modeling

non-linear functions, and widely used to model species dis-tributions (Elith et al. 2006, Franklin 2009). Te originaldataset was randomly split into a calibration (i.e. training;75% of records) and a validation (i.e. testing; remaining25% of records) dataset. Only species with at least 15records in the calibration dataset and 7 records in the valida-tion dataset were included in analyses (limiting analyses toonly more abundant species did not give different results).

GAMs are a non-parametric extension of generalizedlinear models that use smoothers to estimate the relation-ship between response and predictor variables (Yee andMitchell 1991). GAMs were tted in R statistical software(R Development Core eam), using the mgcv package(Wood 2011) which calculates the extent to which thedegrees of smoothness for each smoother can be reducedduring model tting without signicantly lowering modelt. Tese models were tted assuming a quasibinomialdistribution of errors (applying a logit link function), settingthe initial degrees of smoothness for each univariate termat four.

BR is a machine learning method that estimates therelationship between a response variable and its predictors without a priori specication of a data model (De’ath2007, Elith et al. 2008). Tis technique combines largenumbers of simple tree-based models to form a nal modeloptimized for prediction, using cross-validation for model

building. BR s can model complex functions and auto-matically incorporate interactions between predictors. AsGAMs and BR s gave similar results, only those from theGAM analyses are presented here (detailed BR methodo-logy and results are presented in Supplementary material Appendix 1, able A1 and Fig. A1, A2).

o test if the inclusion of the two disturbance variablessignicantly improved the t of species distribution models, we followed the methodology of Zimmermann et al.(2009) and le Roux et al. (in press), rst modeling each spe-cies occurrence as a function of simple topography–soil–climate variables:

Presence/Absence Soil moisture Radiation Soil quality emp July

…(simple model)

Next, we incorporated terrestrial and uvial geomorpho-logical disturbances into the model

Presence/Absence Soil moisture Radiation Soil quality emp July Disturbterr Disturbuv

…(full model)

Simple and full models for each species were tted to thecalibration dataset and an ANOVA (implementing a Chi2 test) was used to test if the full model provided a signicantlybetter t to the calibration data than the simple model.Te explanatory power of both models was assessed bycomparing the adjusted R2 for all relationships (i.e. theproportion of variance explained, after accounting for thedifferent numbers of explanatory variables in simple andfull models). Te relative importance of predictor variables was assessed from calibration models from each variable’sdrop contribution (i.e. change in deviance associated with exclusion of a given variable from a model containingall the other variables). Variables’ contributions werescaled so as to sum to 100, with higher numbers indicatingstronger inuence on the response.

Te predictive power of the models was determinedby testing the accuracy of predictions made for the valida-tion dataset (i.e. data that was not used in model develop-ment), calculating the area under the curve of a receiveroperating characteristic plot (AUC; Fielding and Bell1997) and the true skill statistic ( SS; Allouche et al.2006). A non-parametric Wilcoxon’s matched pairs test was used to compare if the explanatory power and pre-dictive accuracy of the simple and full models differed

signicantly. Both explanatory accuracy and predictiveability of models are considered, so as to evaluate models’ability to explain local phenomena and to predict to inde-pendent data.

Predicting species ne-scale distribution

o visualize the distribution patterns predicted by eachspecies best-t distribution models (and the resulting pat-terns of species richness), an area of 3.1 5.2 km in thecentre of our study region (covering the Saana and, partially, Jehkats massifs) was divided in 25 25 m cells. A 1 m2


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and validation data sets) having signicantly lower deviancethan equivalent simple models (ANOVA test on GAM mod-els; able 1). Full models also had better explanatory power, with signicantly higher adjusted R2 (mean increase 6%,max. increase 33%; able 1, Supplementary material Appendix 2, able A2). emperature of the warmest month was on average the most important predictor of speciesoccurrence in both simple and full models (Fig. 1a). However, when including terrestrial and uvial disturbance in models

the importance of emp July declined strongly (e.g. averageimportance dropping from 58 to 32%), with Disturbterr becoming the second most important variable. Te impor-tance of the two geomorphological disturbance variablesdiffered strongly between species, although their combinedimportance ranged between 10 and 40% for most species(Fig. 1b). Tus the contribution of the disturbance variables

plot in the centre of each cell was used for all calculationsand predictions. Soil moisture and solar radiation for eachplot was calculated from the 10 m resolution DEM, and soilquality and mean temperature of the hottest month weredownscaled from the available data (following the methodsdescribed above). Te intensity of geomorphological distur-bances were mapped across the prediction area usingthe DEM, false-colour aerial photographs (0.5 m spatialresolution), and extensive eld surveys. From the coverof these active terrestrial and uvial geomorphological pro-cesses the intensity of geomorphological disturbances wasestimated for each plot in the prediction area (followingHjort and Luoto 2009, 2011, Virtanen et al. 2010;Supplementary material Appendix 2, Fig. A3). Best-tsimple and full models were then used to predict the occur-rence of each species across this landscape, with speciesrichness for each cell calculated as the sum of predictedspecies occurrences (repeated for simple and full models).Predicted probabilities of occurrence were converted topresence/absence predictions using the threshold value max-imizing sensitivity specicity (Liu et al. 2005).

Results

Models including the two geomorphological disturbancevariables provided a better t to calibration data than simpletopography–soil–climate models, with 81% of full models(124 of 154 species with adequate records in the calibration

Figure 1. Te relative importance of explanatory variables as calculated from generalized additive models: (a) variable importance for thesimple (‘ D’: topography–soil–climate variables) and full (‘ D’: topography–soil–climate–disturbance) models, and (b) the distributionof the combined importance of terrestrial and uvial disturbances for all species. In (a), empty circles indicate the maximum importancefor each variable across all 154 species modeled and standard error bars are omitted as they are too small to display clearly (all SE 2).Rad. solar radiation, Moist. soil moisture, Qual. soil quality, emp. mean July temperature, Fluv. uvial disturbance, err. terrestrial disturbance.

Table 1. Results from Wilcoxon ’s matched pairs tests, testing ifthe explanatory accuracy and predictive power of full models(including geomorphological variables) differs from that of simplemodels (containing topography, soil and climate variables). Explan-atory accuracy is measured as adjusted R 2 , and predictive poweras the area under the curve of a receiver operating characteristic plot(AUC) and the true skill statistic (TSS). n 154.

Simple model Full model WilcoxonVMetric (mean SE) (mean SE) p

Adjusted R2 19.5 1.0 25.7 1.1 27 0.001AUC 82.5 0.7 84.5 0.6 1888 0.001TSS 51.0 1.3 54.1 1.3 3461 0.001


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disturbance, with more mixed responses from lichen species.Comparing the response curves of arctic-alpine and borealspecies showed the former group to be positively affectedby terrestrial disturbance, while the latter species wereclearly negatively impacted by higher cover of terrestrialgeomorphological disturbances (full results and additionalanalysis in Supplementary material Appendix 2, able A4).

Across our interpolation extent the clearest difference tothe predicted distributions between simple and full modelsis the addition of linear elements into species occurrencepatterns, reecting some species strong (positive or negative)responses to uvial disturbance (see for exampleSaussureaalpina in Fig. 3). Changes in species occurrence patterns dueto terrestrial disturbances were more evenly distributedthroughout the landscape, and thus not as noticeable.Predictions of species richness (derived by summing predic-tions for individual species) were similar between simpleand full models, although the effects of uvial disturbance were clearly visible, having a positive effect on the richness ofvascular plants, but a negative effect on the number ofspecies of bryophytes and lichens (Supplementary material Appendix 2, Fig. A4).

Discussion

Te results from this study clearly support previoushypotheses (Dirnböck and Dullinger 2004, Randin et al.2009) that the inclusion of geomorphological processesimproves predictions of species distributions. Tis is inagreement with the perceived role of geomorphology ininuencing plant ecology in high latitude and altitudeecosystems (Körner 2003, Nagy and Grabherr 2009,Malanson et al. 2012). Our results show that incorporatingmeasurements of terrestrial and uvial geomorphological

processes strengthens species distribution models’ explana-tory accuracy (i.e. ability to model the observed data)and predictive power (i.e. ability to extrapolate results toindependent datasets). Tese ndings were consistentfor vascular plants, bryophytes and mosses, despite funda-mental differences between these groups of species (Molauand Alatalo 1998), highlighting that the inclusion ofgeomorphological variables benets species distributionsmodels across multiple taxa. Tus, geomorphological vari-ables have non-trivial effects for the predictions of the cur-rent species distributions, and these should be carefullyconsidered in the context of constructing climate changeimpact models.

Geomorphological variables exhibited considerableimportance for the majority of species, and very highimportance for a subset of the species (consistent withRandin et al. 2009). Terefore, in agreement with otherstudies examining the inuences of disturbances ( uckeret al. 2012), species showed a wide range of responses to thesame geomorphological disturbances. Consistent improve-ments in model performance across all species grouping(vascular plants–bryophytes–lichens, and boreal–arctic-alpine) suggests that inclusion of geomorphological vari-ables can improve species distribution models for differenttaxa. However, species response curves reveal that thesegroups differ in their dominant responses to uvial and

varied considerably between species, but was strong enoughon average to signicantly improve model explanatorypower.

Model predictive performance improved with theinclusion of the two disturbance variables, with small butsignicant improvement in AUC values for full models(mean increase 0.02, max. increase 0.23; able 1).Mean SS values were also signicantly higher formodels incorporating the two disturbance variables (meanincrease 0.03, max. increase 0.47; able 1). Overall,most GAM models had higher AUC (77% of models) and

SS values (66%) for full models than simple topography–soil–climate models. Analysing species subsets based ontaxon (bryophyte, lichen or vascular species) or biogeographicdistribution (arctic-alpine or boreal species) gave similarresults to the initial analysis of all species (Supplementarymaterial Appendix 2, able A3).

Response curves for the disturbance variables variedgreatly between species (i.e. from strongly positive to stronglynegative impacts on species probabilities of occurrence; seee.g. Saussurea alpina in Fig. 2; all data in Supplementarymaterial Appendix 2, able A2). Vascular plant speciesresponse curves for uvial disturbance were most commonlypositive, with lichens predominately responding negativelyto increasing uvial disturbance, and bryophytes exhibit-ing an intermediate response (Supplementary material Appendix 2, able A4). Vascular plants and bryophytesgenerally responded positively to increasing terrestrial

Figure 2. Response curves forSaussurea alpina as estimated bygeneralized additive models. Solid lines indicate the responsecurves for the simple model, with dashed lines indicating theresponse curves from the full models. emp. July mean July tem-perature, Dist. uv. uvial disturbance, Dist. terr. terrestrialdisturbance.


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Figure 3. Modeled distribution ofSaussurea alpina in a 3.1 5.2 km area in the centre of our study region, based on the predictions of(a) simple and (b) full generalized additive models. Grey indicates predicted areas of occurrence, and black indicates lakes and pondsLatitude and longitude are indicated in the Finnish coordinate system with contours representing altitude (m a.s.l.).

terrestrial geomorphological disturbances. For example,arctic-alpine species showed more strongly positive res-ponses to high levels of geomorphological disturbance thanspecies with boreal distributions. Tis agrees with previousndings that geomorphological disturbances are necessaryfor the occurrence of some species outside their typicalranges (Jonasson and Sköld 1983) and suggests that earthsurface processes may be a prerequisite for many of the smallarctic-alpine species (e.g.Saxifraga foliolosa , Ranunculusnivalis and Cardamine bellidifolia ; Hämet-Ahti et al.1998). By contrast, boreal species which are known to bedisturbance-intolerant and restricted to relatively undis-turbed sites, including shrubs and dwarf shrubs (e.g.Betulanana ; Jonasson 1986, Oksanen and Virtanen 1995), gener-ally exhibited a negative response to geomorphologicaldisturbance. As our study site encompasses the ecotonebetween the arctic-alpine and boreal biomes it is interestinghow these two co-occurring species groups differ stronglyin response to geomorphological disturbances, suggestingimportant differences in their relative sensitivity to theseprocesses. Tus, while some species are clearly more stronglyaffected by geomorphological disturbances than others, weare presently unable to a priori predict species sensitivity tothese processes, and therefore these geomorphological vari-ables should be incorporated whenever possible in ne-scalespecies distribution models.

Te geographic distribution projections includinggeomorphological variables produced much more detailed

distribution maps than traditional models with onlytopography–soil– climate variables. For instance, the inclu-sion of these disturbance processes markedly rened theareas predicted to be suitable for many species. If notincluded, model predictions grossly exaggerated or underes-timated some species potential ranges (e.g. Fig. 3), empha-sizing the need for the inclusion of ecologically meaningfuland suffi ciently ne-scale variables for realistic species distri-bution models (Sormunen et al. 2011). However, we believeit is still necessary to test the robustness of these results with data from other environments to fully understand thegenerality of these ndings. In agreement with Randinet al. (2009), we also observed changes in the connectivityof suitable habitat when disturbance variables wereincluded, chiey due to greater ne-scale patchiness. Dueto the strong contagion of cells containing uvial distur-bance, it appears that habitats with uvial inuencehave clear potential to act as migration corridors for thosespecies tolerant of disturbance and responsive to warmingtemperatures.

Geomorphological processes frequently generate lowintensity small-scale disturbances. Such geomorphologicaldisturbances maintain similar conditions from year to year, with the spatial positions of these processes being relativelystable over decades (French 2007). Te scale at which thesedisturbances operate remains unknown but it is likely thattheir incorporation at a resolution of 1 m is adequate formany purposes. Even though species distributions within


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In conclusion, this study demonstrates that the explicitincorporation of geomorphological processes improvesspecies distribution models’ explanatory and predictivepower in a high latitude environment. Based on the relativeimportance of geomorphological variables, these processesare on average as important (if not more important) thantopography, climate and soil variables, highlighting thesignicance of disturbance measures in species distributionmodels. Our results could thus be a step towards forecastingmore accurate and realistic species distributions and rangeshifts.

Acknowledgements – We acknowledge funding from the Academyof Finland (project no. 1140873).

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disturbed systems are essentially unpredictable at very nescales (Levin 1992), the incorporation of geomorphologicaldisturbances at this scale was useful. Indeed, the scalesensitivity of disturbance for species distributions remainsto be studied further. Furthermore, in systems characterizedby infrequent and large-scale disturbances (e.g. avalanchesor landslides) more complex modelling approaches maybe necessary to capture the successional dynamics initiatedby disturbance events (Franklin 2010). Species distributionmodels would, for example, at least require the inclusionof time since the disturbance as a covariate (Moretti et al.2006). Recent studies have suggested that biotic distur-bances should also be incorporated into species distributionmodels (Munier et al. 2010), although this too may requirefurther methodological developments. Indeed, the fre-quent low intensity impacts of burrowing, browsingand grazing animals or the infrequent (but often devastat-ing) outbreaks of insects and lemmings can strongly inu-ence high altitude and latitude vegetation (Fox 1985,Ravolainen et al. 2011). However, due to uctuations ofherbivore populations and the patchy occurrence of theireffects, documenting the spatio-temporal variability ofbiotic disturbances has been diffi cult (but see Virtanen et al.2002) and their inclusion in species distribution modellingremains a challenge.

Due to on-going climate change, physical disturbanceregimes are predicted to change rapidly in the future asmany disturbance processes have a signicant climateforcing (IPCC 2007, Hjort and Luoto 2009). Although thisconsequence of global climate change is recognized, thereis an urgent need for more comprehensive evaluation ofscenarios of future disturbance regimes because they maystrongly mediate the ecological consequences of climatechange (Walther 2004, Cannone et al. 2007). For example,the response of the forbEuphrasia frigida to warming

will strongly depend on how changing climatic conditionsaffect the distribution of cryoturbation (Nyléhn andotland 1999). With declining frost-related disturbances

forecast for northern Fennoscandia (Fronzek et al. 2010),it is likely that strongly competitive species (includingshrubs) may increase in abundance under the milder dis-turbance regime, with negative consequences for less com-petitive species (Pajunen et al. 2011). Tus communitycomposition may be simultaneously impacted by warmingtemperatures and declining frost-disturbance frequency.Moreover, from a conservation point of view, areas withintense earth surface processes may be more importantthan previously understood since traditional species distri-

bution models have not explicitly accounted for geomor-phological disturbances. It is, therefore, imperative thatspatial and temporal variation in disturbance processes isincorporated into studies of global change. While frost- andsnow-related disturbances are particularly important athigh latitudes and altitudes, in temperate systems a greaterfocus could be placed on disturbances driven by wind,owing water and slope processes. For example, in coastaland riverine systems species distributions may be especiallysensitive to wind and uvial disturbances which can stronglyalter habitat conditions and generate pronounced hetero-geneity in environmental conditions over short distances(Osterkamp and Hupp 2010).


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