Patterns of Plant Species Richness Along EnvironmentalGradients in German North Sea Salt Marshes

Sigrid Suchrow & Martin Stock & Kai Jensen

Received: 26 May 2013 /Revised: 29 January 2014 /Accepted: 24 March 2014# Coastal and Estuarine Research Federation 2014

Abstract In salt marshes, species richness changes alongenvironmental, disturbance and productivity gradientsforming a complex network of abiotic and biotic factors. On2,691 plots along 121 transects, we sampled vegetation alongthe German mainland North Sea coast (13 regions) during1987–1989. Applying regression tree analysis (RTA), we nowused this large data set to analyse variance in species richness(SRich) in relation to 13 explanatory variables varying ondifferent scales. SRich (mean, 4.9 per m2) was significantlycorrelated to most variables. Only six variables were includedin our final model, together explaining 68.5 % of variance inSRich, in hierarchical order: moisture, salinity, evenness, ni-trogen, region and elevation. Predominantly, SRich was lim-ited by environmental heterogeneity (moisture, salinity andnitrogen, each explained approx. 50 % variance). SRichtended to be high on plots exhibiting a combination of lowmoisture, salinity and nitrogen values, with high evenness—and especially high in some regions when plots were lyinghigh in relation to mean high tide. Grazing regimes did notaffect SRich significantly. In conclusion, our model showedthat SRich in the study area was predominantly explained on a

small scale and less along large-scale gradients. RTA provedsuitable to identify the set of variables that mainly explainedvariance in SRich.Our tree model improves the understandingof richness patterns in salt marshes and can be used to predictspecies richness for the study area. Furthermore, our dataprovide a reference to detect richness changes due to, forexample, management changes or sea level rise.

Keywords Ellenberg’s indicator values . Grazingmanagement . HOF-modelling . Regression tree analysis(RTA) . Sea level rise (SLR) . Species diversity

Introduction

Salt marshes are intertidal ecosystems developing at shallowtidal coasts with low wave energy, and regular sedimentationand erosion processes (Adam 1993). These naturally treelesswetlands in the transition between marine and terrestrial eco-systems are among the most productive ecosystems in theworld (Mitsch and Gosselink 2000), providing ecosystemservices such as biodiversity, coastal protection or carbonsequestration (cf. Barbier et al. 2011). In the Wadden Sea areaalong the North Sea coast, salt marshes are found on the leeside of the barrier islands and along the mainland coast wherethey are mostly backed by dikes. Most mainland salt marshesare relatively young and semi-natural owing to land reclama-tion and embankment activities (cf. Dijkema 1983), coastalprotection and land use management since medieval times(Behre 2005). Nevertheless, from a nature conservation pointof view, these marshes are valuable habitats and thus becamepart of the Wadden Sea National Parks and of the UNESCOWorld Heritage Site German-Dutch Wadden Sea. Since theimplementation of the Convention on Biological Diversity bythe United Nations in 1992, monitoring, preserving and re-storing biodiversity is stipulated from all contracting states

Communicated by Bob Christian

Electronic supplementary material The online version of this article(doi:10.1007/s12237-014-9810-9) contains supplementary material,which is available to authorized users.

S. Suchrow (*) :K. JensenBiocentre Klein Flottbek and Botanical Garden, Ohnhorststraße 18,22609 Hamburg, Germanye-mail: Sigrid.Suchrow@uni-hamburg.de

S. Suchrowe-mail: SSuchrow@web.de

M. StockSchleswig-Holstein Agency for Coastal Defence, National Park andMarine Conservation - National Park Authority, Schlossgarten 1,25832 Tönning, Germany

Estuaries and CoastsDOI 10.1007/s12237-014-9810-9

(www.cbd.int). Thus, respective data are asked for byconservationists, wildlife habitat managers and politicians(Sharpe and Baldwin 2009). In salt marshes, biodiversity dataare of increasing interest with respect to changes in manage-ment (cf. Esselink et al. 2009) and accelerated sea level rise(SLR; IPCC 2007) which is considered a threat to biodiversity(e.g. Boorman 1999). Species richness as the number ofspecies per unit area is a straightforward diversity variablefor the purposes of evaluation and monitoring.

In salt marshes, constantly changing and partly extremeconditions are caused by complex multiple ecosystem pro-cesses involving environmental, disturbance and productivitygradients. Within this gradient network, richness, compositionand dominance pattern of the—mainly halophytic—specieschange scale-dependently along coastlines and from sea toland (Adam 1993; Beeftink 1977; Dijkema 1983). On a largescale, changes in salinity along the coast (Engels and Jensen2009), different exposure of a marsh to waves or sedimenta-tion (Houle 2005; Wigand et al. 2003), tidal range (Adam1993), flooding depth depending on distance from the sea(Sánchez et al. 1998), management (Olff and Ritchie 1998)or seed dispersal (Wolters et al. 2005), for example, caninfluence vegetation. On a small scale, variation in micro-topography causes small-scale environmental heterogeneityalong the elevational gradient (e.g. Zedler et al. 1999) espe-cially regarding moisture (frequency and duration of flooding,soil waterlogging) and salinity (Adam 1993; Ranwell 1972)but also regarding nutrient supply (Wigand et al. 2003).Furthermore, grazing as a form of disturbance results in bio-mass reduction and plant damage due to browsing and tram-pling, and in combination with livestock’s specific grazingbehaviour has an effect on species diversity (cf. Jensen 1985).Consequently, when studying species richness patterns alonggradients in salt marshes, large- and small-scale factors shouldbe focused upon and interactions of variables should beconsidered.

With regard to the relation of species richness to individualexplanatory variables, Pausas and Austin (2001) emphasisethe importance of the response shape along a respective gra-dient. According to the theories of Connell (1978), Grime(1973, 1979) and Huston (1979), species richness showsgenerally a hump-shaped response along environmental, dis-turbance and productivity gradients. Thereby, diversity typi-cally decreases towards one end of a considered gradient asenvironmental conditions become more stressful, resource-limiting or disturbance severe. At the intermediate level ofthe considered gradient, more species can cope with prevalentconditions and thus richness peaks. Towards the other end ofthe gradient, mainly resource competition determines speciesrichness.

For linking species richness data to gradients, model-ling of response curves using Huisman-Olff-Fresco models(Huisman et al. 1993) has proven to be successful (e.g.

Peppler-Lisbach and Kleyer 2009; Wesuls et al. 2013) andthus seemed ideal for our study when dealing with singlevariables. Dealing with many variables at once, regressiontree analysis (RTA; Breiman et al. 1984) is well-suited as anexploratory technique to uncover structure in data (Clark andPregibon 1992; Iverson and Prasad 1998). It can handle bothcategorical and continuous explanatory variables (McKenzieand Ryan 1999), even if there might be complex relationshipsbetween the variables and/or the target variable (cf. Iversonand Prasad 1998; Scull et al. 2005; Tyler et al. 2009) and thusis not prone to (spatial) autocorrelation (cf. Andersen et al.2000). Consequently, RTA has been effectively used in manydifferent fields of application (Andersen et al. 2000; Iversonand Prasad 1998; Suchrow et al. 2012a), and we regarded thisapproach appropriate to explain variance in species richness insalt marshes.

In this study, we evaluated for the first time the relationshipbetween species richness (target variable SRich) and a set of13— environmental, disturbance and productivity— explan-atory variables in German salt marshes by applying RTA to alarge data set (2,691 plots recorded with a consistent methodalong the mainland North Sea coast, Schleswig-Holstein; cf.Suchrow et al. 2012b). On this basis, our aim was (1) to checkto what extent SRich was related to each of these explanatoryvariables that varied on different scales and (2) to evaluatewhich explanatory variables in combination contributed main-ly to explain variance in SRich. Since our data characterisespecies richness in the study area in the late 1980s, they give areference for future studies in the study area with respect tochanges in management, to climate change or to SLR, and thegeneral rules encoded in the tree model explaining speciesrichness patterns might be used for prediction.

Study Area and Methods

Study Area

We carried out our study in theWadden Sea salt marshes alongthe German mainland coast (federal state of Schleswig-Holstein; approx. 55°N to 54°N and 8°E to 9°E; Fig. 1). Thetemperate climate in this area is characterised by cool sum-mers (mean temperature in July, approx. 16 °C) and mildwinters (mean temperature in January, 0.5-1.0 °C) and anannual precipitation of approximately 700 mm (maximum inautumn; Landesamt für den Nationalpark Schleswig-Holsteinisches Wattenmeer und Umweltbundesamt 1998).Along the coast, the tidal range varied during our study periodin the late 1980s between approximately 280 cm at the mouthof the Elbe estuary and 180 cm at the Danish border, and up to340 cm in some bights (Deutsches Hydrographisches InstitutHamburg 1989). Water salinity decreased southwards fromapproximately 32 psu at the Danish border to 10 psu in

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summer and from 25 psu to 4 psu in winter but with morebrackish conditions also in some bights (Landesamt für denNationalpark Schleswig-Holsteinisches Wattenmeer undUmweltbundesamt 1998). The mainland salt marshes devel-oped as a result of centuries-long human activities of diking,land reclamation and drainage (e.g. Behre 2005). Hence, theyformed strip-like in front of the sea dikes within a regularsystem of land reclamation fields that are trenched by a regularsystem of drainage ditches between the salt marsh beds(Fig. 2a). Additionally, flats, tidal channels, levees, hum-mocks, pans and small depressions contribute to the charac-teristic micro-topography (cf. Dijkema 1983; Fig. 2b). Attimes of our study, the mainland salt marshes covered approx-imately 5,500 ha (Prokosch and Kempf 1988). For severaldecades in the twentieth century, high-density sheep grazingbetween March and November was predominant in these saltmarshes. This management with a stocking density of approx-imately 10 sheep/ha was still ongoing on approximately 95 %of the marshes until the early 1990s (Prokosch and Kempf1988; Stock et al. 1998); thus, during our study period, onlyfew areas were under moderate grazing (0.75 to 1.5 sheep/ha)or ungrazed. Due to the long grazing history, the vegetationwas mostly dominated by grasses (cf. Stock et al. 1998). Quitefrequently, Spartina anglica, Salicornia europaea agg.,Puccinellia maritima, Festuca rubra, and Elymus athericusoccurred (see Online Resource Electronic supplementary

material (ESM) 1), dominating the vegetation types from thepioneer zone via low to high marsh, with their elevationalranges following a continuum along the elevational gradient(Suchrow and Jensen 2010). Thereby, species order and

Fig. 1 Map of the Wadden Seacoast (modified from Stock et al.2005), a: location of the studyarea, b: salt marsh distribution(grey shading) along the coast ofSchleswig-Holstein; arrows pointto the study regions (Reg_…),and c: location of Transects 107and 108 as examples

Fig. 2 Salt marsh area along the German North Sea coast, developedwithin reclamation fields in front of the dike, trenched by a regular systemof drainage ditches; a: part under high-density sheep grazing, photo by S.Suchrow; b: a sketch of an ungrazed part to give an idea of the recurrentpattern perpendicular to the shoreline (two beds with ditches in between)

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zonation of vegetation types showed some recurrent patternacross the regular foreland marsh system. After the establish-ment of the Wadden Sea National Park of Schleswig-Holsteinin 1985, shortly before our study period, coastal managementactivities were reduced, especially in the seaward parts of thesalt marshes. At many sites, grazing ceased or was changed tomoderate grazing in addition to abandoning the maintenanceof the drainage ditches. All this resulted in—still ongoing—changes of vegetation and landscape since the late 1980s, withspecies poor stands ofE. athericus dominating some areas anddiverse vegetation characterising other areas rich in structureand species (Stock et al. 2005; Wanner et al. 2013).

Field Work

Between 1987 and 1989, we established transects perpendic-ular to the coastline, more or less evenly distributed (1,000–3,000 m apart) between the border to Denmark and the Elbeestuary (see Fig. 1). Each of the 121 transects traversed themarsh from the shoreline (first vascular plants) to the dike.Transect length varied considerably depending on the extentof the salt marsh (on average about 500 m). Along the tran-sects, we positioned 1 m2 plots taking into account the micro-topography in addition to the spectrum of the visually distin-guishable vegetation units, considering changes in speciesspectrum and dominance. Accordingly, few plots were placedin a cluster (range 1–19; average four plots per cluster), mostlynext to one another perpendicular to the ditches to capture thesmall scale vegetation changes across the ditches and marshbeds. To consider the vegetation changes from land to sea, plotclusters were placed along the transect, mostly at irregularintervals due to the patchiness and zonation of the vegetation,respectively. Consequently, plot design within the clusters (forsketch see Wanner et al. 2013) and number of plots pertransect differed between transects. In total, our final data setcomprised 2,691 plots (cf. Suchrow and Jensen 2010).

In each plot, we recorded all vascular plant taxa. If—in rarecases—not every individual could be identified beyond genus(e.g. Salicornia spp.), we aggregated to groups. For somespecies, subgenera or ecotypes could not be differentiated inany case from other subgenera or ecotypes in the field and thuswere aggregated on the species level. Furthermore, somespecies occurred mostly as a single subspecies or variety(e.g. F. rubra ssp. litoralis, Agrostis stolonifera var.maritima).Hereafter, we refer to all taxa as “species”. Within the plots,we visually estimated the total percent cover of the vegetationas well as the percent cover of each species (1–100 % coveras integer values; cover values <1 % further differentiated;for descriptive statistics, see Online Resource ESM 1).Furthermore, in each plot, we categorised the bare patchesdepending on whether there were additionally some consider-ably larger gaps not covered with vegetation, and we roughlyclassified the height of the vegetation. All estimationwas done

by the first author and thus consistently across the data set. Allvegetation data were processed with vEGplan 1.9c (Grandt2007). Taxonomy and nomenclature follows Wisskirchen andHaeupler (1998). The list of all species is presented in OnlineResource ESM 1.

Levelling was done in autumn/winter following vegetationstudies. We levelled the elevation of all plots (using an opticallevelling instrument Pentax AL M5c; 2.0 mm accuracy perkilometer double-run levelling) in relation to nearby bench-marks of the vertical control survey net. The measured valueswere related to the German height reference system. Makinguse of mean high tide data (MHT) from automatically record-ing tide gauges along the coast, we interpolated MHT valuesfor each transect. Using these values, we then converted theabsolute elevation values of each plot into the correspondingrelative elevation in relation to MHT. All elevation data wereprocessed with vEGplan 1.9c (Grandt 2007). For a detaileddescription of the calculation, see Suchrow and Jensen (2010).

Variables

The number of species per plot accounted for our targetvariable SRich. To explain variance in SRich within the studyarea, we chose a mix of environmental, disturbance and pro-ductivity variables that varied on different scales (for variabledetails, see Table 1).

Large Scale

We used dams, small harbours, or coastal parts lackingmarshes to separate regions along the coast (see Fig. 1). Bythe variable Region, we wanted to capture possible large-scalegeographical gradients and environmental heterogeneityalong the coast. We regarded factors for which we had nodata in detail—like age of the marsh, exposition along thecoast, tidal range, foreland structure, salinity of inundationwater or soil type—to be similar within a region and presum-ably different from the next region. To likewise take environ-mental heterogeneity from sea to land into account, we intro-duced the variable Extension using length and number of thebeds within the sedimentation fields to estimate the extensionof each transect from land to sea and assigned the transectto one of three extension categories. To roughly consid-er the distance to the shoreline, we separated each transectfrom land to sea into three parts and categorised each plot withrespect to its position along the transect (variable Position).Management in the studied salt marshes was tracked by theNational Park Administration in 1988 (Stock unpublished),accounting for the variable Management in our study. Thedifferent management regimes were combinations of grazingintensity and maintenance/abandonment of the artificial drain-age system which interacted and could thus not be separated.The management regime was mostly the same along entire

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Table 1 Explanatory variables which were related to species richness (SRich) of salt marshes in the study area, recorded for 2,691 plots

Variable Category code Unit Description regarding category andinformation on species’ Ellenberg’sindicator values, respectively

Out of2,691 plots

Code Derivation Type n %

Region Regions along the coast, Categorical Reg_Rickel Rickelsbüll 44 1.6

listed from North toSouth (see Fig. 1)

Reg_LüKoog Lübke-Koog 95 3.5

Reg_Marien Marienkoog 133 4.9

Reg_OsKoog Osewoldter 133 4.9

Reg_NissHH Sönke-Nissen-Koog and HamburgerHallig

280 10.4

Reg_NordBu Nordstrand Bight 316 11.7

Reg_EiderN North side of Eiderstedt 305 11.3

Reg_Tümlau Tümlau Bight 293 10.9

Reg_Ehsten Ehstensiel 97 3.6

Reg_Hering Heringsand 112 4.2

Reg_AugVik Auguste-Viktoria 248 9.2

Reg_DiekKo Dieksanderkoog 365 13.6

Reg_KWiNeu K-W-Koog+Neufeld 270 10.0

Extension Extension of foreland Categorical Ext_small Small extension (x ≤ 400 m; approximately) 845 31.4

Ext_medium Medium extension (400 m< x ≤ 800 m;approximately)

997 37.0

Ext_large Large extension (800 m < x; approximately) 849 31.5

Position Position of plot withinforeland

Categorical Pos_landward Landward position (plot within the first thirdrelative to foreland extension)

1157 43.0

Pos_middle Middle position (plot in the middle thirdrelative to foreland extension)

827 30.7

Pos_seaward Seaward position (plot in the last thirdrelative to foreland extension)

707 26.3

Management Management regimeofficially categorised1988 (Stock unpublished)

Categorical Man_intensive High-density grazing (x>3 sheep/ha,including lambs)

2495 92.7

Man_nograzing No grazing in combination withabandonment of artificial drainage ditches

196 7.3

Grazing De facto grazing on the plotlevel, among othersinfluenced by behaviourof sheep

Categorical Grz_intensive Indication of high-density sheep grazing 1358 50.5

Grz_moderate Indication of low-density sheep grazing 405 15.1

Grz_nograzing (Almost) no indication of sheep grazing 928 34.5

Bare Size of bare patches inthe plot

Categorical Bar_equal Bare patches of roughly equal size, evenlydense vegetation

978 36.3

Bar_differing Bare patches considerably differing in size,vegetation showing gaps

1713 63.7

Vegetation-Cover Percentage cover ofvegetation

Continuous %

Vegetation-Height Vegetation height Categorical Veg_short Short vegetation (x ≤ 15 cm; mainly this height) 1258 46.7

Veg_medium Vegetation of medium height (15 cm< x ≤30 cm; mainly this height)

674 25.0

Veg_tall Tall vegetation (30 cm < x; mainly this height) 759 28.2

Evenness Evenness (Pielou 1966) Continuous Dimensionless

Micro-Topography Micro-topography of plot Categorical Mic_Flat Flat parts, not regularly trenched by ditches 302 11.2

(for a sketch, seeSuchrow et al. 2012a)

Mic_Ditch Ditch or pool 273 10.1

Mic_DitchEdge Ditch edge, elevated from ditch 132 4.9

Mic_Bed Marsh bed 794 29.5

Mic_BedEdge Marsh bed edge, lower than the marsh bed 281 10.4

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transects, but some transects cut across (mainly two) differentmanagement regimes.

Small Scale

Within the salt marshes, grazing behaviour of sheep differedbecause, for example, the sheep avoided some species whenbrowsing or they stayed less frequently in parts far off inexpansive salt marsh areas (cf. Jensen 1985). To take this intoaccount, we categorised the de facto grazing on each plot(variable Grazing) by rating the behaviour of the sheep(browsing marks, hoof prints, droppings). To determineinformation about disturbances (e.g. wrack deposition,recent ditching activities, overgrazing), we considered thepatchiness of the bare ground (variable Bare). Furthermore,we selected three vegetation variables: both vegetation cover(variable Vegetation-Cover) and vegetation-height (variableVegetation-Height) as a surrogate for productivity (cf. Houle2005); and additionally, we calculated Pielou’s evenness (Pielou1966; variable Evenness) per plot that allows for direct compar-ison of the dominance structure even if plots have differentspecies numbers, using PAST 2.10 (Hammer et al. 2001).

We categorised each plot with regard to the micro-topography (variable Micro-Topography) in addition to theelevation in relation to MHT (variable Elevation-MHT), totake into account small-scale differences regarding environ-mental factors. To characterise the main environmental con-ditions on the plots, we calculated unweighted meanEllenberg’s indicator values for moisture, salinity and nitrogenon the basis of species’ indicator values (Ellenberg et al.

1992). This practice is a common surrogate for direct mea-surements of environmental factors (Diekmann 2003;Ellenberg et al. 1992), although results can be biased(Zelený and Schaffers 2012). Ellenberg et al. (1992) assignedvascular plant species of Central Europe to ordinal scales forseven climatic and edaphic factors (for ranges of the indicatorvalues, see Table 1). In the following, we refer to the meanindicator values as “moisture” (variable EIV_F), “salinity”(variable EIV_S) and “nitrogen” (variable EIV_N).

Data Analyses

Significance of Explanatory Variables

For each categorical explanatory variable, we carried out aKruskal-Wallis test to check for differences in SRich betweenthe differentiated categories. This non-parametric approachwas required because, for most variables, the data werenot normally distributed. To identify significant pairwisedifferences between variables’ categories, we conductedpost hocmultiple Mann–WhitneyUtests (α=0.05; Bonferronicorrection applied for multiple tests; see Siegel and Castellan1988). For these analyses, we used STATISTICA 8.0(StatSoft, Inc. 2007).

To investigate the general trend of SRich in relation to thegradients of our continuous variables, we used HOF-modelling (Huisman-Olff-Fresco models; Huisman et al.1993; see also Oksanen and Minchin 2002a, 2002b). In thisapproach, a set of five hierarchical models of increasingcomplexity allows for calculation of linear to non-symmetric

Table 1 (continued)

Variable Category code Unit Description regarding category andinformation on species’ Ellenberg’sindicator values, respectively

Out of2,691 plots

Code Derivation Type n %

Mic_BedPile (Mostly central) part of marsh bed, piled uphigher than other parts of bed

418 15.5

Mic_Hummock Hummock, or sheep dam piled up 308 11.4

Mic_DikeFoot Foot of the dike (part of the dike foot downto where the ditches begin)

183 6.8

Elevation-MHT Elevation of plot in relationto mean high tide (MHT)in 1988-1990

Continuous cm

EIV_F Mean Ellenberg's indicatorvalue for moisture

Continuous Dimensionless 1 (on extremely dry soils) to 10 (on frequentlyinundated soils)

EIV_S Mean Ellenberg's indicatorvalue for salinity

Continuous Dimensionless 0 (halophobe, non-persistent if subjected tosaline spray or water) to 9 (halophytes, inextremely saline conditions)

EIV_N Mean Ellenberg's indicatorvalue for nitrogen

Continuous Dimensionless 1 (on extremely infertile sites) to 9 (in extremelyrich conditions)

For categorical variables, number and proportion of plots per category is given. Naming of regions is derived from the geographic names along the coast.Grazing management according to Stock et al. (2005); Ellenberg’s indicator values according to Ellenberg et al. (1992); the actual ranges of species’indicator values within the study area are outlined. For further explanations, see text, Fig. 2 and Suchrow et al. (2012a)

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response curves: I – no change along the gradient, II – mono-tone increasing/decreasing, III – monotone increasing/decreasing with plateau, IV – symmetric unimodal, V – skewedunimodal response. Accordingly, up to four parameters areincluded in the model by maximum likelihood estimationprocedures. To determine the best fitting model out of the setof models, we used the Akaike information criterion which issufficient for large n (Burnham and Anderson 2004). Based onour SRich data, the resulting curves display the estimatedspecies richness along the environmental gradients. For eachgradient, the ‘optimum’ was estimated, with highest calculatedspecies richness (HOF Top) as response. Additionally, wecalculated the central lower and upper borders (below andabove optimum) giving the points where the response is thee−0.5 times fraction of the maximum response (Heegaard 2002).For fitting the HOF models, we used the package ‘HOF’(written by F. Jansen, available at http://geobot.botanik.uni-greifswald.de/download; see Jansen 2008) in the statisticalcomputing environment R (R Development Core Team 2012).

Regression Tree Analysis

To evaluate which explanatory variables in which combina-tions mainly contribute to variance in SRich, we carried outregression tree analysis (RTA; Breiman et al. 1984; Clark andPregibon 1992) using the modelling software DTREG(Sherrod 2010). Tree-based statistical models are fitted by arecursive partitioning learning algorithm. For every binarysplitting of the data set, all values of the target variable aretried as a split value, and each explanatory variable is tried tobest maximise the heterogeneity between the resulting subsets(De’ath and Fabricius 2000). Thus, starting with the entiredata set, the resulting subsets become increasingly smaller andmore homogenous. For each node, the sum of variance servesas a measure of goodness of fit, the more reduced the better. Toavoid overfitting, a pruning process follows, subsequentlyremoving branches of the large tree to get simpler but increas-ingly better fitting trees. The final tree—preferably simple andincluding only few variables—has the smallest cross-validated relative error and should explain a high proportionof variance (Breiman et al. 1984).

We used 10-fold cross-validation (Breiman et al. 1984),which is an often applied pruning procedure (cf. De’ath andFabricius 2000; Tyler et al. 2009). To select the optimal treesize, we allowed for pruning back by the 1-SE rule (Breimanet al. 1984), meaning that the cross-validated error of theresulting tree is no more than one standard error from theminimal cross-validated error value (Sherrod 2010). Within atree, the influence of the explanatory variables is given aspercentage variable importance, relative values with the mostimportant variable scaled to 100 %.

To finally obtain an optimal tree, we started with a13-variable-model including all 13 variables that proved to

be (significantly) correlated with SRich comparing this modelwith single-models including just one variable at a time. Step-by-step, we then checked for improving the model by exclud-ing further variables out of the variable pool, constructingmodels with fewer variables in various combinations. Tocounter-check, we started with the best 2-variable-model,including one more variable at a time in various combinations.The optimal tree (for our detailed approach, see OnlineResources ESM 4, 5, 6 and 7, with explanatory notes) shouldexplain a high proportion of variance in SRich within our dataset and should reveal the combinations of variables bestexplaining the particular pattern of SRich values.

Autocorrelation

To test for autocorrelation when relating SRich to our contin-uous explanatory variables, we used the Durbin-Watson test.A Durbin-Watson coefficient near 0 indicates strong positive,near 4 strong negative, and near 2 no hint to any autocorrela-tion of the regression residuals. Analyses were carried outusing STATISTICA 8.0 (StatSoft, Inc. 2007). To test forspatial autocorrelation, we used a representative subset ofour data — 423 plots along 31 transects for which we hadrecorded the geographical coordinates in the context of anoth-er study (Suchrow et al. 2012a,b). We calculated Moran’s Ivalues (see Legendre 1993) for both the raw SRich data andthe residuals of our final regression tree model. A Moran’s Ivalue (significant with p≤0.05) near −1 indicates strongnegative and near +1 strong positive spatial autocorre-lation, and a value near 0 no correlation or random spatialdistribution. To evaluate the relationship between SRichvalues and increasing distance between plots (lag distance),we examined correlograms (see Legendre 1993). These aresignificant only with at least one pvalue≤α (Bonferroni cor-rection for multiple comparisons with α=0.05/number ofdistance classes tested). Analyses were carried out usingSAM v4.0 (Rangel et al. 2010).

Results

Autocorrelation

The Durbin–Watson coefficients provided no significant indi-cation of autocorrelation of the residuals when relating SRichonly to the productivity variables (DW=1.79; p=0.10), to thethree environmental variables plus variable Elevation-MHT(DW=1.85; p=0.07), or most importantly, to the five contin-uous explanatory variables included in our final regressiontreemodel (DW=1.83; p=0.08).When analysing the residualsfrom our final regression tree model for spatial autocorrela-tion, Moran’s I values (significant for some distance classes)were even smaller than for the raw data, oscillating along the

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zero line with the strongest “positive” value for the smallestdistance class. Hence, any spatial or other autocorrelation thatwas still present in our data was unlikely to have significantlybiased our model results.

Variation in SRich

In total, we recorded 92 species, whose frequency variedwidely over the 2,691 plots (Online Resource ESM 1).SRich per plot ranged from 1 to 20 (mean±SD=4.9±2.7).S. europaea (the second-most frequent species) and S. anglicawere building the expanded stands of the pioneer zone. Thevegetation types of the low marsh were mostly dominated byP. maritima (the most frequent species), or by Atriplexportulacoides. Dominating the high marsh, Artemisiamaritima, F. rubra and Juncus gerardii continuously tookover with increasing elevation and, in turn, were replacedhigher up by E. athericus. Apart from this general trend,species occurrence and dominance pattern differed mosaic-like due to specific environmental conditions in combinationwith management and grazing. Additionally, zonation andspecies assemblages showed some recurrent pattern due tothe regular drainage system (cf. Fig. 2).

Single Explanatory Variables Driving SRich

Large Scale

For variable Region (H=139.40, p<0.001; Fig. 3a), only onethird of our multiple pairwise comparisons showed significantdifferences between categories regarding SRich (OnlineResource ESM 2a). Furthermore, the highest richness (maxi-mum, 20) occurred in two regions apart from one another(Reg_Rickel and Reg_NordBu), and SRich values fluctuatedalong the coast, with nomain trend from, for example, south tonorth along the entire coast. For variable Extension (H=2.22,p=0.33), differences regarding SRich between categories werenot significant. Thus, we excluded this variable from furtherRTA. For variable Position, SRich values of all three categories(H=216.42, p<0.001; Fig. 3b) differed significantly from eachother, increasing from sea to land. Variable Management re-vealed no significant differences in SRich (H=3.65, p=0.06;Fig. 3c), being only slightly higher under non-grazing condi-tions (Man_intensive 4.9; Man_nograzing 5.6). Due to theuneven plot number though (only 196 plots for categoryMan_nograzing), possibly existing differences in SRich be-tween the categories might not have been revealed. Thus, wedid not exclude this variable from our following RTA.

Small Scale

All three categories of variableGrazing (H=434.57, p<0.001;Fig. 3d) significantly differed pairwise regarding SRich. The

lowest mean value resulted for Grz_nograzing (3.7) and thehighest forGrz_intensive (5.8). The two categories of variableBare differed significantly (H=67.11, p<0.001; Fig. 3e) inSRich, Bar_equal plots having slightly higher richness thanBar_differing (mean, 5.4 versus 4.7). With regard to variableVegetation-Cover, SRich showed a unimodal response (modelV; HOF Top=6.7), with a wide central part (48.3–100.0;Fig. 4a, Online Resource ESM 3). Only towards veryhigh cover values (optimum=88.0 %) did the modelled curveslope down relatively steeply. For variable Vegetation-Height(H=342.39, p<0.001; Fig. 3f), multiple pairwise comparisonsrevealed significant differences in SRich between Veg_short(mean, 5.8) versus both Veg_medium and Veg_tall (samemean, 4.2). With regard to variable Evenness, SRich showeda unimodal response (model V; HOF Top=6.0; Fig. 4b,Online Resource ESM 3). Towards low evenness values, thecurve sloped steeply (right skewed) but showed a wide centralpart (0.1–1.0).

VariableMicro-Topography (H=697.49, p<0.001; Fig. 3g,Online Resource ESM 2b) showed increasing SRich fromditches upwards to beds, to hummocks, and up to highestvalues for Mic_DikeFoot. For variable Elevation-MHT(Fig. 4c, Online Resource ESM 3), the HOF model wasmonotonic with plateau (model III), the richness increasing(HOF Top=7.2) with increasing elevation in relation to MHT.The other HOF models (Fig. 4d, e and f, Online ResourceESM 3) were skewed unimodal (model V) with optima be-tween 5.5 and 6.3. For variables EIV_F (HOF Top=6.9) andEIV_S (HOF Top=7.0; model III only slightly poorer thanmodel V), the curves showed a steep decrease towards highmean indicator values. In contrast, the curve for variableEIV_Nwas lower (HOF Top=5.9) and with a less pronounceddecrease towards low mean indicator values.

Overall, 13 explanatory variables showed relevance inexplaining variance in SRich values within our data set. Anyinteractions between the explanatory variables, though,remained unconsidered so far.

SRich Explained by Interaction of Explanatory Variables

Our final regression tree model included six variables inhierarchical order (EIV_F, EIV_S, Evenness, EIV_N, Regionand Elevation-MHT) that together explained 68.5 % of vari-ance within our SRich data (Online Resource ESM 7). Allother explanatory variables did not contribute to either im-proving the model or enhancing the proportion of varianceexplained. In the tree (Fig. 5), the explanatory variable EIV_Fgenerated the main split of the entire data set, indicating theprominent role of moisture (mainly flooding frequency andduration, but also soil moisture) in explaining variance inSRich. The resulting main tree branches were remarkablydifferent in degree of branching. SRich on plots with lowmoisture (Node 2; SRich=6.28) was more than twice as high

Estuaries and Coasts

as on plots with high moisture (Node 3; SRich=2.41). Theselatter plots tended either to monospecific stands (lowEvenness) with the lowest SRich of all (Node 156; SRich=1.00), or to few species being less dominant (highEvenness)—slightly more species where salinity was low(Node 160; SRich=3.42), and less where salinity was high(Node 161; SRich=2.00). On the other main branch, fromNode 2 downwards, high salinity combinedwith lowmoistureresulted in fewer species (Node 102; SRich=3.83) than withhigh moisture (Node 103; SRich=5.10). Starting from Node 4(low EIV_S), plots with high moisture were either (lowEvenness) dominated by a single species with few accompa-nying species (Node 60; SRich=4.05) or (high Evenness) werecolonised by more species that were less dominant (Node 61;SRich=6.20). Following the other branch via Node 6 (lowEIV_F), several plots were split off due to high nitrogen (Node9; SRich=4.60) from plots with considerably higher SRich(Node 8). The latter were further divided, and from those plotswith high salinity, a few plots (low Evenness) were dominatedby a single species accompanied by very few if any otherspecies (Node 28; SRich=1.83), whereas on the remainingplots (high Evenness) the species were less dominant. Wherenitrogen was low on these latter plots, SRich was slightlylower (Node 30; SRich=6.13). Where nitrogen was high,SRich was even lower on plots with low moisture (Node 38;SRich=4.95) but remained higher on plots with high moisture(Node 39; SRich=7.57). From Node 10 (low EIV_S), plots of

five regions were separated (Node 12; SRich=7.08) fromthose of six regions with a nearly twice as high SRich. Fromthis latter group, however, few plots at low elevations showedsimilar SRich (Node 22; SRich=8.14) in contrast to all re-maining plots showing the highest SRich of all (Node 23;SRich=13.37).

Generally, species richness tended to be high on plots witha combination of low values for moisture, salinity and nitro-gen, and with high evenness—and especially high in half ofthe regions when plots were lying high in relation to meanhigh tide.

Discussion

Variance in SRich

Species richness was on average 4.9 per m2 within a rangebetween 1 and 20 which is generally in accordance with otherstudies from Wadden Sea salt marshes (e.g. Bos et al. 2002;Esselink et al. 2002; Kiehl et al. 2007). P. maritima, F. rubra,S. anglica, A. stolonifera, S. europaea and E. athericus werethe most dominant species, sometimes forming monospecificstands but frequently occurring together with few other spe-cies (see Online Resource ESM 1). Apart from these compet-itive species, most of the 92 species recorded occurred in lowabundance. This is in accordance with the general trend in

Fig. 3 Species richness (SRich)related to seven categoricalexplanatory variables, on thebasis of 2,691 1 m2 plots recordedin German mainland North Seasalt marshes: a Region (orderedfrom north (left) to south), bPosition, c Management, dGrazing, e Bare, f Vegetation-Height, g Micro-Topography(ordered with increasing median).Displayed: Median, quartiles(box), minimum and maximum(whiskers); different letters on topdenote significant differences(p<0.05) between categories,based on multiple pairwiseMann–Whitney Utests (pvaluesBonferroni-corrected; Siegel andCastellan 1988); for significantdifferences regarding variablesRegion and Micro-Topography,see Online Resource ESM 3. Forvariable details, see Table 1

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coastal marshes, where species richness is generally lowwith a single or few dominant species (Adam 1993;Esselink et al. 2009). Nevertheless, worldwide speciesrichness in salt marshes varies considerably (Bos et al. 2002;Davy et al. 2011; Gough et al. 1994; Sánchez et al. 1998;Sharpe and Baldwin 2009) albeit comparisons between stud-ies might be constrained because diversity is scale-dependent(cf. Dengler 2012).

Regression Tree Analysis to Explain SRich Patterns

As an advantage of this study, our large data set allowed forwell-defined regression tree analyses (Clark and Pregibon1992; Moore et al. 1991), an approach that we used for thefirst time to evaluate general ecological rules within the com-plex gradient network explaining plant species richness pat-terns in salt marshes. The great advantage of tree-basedmodels is their ability to deal with many variables—

categorical and continuous—at once (Iverson and Prasad1998; McKenzie and Ryan 1999), regardless of the kind ofinteractions among the explanatory variables and/or with thetarget variable (cf. Iverson and Prasad 1998; Scull et al. 2005;Tyler et al. 2009), and which is why RTA is not prone to(spatial) autocorrelation (cf. Andersen et al. 2000). Thus, weeven could mix environmental, disturbance and productivityvariables as explanatory variables (cf. García et al. 1993;Grace and Pugesek 1997; Houle 2005). Furthermore, in com-parison to classical (multiple) regression models, trees areeasy to interpret (Clark and Pregibon 1992), can reveal inter-actions that would have remained invisible (cf. Andersen et al.2000), and show off the hierarchical order of the explanatoryvariables (cf. Tyler et al. 2009). Our final regression treemodel (Fig. 5; Online Resource ESM 7) explained 68.5 %of variance in SRich in the study area and revealed a set of sixvariables that in hierarchical order mainly explained richnesspatterns: moisture, salinity, evenness, nitrogen, region andelevation in relation to MHT. Studying the relevance of eachsingle explanatory variable with respect to SRich served as abackground, but it is the model that considerably improves theunderstanding of species richness patterns that we outline inthe following.

SRich Explained on a Small Scale

In salt marshes worldwide, species richness is typically in-creasing along elevational gradients (e.g. Gough et al. 1994;Houle 2005; Moeslund et al. 2011; Sánchez et al. 1996; Zedleret al. 1999). In our tree model, this was reflected by thebranching from Node 1 to 10. Generally, the elevationalgradient is related to flooding (frequency and duration), salin-ity and nutrient supply. The interactions of these environmen-tal factors, however, form a complex gradient network(Ranwell 1972; Adam 1993; Sánchez et al. 1996) that wasfor our study area reflected by the multi-branching of our treemodel. The environmental variables moisture, salinity andnitrogen explained a high proportion of variance (each approx.50 %) in species richness and acted multiply as splittingvariables, even high up along the tree branches. This indicatedboth strong interactions among them and their overridinginfluence on species richness. The recurrent interruption, es-pecially, of the elevational gradient (Fig. 2) resulted in addi-tional variation in environmental conditions on a small scaleand thus might have further contributed to the great explana-tory importance of the environmental variables. Evenness wasa slightly less powerful variable, the respective splits revealingstrong shifts in dominance structure. In contrast, elevation wasthe least important variable, being the splitting variable onlyonce at a low tree end. The relatively high proportion ofvariance explained by elevation (slightly more than evenness,each approx. 35 %), however, pointed to the tight relationbetween elevation and environmental variables. Apparently,

Fig. 4 HOFmodels for species richness (SRich), fitted byHuisman-Olff-Fresco modelling (HOF; Oksanen and Minchin 2002a, 2002b; Jansen2008) on the basis of 2,691 1 m2 plots recorded in German mainlandNorth Sea salt marshes, in relation to six explanatory variables: a vege-tation cover (Vegetation-Cover), b evenness (Evenness), c elevation inrelation to mean high tide (MHT), and Ellenberg’s mean indicator valuefor d moisture (Ellenberg et al. 1992; EIV_F), e salinity (EIV_S), and fnitrogen (EIV_N). For a summary of model types and parameters of theresponse curves, see Online Resource ESM 3

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the direct environmental variables were overriding the indirectvariable elevation. Decreasing from low to high marsh, soilsalinity in the study area was closely related to the watersalinity (Bockelmann and Neuhaus 1999). Mainly the physi-ological constraints of high salinity and severe flooding limitspecies occurrence and richness at the low end of theelevational gradient (Nodes 102; 156, 160, 161; cf. Bertnessand Ellison 1987; Ranwell 1972). Under these extreme con-ditions (cf. Davy et al. 2011; Sánchez et al. 1998), only fewspecies could prosper, like S. anglica, S. europaea andP. maritima. They formed extended, often monospecific standstypical for Wadden Sea and other European salt marshes(Beeftink 1977; Dijkema 1983), whereas in American andCanadian Atlantic salt marshes mostly Spartina species dom-inate in similar conditions (e.g. Bertness and Ellison 1987;Houle 2005). With less severe moisture and salinity conditionsand increasing resource availability—with increasing eleva-tion—the physiological constraints were increasinglyoutweighed by interspecific competition (cf. Bertness and

Ellison 1987; Ranwell 1972). Consequently, more speciescould potentially grow (cf. Gough et al. 1994; Grace andPugesek 1997). The respective trend of increasing speciesrichness was displayed downwards along the tree branches.With further decreasing salinity and increasing elevation, how-ever, richness remained (nearly) constant (see HOF-models),which is a typical response of species richness along environ-mental stress gradients towards the strictly confined “zerostress” end (Jansen and Oksanen 2013). Any further differ-ences in species richness resulted now due to different nitrogensupply (Nodes 9/10, 30/31) and shifts in dominance (Nodes28/29). E. athericus, for example, could profit from highnitrogen availability, mostly restricted to high redox potentialand high elevation (cf. Davy et al. 2011), preferably onungrazed parts (cf. Bos et al. 2002; Esselink et al. 2009). Incontrast, dominance of species like P. maritima and F. rubraoften allowed for growth of inferior competitors like Armeriamaritima, Glaux maritima, Plantago maritima or Spergulariamedia, and thus for higher species richness (cf. Kiehl et al.

Fig. 5 Regression tree model on the basis of 2,691 plots recorded inGerman mainland North Sea salt marshes, explaining 67.8 % of variancein species richness (SRich). Tree was cross-validated (relative error=31.1 %), pruned to 14 terminal nodes, and was built on the combinationof six variables, though 13 explanatory variables were included in themodelling. Variable importance: EIV_F (100.0 %), EIV_S (20.5 %),Evenness (12.1 %), EIV_N (7.3 %), Region (6.9 %) and Elevation-MHT(1.6 %). Starting with the entire data set, the plots were subsequently

separated by a splitting value or categories into two groups of increasinghomogeneity. Per node, number of plots (N), mean species richness(SRich) and standard deviation (Std.dev.) are given. For variables, seeTable 1.*for better legibility mentioned here and not in the tree boxes, theregions split up into: Node 12–Region={Reg_AugVik, Reg_Hering,Reg_KWiNeu, Reg_OsKoog, Reg_Tümlau} Node 13–Region={Reg_DiekKo,Reg_EiderN, Reg_LüKoog, Reg_Marien, Reg_NordBu,Reg_Rickel}

Estuaries and Coasts

2007; Wanner et al. 2013). Highest richness was reachedwhere at highest elevations species of the adjacent (dike)grassland were out-competing the salt marsh species (Nodes22/23; cf. Adam 1993; Beeftink 1977). Predominantly, how-ever, species richness patterns in the study area were explainedby environmental heterogeneity on a small scale.

SRich Explained on a Large Scale

Along the coast and perpendicular to the shoreline, a mix ofdifferent factors influenced species richness by further addingto environmental heterogeneity and competitive interactionson a large scale, such as general topography, sedimentation anderosion, soil type, redox potential or salinity of inundationwater (Beeftink 1977; Davy et al. 2011; Houle 2005;Sánchez et al. 1998; Wigand et al. 2003). These factors areintegrated in complex large-scale gradients and might be di-rectly and indirectly affected by the management (e.g. Olffet al. 1997). Perpendicular to the shoreline, we found a generalincrease in species richness from sea to land, which is inaccordance with results from Houle (2005) and Zedler et al.(1999). Nevertheless, variable Position was not included intoour tree model. Management did not even affect SRich signif-icantly. This contradicts with previous studies (e.g. Bos et al.2002; Esselink et al. 2009; Schröder et al. 2002), however,finding either higher or lower species numbers in ungrazedparts compared with parts under high-density grazing andmight indicate that disturbance gradients are specific in everyregion (cf. Bos et al. 2002). Apparently, within our study area,the variable Region covered various factors—including man-agement—that contributed to large-scale environmental het-erogeneity and thus to differences in species richness betweenthe regions (cf. Beeftink 1977; Dijkema 1983; Houle 2005;Sharpe and Baldwin 2009; Wigand et al. 2003). Consequently,Region was included into our model as the only large-scalevariable but, contrary to our assumption, had not much explan-atory power. It was the splitting variable only once, far downthe tree, concerning mostly high marsh plots that in someregions (Node 13) showed comparatively high richness values.These could have different reasons, such as: (a) grazed andungrazed parts in the same region (Reg_NordBu; cf. Olff andRitchie 1998; Jensen 1985; Schröder et al. 2002) or (b) saltmarsh in transition with freshwater influxes (Reg_EiderN;Reg_NordBu; cf. Esselink et al. 2002; Engels and Jensen2009; Sharpe and Baldwin 2009). Overall, our model showsthat species richness within the study area was also explainedon a large scale but only to a minor degree.

Conclusions

Regression tree analysis proved a useful approach for study-ing species richness of salt marshes when considering various

explanatory variables. Using this approach, we could identifya set of six variables varying on a small or a large scale, that—interacting—in various combinations contributed to mainlyexplain variance in species richness in German mainlandWadden Sea salt marshes. Generally, SRich tended to behigher on plots exhibiting a combination of low moisture,salinity and nitrogen values, with high evenness, and to behighest in some of the regions when plots were lying high inrelation to mean high tide. Our model revealed that speciesoccurrence and richness was limited predominantly on a smallscale, strongly driven by the environmental gradients in com-bination with interspecific competition. Little variance wasexplained along large-scale gradients. Management did noteven affect SRich significantly, although the de facto grazingintensity caused differences in species richness and composi-tion. Our model improves the understanding of species rich-ness patterns in salt marshes. The data provide a valuablereference to evaluate changes in species richness in the studyarea. Furthermore, the tree-encoded general rules can be usedto predict species richness, for example, with regard to chang-es in management or SLR as those directly and indirectlyresult in changed environmental conditions and shifts in in-terspecific competition on a small and large scale.

Acknowledgements Our thanks go to all management and contractingauthorities for supporting this project. Special thanks are due to theSchleswig-Holstein Agency for Coastal Defence, National Park andMarine Conservation National Park Authority for the excellent andconstructive cooperation and for providing data from the automaticallyrecording tide gauges along the North Sea coast. Furthermore, we thank afew anonymous hands for field assistance and data collection, JensOldeland for helpful advice regarding statistics and Wiebke Schoenbergfor support in GIS. Thanks go also to a few anonymous colleagues, theeditor and two anonymous reviewers for their helpful comments improv-ing the manuscript, and to Tom Maxfield for carefully reviewing thecorrect use of English.

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