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Journal of Vegetation Science 17: 397-406, 2006© IAVS; Opulus Press Uppsala.

Plant characteristics are poor predictors of microsite colonizationduring the first two years of primary succession

Walker, Lawrence R.1*; Bellingham, Peter J.2 & Peltzer, Duane A.2

1Department of Biological Sciences, University of Nevada Las Vegas, 4505 Maryland Parkway, Las Vegas, NV 89154-4004, USA; 2Landcare Research, PO Box 69, Lincoln 8152, New Zealand;

E-mail [email protected]; [email protected]:*Corresponding author; Fax +1 702 895 3956; E-mail [email protected];

Website http://biology.unlv.edu/NewPage/faculty/LR%20Walker.htm

AbstractQuestions: Do plant characteristics predict microsite coloni-zation in severe habitats dominated by abiotic factors? Spe-cifically, does colonization of microsites differ among shrubs,forbs and grasses or between wind- and water-dispersed plants,non-native and native plants, or N-fixing and non-N-fixingplants?Location: Kowhai River floodplain, Kaikoura, South Island,New Zealand.Methods: Five microsite characteristics were measured for >1000 individuals representing 27 colonizing plant species on atwo-year old surface of a primary succession on a New Zea-land floodplain. The microsite characteristics included surfacecontour (convex, concave, or flat), the position of the plant(e.g., upstream, downstream) relative to the closest rock with> 20 cm maximum dimension, the distance to that same rock,the depth of the base of the stem below the surface of a planeresting on the adjacent microrelief, and soil particle size (gravel,pebbles or sand).Results: All plants preferred concave microsites near largerocks relative to systematically placed null points. We foundno clear preferences for microhabitats by dispersal mode,native vs. non-native status, or plants with or without nitrogen-fixing symbionts, but grasses preferentially colonized fine soilparticles. Highly variable responses among species contrib-uted to these results. Better predictability of microsite prefer-ence was obtained for individual species than for plants groupedby characteristics.Conclusions: Our results suggest that in severe habitats withstrong abiotic filters and low microsite availability, such asfound in early primary succession, coarse categories of speciescharacteristics are poor predictors of colonization success.

Keywords: Dispersal mode; Filter; Floodplain; Functionalgroup; Growth form; Native; New Zealand; Nitrogen fixation;Non-native; Surface contour.

Nomenclature: Allan (1961) with amendments suggested byConnor & Edgar (1987); Webb et al. (1988); Edgar & Connor(2000).

Introduction

How plant species assemble and interact in succes-sion is a long-standing enigma with important implica-tions for ecological theory and restoration. One way toexplore this issue is to examine plant characteristics(e.g., traits, attributes) thought to control colonizationdynamics. Microsites that provide favourable condi-tions for seed germination and seedling establishmentare often scarce on the infertile, unstable substrates thatdominate primary succession. This scarcity of suitablesites, combined with a lack of significant biologicallegacies, makes primary seres excellent but often over-looked systems in which to examine colonization dy-namics and search for evidence of community assembly(Booth & Larson 1999; del Moral & Lacher 2005).Unpredictable disruptions of the abiotic environmentare likely to predominate over biological determinism insevere environments (Walker & del Moral 2003). Incontrast, most generalizations about plant assembly in-voke biotic filters (sensu Díaz et al. 1998) and derivefrom fertile, stable sites such as wetlands (Wilson &Whittaker 1995; Weiher et al. 1998), lawns (Wilson &Roxburgh 1994) and old fields (Fukami et al. 2005).

Successful colonization of microsites can have long-lasting implications for ecosystem function (Díaz et al.2004), species composition (van Andel et al. 1993),competitive success (Matthews 1992; S.D. Wilson 1999)and long-term successional trajectories (Walker & delMoral 2003). Initial partitioning of microsites amongcolonizing plant species in succession is thought to becontrolled by dispersal dynamics (Wood & del Moral1987; van der Valk 1992), order of arrival (Egler 1954),physiological tolerances of abiotic stresses (Chapin 1993)and relative success of certain plant characteristics (i.e.growth form and functional groups; Gitay & Noble1997; Prach & Pyšek 1999) but it is still unresolved towhat degree plant species that share similar characteris-tics tend to colonize similar microsites (Bell 2005).

398 Walker, L.R. et al.

Plant characteristics that determine microsite coloniza-tion are numerous and interact with environmental fac-tors, perhaps generating a unique response for eachspecies (Gleason 1926; Weiher & Keddy 1999; J.B.Wilson 1999). We propose that examining severe habi-tats such as those found in primary succession can helpaddress whether plant characteristics are useful to pre-dict successful microsite colonization.

In habitats typical of primary succession, strongabiotic filters apply at a fine scale (e.g. infertility, insta-bility and drought) with the result that species mustcolonize the same scarce, favourable microsites.Microsite availability in primary succession is thereforelow because only a small fraction of all potential habi-tats can be colonized (Fig. 1, below). We suggest thatmicrosite availability is higher in more fertile and stablehabitats where abiotic filters (sensu Díaz et al. 1998)weaken and a higher proportion of microsites are avail-able for colonization (Jones & del Moral 2005). In muchmore fertile secondary succession, biotic filters (e.g.competition, herbivory) assume greater importance thanabiotic filters, and microsite availability again declines.We expect that plant characteristics influencing coloni-zation will be least predictable in environments withlow microsite availability where abiotic filters applymost strongly. Under such conditions typical of earlyprimary succession, all species colonize a limited set ofmicrosites, but the availability of these microsites isgoverned by strong stochastic factors.

We chose four plant characteristics from the manyphysiological and ecological attributes commonly usedto explain species distributions (Wardle et al. 1999;Weiher et al. 1999; Loreau et al. 2001). These character-istics were most likely to explain differences in coloni-zation among species and offset the predominantlystochastic drivers in early primary succession. First,plant growth forms may have more success establishingon soils of a particular particle size (e.g. grasses on siltor sand, herbs on gravel and trees in rock crevices;Grubb 1986, but see exceptions in Walker & del Moral2003). Second, light, wind-dispersed seeds are likely toreach potential microsites before those dispersed bywater or animals, yet be less tolerant of desiccation(Chapin 1993). Third, non-natives are often successfulinvaders of severely disturbed sites, including floodplains(D’Antonio et al. 1999; Williams & Wiser 2004), per-haps due to tolerance of a wider or different range ofmicrosites than native plants (Crawley 1997). Finally,plants with nitrogen-fixing symbionts (hereafter N-fix-ers) clearly impact their environment once established(Bradshaw 2004) but may need particularly mesic andfertile microsites to supply them with adequate nutrientsto begin the process of N-fixation (Uliassi & Reuss2002). There are few detailed examinations of howthese four characteristics interact to influence coloniza-tion of microsites by plants in the context of primarysuccession despite their pivotal role in understandingthe patterns and trajectories of primary seres (Walker &del Moral 2003).

In this study, we described the microsites colonizedby plants at the very earliest stage of primary successionon a New Zealand floodplain (Bellingham et al. 2005).Such studies with no biological legacy are rare (Walker& del Moral 2003; del Moral & Ellis 2005), particularlywhen conducted within several years of a severe distur-bance. This study addressed the following question:Does colonization of microsites differ among shrubs,forbs and grasses or between wind- and water-dispersedplants, non-native and native plants, or N-fixing andnon-N-fixing plants? If plant characteristics that guidecolonization and community assembly in habitats domi-nated by abiotic factors can be determined, this knowl-edge will help predict succession, guide restoration ef-forts in severely damaged habitats and provide data tohelp interpret how the role of such characteristics variesacross resource and disturbance gradients (Weiher &Keddy 1995; White & Jentsch 2004). However, to theextent that stochastic factors overrule deterministic onesin directing successional trajectories, prediction andrestoration goals will remain broadly defined.

Fig. 1. Microsite status with respect to colonization in threehabitats where abiotic and biotic filters vary in relative strength.Habitat A has strong abiotic filters (e.g., instability, infertility,drought) and few microsites available for colonization. Habi-tat B has intermediate abiotic and biotic filters and the mostmicrosites available. Habitat C has few microsites availabledue to strong biotic filters (e.g., competition, herbivory). Emptycircles: unavailable due to abiotic filters, hatched circles:available, filled circles: unavailable due to biotic filters.


Methods

Study site

We conducted our study on a floodplain in the KowhaiRiver Valley, eastern South Island, New Zealand (42°20'S, 173°33' E, 220-280 m a.s.l.). The surrounding Sea-ward Kaikoura mountain range (maximum altitude 2600m) is underlain by highly fractured greywacke sand-stone with some argillite and tuffaceous sandstone. Therange is bounded by the extremely active Hope Fault(Bull 1991), and has uplift rates at its crest of about 6-10mm.a–1 (Van Dissen & Yeats 1991). The annual precipi-tation at Snowflake Spur (1050 m, 5 km NNE of thestudy site) was evenly distributed throughout our studyperiod (2002-2003 annual mean: 1287 mm; not differ-ent from the long-term mean). The mean annual tem-perature at Kaikoura, on the coast 12 km from the studysite, is 12.1 °C (mid-summer mean: 16.2 °C, mid-wintermean: 7.7 °C). Steep hill slopes in the Kowhai Rivercatchment are highly erodible, and yield sediment of ca.5000 t.km–2.a–1 (O’Loughlin & Pearce 1982) despiteextensive cover by native trees and shrubs (Kunzeaericoides, Melicytus ramiflorus, Sophora microphylla,Olearia paniculata, Griselinia littoralis; Wardle 1971).The mildly seasonal climate with rare frosts allowssome germination to occur throughout the year. Thefloodplain is sparsely vegetated, floods every 10-15 yrand the newly deposited surfaces are low in nitrogen.Buddleja davidii and Coriaria arborea shrubs dominateearly stages of floodplain succession to woody vegeta-tion and form canopies up to 5 m tall on older terraces(Bellingham et al. 2005).

Sampling design

We randomly located eleven, 50-m long transects inDecember 2003 on floodplain surfaces along a 2-kmstretch of the Kowhai River Valley. We chose sites thatwere > 1m above water level and had new deposits ofgravel following a major flood (226 mm rainfall) on 12January 2002 and no developed vegetation or othertransects within at least 5 m. Therefore, all measure-ments were on plants that had colonized our initiallybare sites from seed within the previous two-year pe-riod. The sites were not flooded in the intervening twoyears and total plant cover in December 2003 wasconsistently < 20%. On each transect, we placed nine7.07-m2 circular plots (1.5 m radius) spaced 4 m apartand analysed microsite characteristics of the largestindividual (maximum height: 30 cm) of each vascularplant species present in each plot. We assumed that thelargest individuals established earliest and inhabited themost favourable microsites. First, we evaluated the re-

productive status (presence of flowers, fruits or seeds)of all targeted plants. Next, we measured the followingfive microsite characteristics, modified from Jumpponenet al. (1999): the surface contour (convex, concave orflat: < 1 cm vertical relief) of a 10 cm × 10 cm plotcentred on each plant stem; the position of the plantrelative to the closest rock (within 1.5 m) with a maximumdimension of > 20 cm (upstream, downstream, to theside or on top of rock; upstream and downstream of rock– each encompassed a 90° arc); distance to that samerock; depth of the base of the stem below the surface ofa 1-m2 plane centered on the plant and allowed to rest onadjacent micro-relief; and soil particle size: the percentof the surface area of the 10 cm × 10 cm plot that wascovered by gravel (> 16 mm), small pebbles (2-16 mm)or sand (< 2 mm) to the nearest 5%. An additional nullpoint was evaluated at the centre of each circular plot,ignoring any plants that might be present, in order todetermine how representative actual plant habitats wereof available habitats. Preference for each microsite wascompared for plants grouped by growth form, native ornot to New Zealand, N-fixer or not (1328 plants), dis-persal mode (wind and water only; n = 1216), and forindividual species. Finally, seed mass was comparedamong microsites for our initial set of four plant charac-teristics and also for the most common 27 plant species.

Statistical analyses

We used path analysis to determine which micrositefactors were so strongly correlated that they should beremoved prior to further analyses (Mitchell 2001). Theseanalyses revealed that soil particle size variables werestrongly negatively correlated (r = 0.92) and that depthwas positively correlated with sand cover (r = 0.90),hence we used only sand cover (soil < 2mm) in subse-quent analyses. We constructed logistic regression mod-els that contrasted plant growth form (shrub, forb, grass),water- or wind-dispersal modes, native or non-nativestatus and N-fixing or non-N-fixing species. Full mod-els had three levels of a categorical response variable(null points, i.e. absence, and two levels of plant charac-teristics) or a four-level response in the case of plantgrowth forms (Agresti 1996; Floyd 2001; Quinn &Kenough 2002). We also analysed separate models whichexcluded the null points; these were binomial (two-levelcategorical) response models for all but growth forms,which had three levels. Here the dependent variableswere categorical, having one level per plant characteris-tic. Before each analysis, proportional data were arcsine-square-root transformed (soil particle size) and distanceto rock was log-transformed. We restricted these analy-ses to include two-way interactions because higher-level interactions were either intractable (i.e., conver-


gence criteria could not be met with this data set) orwere not clearly linked to our hypotheses (e.g. Jumpponenet al. 1999). We applied Bonferroni corrections (n = 10tests based on the same dataset) so P = 0.005 was used asthe significance level for these models. Initial analysesrevealed that presence-absence of plant groupings orspecies did not vary with transect (P > 0.25 in all cases),so data were analysed across transects.

Similarly, a model considering the response of allspecies was constructed to determine whether speciesdiffered in their microsite preferences. This analysiswas restricted to those species having > 15 observationsacross all transects (summarized in App. 1; n = 27 plantspecies). These 27 plant species comprised 92.1% of thetotal number of individuals observed; an additional 35species were present in lower numbers. To assess howcommon plant species differed in their microhabitatpreferences, we also carried out separate logistic regres-sion analyses for each of those species having ≥ 45individual observations (n = 14 species). Log-trans-formed seed mass for each species was compared amonggrowth forms, dispersal mode, native/non-native, andN-fixer/non N-fixer groups by ANOVA or t-test.

Results

We analysed the microsites of 1328 individuals of27 plant species that had invaded our initially bare plotswithin the first two years of primary succession and 97null points on the Kowhai River in New Zealand (Ta-ble 1, App. 2). Most of the plants (76.8 %) were non-native species. Plants were more likely than null pointsto be on concave surfaces (P < 0.001; Table 1, App. 2).However, models evaluating microsite occupancy byplants in our four groups: from one of three growthforms (shrubs, grasses, forbs), dispersed by wind vs.water, native vs. non-native, or N-fixing vs. non-N-fixing, explained < 10% of variance (P < 0.001; App.2). Logistic regression analysis of presence vs. nullpoints across all common species produced the strong-est predictions of plant microsite preference (L-R χ2

43= 167.9, U = 0.28). U is analogous to r2 from logisticregressions.

Models which excluded null points and contrastedplant groups typically explained < 5% of the variancein plant microsite preference (summarized in App. 3).Plant growth forms varied significantly in their responseto soils < 2 mm (grasses were more common on finesubstrates), distance to rock (forbs were more commonfurther from rocks), and the interaction between surfacecontour and distance to the nearest large rock (grasseswere found more often on flat surfaces further fromlarge rocks). Additional logistic regression models

Table 1. Summary of substrate and microsite characteristics for non-native, native and “nulls” (controls) in a floodplain primarysuccession, Kowhai River Valley, New Zealand; counts: individuals within columns and categories (percentage of total individuals);means (+1 SD) for each cell. N is the total number observed.

Category Growth Form Dispersal mode Invasive status N-fixer status

Forb Grass Shrub Wind Water Native Non-native N-fixer Non-N-fixer Null Total

N 668 313 347 884 332 307 1021 332 996 97 1425

Counts (%)Surface contour1

Convex 25 (3.7) 18 (5.8) 6 (1.7) 27 (2.2) 15 (1.2) 9 (2.9) 40 (3.9) 15 (4.5) 34 (3.4) 35 (36.1) 84 (5.9) Flat 123 (18.4) 54 (17.3) 42 (12.1) 136 (11.2) 60 (4.9) 26 (8.5) 193 (18.9) 60 (18.1) 159 (16.0) 31 (32.0) 250 (17.5) Concave 520 (77.8) 241 (77.0) 299 (86.2) 721 (59.2) 257 (21.1) 272 (88.6) 788 (77.2) 257 (77.4) 803 (80.6) 31 (32.0) 1091 (76.6)

Position2

Upstream 166 (24.9) 67 (21.4) 75 (21.6) 196 (16.1) 87 (7.1) 70 (22.8) 238 (23.3) 87 (26.2) 221 (22.2) 14 (16.9) 322 (22.8) Beside 256 (38.3) 139 (44.4) 127 (36.6) 359 (29.5) 115 (9.5) 102 (33.2) 420 (41.1) 115 (34.6) 407 (40.9) 30 (36.1) 552 (38.7) Downstream 246 (36.8) 107 (34.1) 145 (41.8) 329 (27.0) 130 (10.7) 135 (44.0) 363 (35.6) 130 (39.2) 368 (36.9) 34 (41.0) 532 (37.2) On top 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 5 (6.0) 19 (1.3)

Means (SD)Distance3 16.4 (21.8) 15.8 (25.1) 13.8 (22.7) 15.5 (24.1) 16.5 (20.9) 12.2 (20.9) 16.6 (23.3) 16.5 (20.9) 15.3 (23.5) 24.7 (26.8) 16.2(23.2)Depth4 12.4 (8.5) 13.3 (9.5) 13.7 (9.2) 13.5 (9.1) 11.8 (8.7) 14.4 (8.9) 12.5 (8.9) 11.8 (8.7) 13.3 (9.0) 7.3 (7.0) 12.5(8.9)Particle size5

< 2 mm 25.8 (21.5) 29.1 (25.0) 28.2 (22.3) 25.2 (21.9) 29.1 (23.6) 24.9 (21.3) 27.9 (22.9) 29.2 (23.6) 26.6 (22.2) 17.9 (19.4) 26.6 (22.5) 2-16 mm 31.3 (20.4) 27.7 (19.2) 26.5 (18.6) 29.8 (19.8) 28.7 (20.2) 26.1 (18.3) 30.1 (20.1) 29.3 (20.2) 29.4 (19.6) 31.1 (24.2) 29.3 (20.1) > 16 mm 42.8 (23.4) 43.4 (24.8) 45.3 (23.1) 45.0 (23.4) 42.0 (24.2) 9.1 (22.2) 41.9 (23.9) 42.0 (24.2) 44.1 (23.5) 51.3 (32.9) 44.1(24.5)

1Surface contour in a 10 cm × 10 cm plot centered on an individual plant species; 2Position of individual plant relative to the nearest rock ≥ 20 cm in diameter,upstream and downstream encompassed a 90° arc centered on the rock; 3Horizontal distance (cm) between the plant and rock edge; 4Depth (cm) from plant baseto a plane fitted over a 1m × 1 m area centred on the plant; 5Cover (%) by particle size class of surface substrate.


comparing plant dispersal modes, native vs. non-na-tive status and N-fixing vs. non-N-fixing plants did notdisplay strong effects of microsite factors on plantpresence (App. 3).

Interspecific variation within plant characteristicsexplained why models of microsite occupancy by indi-vidual species were superior predictors (U > 0.22;

Table 2). As one example, variation in the distance torock in microsites occupied by native (range: 8.8–15.1cm) and non-native species (range: 11.4–19.7 cm)resulted in substantial overlap. As another example,there was also variation in the percentage of N-fixers(range 60–96%) and non-N-fixers (range 61–95%;Table 3) on concave surfaces. Similarly, while overall

Table 2. Species-specific results from logistic regression of microsite characteristics predicting species presence for plant specieshaving 45 or more observations. N is the total number of individuals observed. U is analogous to r2 from logistic regressions. Allother data shown are Likelihood-ratio (L-R) χ2 values from logistic regressions for individual species. Analyses included two-wayinteractions were possible. Further details of these analyses are in the methods section. Factors are described in Table 1.

Growth Dispersal N-fixer? N U Surface Position Distance Depth Soilform mode contour <2mm

L-R χ2 df 2 3 1 1 1

Native speciesCoriaria arborea Shrub Water Y 47 0.48 34.1*** 1.40 16.8*** 1.30 0.73Epilobium microphyllum Forb Wind N 65 0.43 29.4*** 6.74* 19.0*** 0.05 7.53*Kunzea ericoides Shrub Wind N 45 0.46 27.0*** 0.03 15.9*** 2.67 5.78**Olearia paniculata Shrub Wind N 55 0.48 33.4*** 6.08* 8.47** 1.60 1.61

Non-native speciesAnthoxanthum odoratum Grass Wind N 58 0.42 15.5*** 0.19 10.2** 1.01 3.64Arenaria serpyllifolia Forb Wind N 99 0.22 16.8*** 4.44 10.5** 1.26 0.75Buddleja davidii Shrub Wind N 94 0.29 33.6*** 3.37 10.9** 1.08 0.18Cerastium fontanum Forb Wind N 73 0.27 25.2*** 2.53 13.6*** 0.58 1.10Echium vulgare Forb Other N 57 0.30 3.16 2.78 7.80** 0.49 32.1***Hieracium pilosella Forb Wind N 73 0.42 39.0*** 1.74 7.55** 4.06* 0.84Schedonorus phoenix Grass Wind N 54 0.28 5.14 2.17 14.3*** 0.83 7.00**Trifolium arvense Forb Water Y 96 0.28 22.1*** 2.28 0.12 0.42 0.01Trifolium repens Forb Water Y 76 0.30 4.69 2.61 0.02 0.14 14.1***Vicia hirsuta Forb Water Y 46 0.29 19.5*** 0.4 11.5*** 0.24 0.11

*: P < 0.05; **: P < 0.01; ***: P < 0.001.

Table 3. Percentages (surface contours) and means (+1 SD; distance, soil) for microsite factors influencing plant species presencethat were significant in logistic regression analyses (Table 3). Factors are described in Table 1. Further details for species are in Table2 and App. 1.

Surface contoursGrowth N-form Dispersal fixer? Concave Convex Flat Distance (cm) Soil (< 2mm)

Native speciesCoriaria arborea Shrub Water Y 96 2 2 9.6 (15.3) 22.1 (16.2)Epilobium microphyllum Forb Wind N 88 11 1 13.4 (20.1) 15.7 (12.2)Kunzea ericoides Shrub Wind N 91 7 2 8.8 (11.1) 25.8 (18.1)Olearia paniculata Shrub Wind N 94 4 2 15.1 (27.2) 26.9 (24.5)

Non-native speciesAnthoxanthum odoratum Grass Wind N 76 14 10 11.4 (18.4) 29.4 (25.8)Arenaria serpyllifolia Forb Wind N 72 25 3 19.7 (27.6) 26.7 (22.4)Buddleja davidii Shrub Wind N 82 17 1 18.1 (28.5) 25.0 (21.6)Cerastium fontanum Forb Wind N 75 23 2 15.8 (22.0) 20.5 (16.3)Echium vulgare Forb Other N 63 28 9 18.6 (20.5) 41.0 (22.0)Hieracium pilosella Forb Wind N 95 4 1 14.3 (20.3) 19.0 (17.1)Schedonorus phoenix Grass Wind N 61 30 9 16.4 (25.6) 34.7 (25.5)Trifolium arvense Forb Water Y 81 16 3 16.7 (19.6) 22.2 (18.4)Trifolium repens Forb Water Y 60 30 10 18.5 (17.7) 36.0 (23.7)Vicia hirsuta Forb Water Y 83 15 2 15.9 (23.0) 28.9 (27.3)


native vs. non-native models were poor predictors ofpresence at a microsite (U = 0.10; P < 0.001; App. 2),individual native (especially shrub) species had rela-tively high predictability (mean U = 0.46; Table 2);this was greater than those of individual non-nativespecies (mean U = 0.30; unpaired t12 = 4.65, P < 0.001;Table 2).

Wind was the principal mode of dispersal (N = 18species) with fewer species dispersed by animals (N =5) and water (N = 4). Microsites with water-dispersedseeds tended to be deeper and closer to rocks than nullpoints (unpaired t20 = 4.38; P < 0.001). Seed mass (asan indicator of dispersal ability, App. 1) was a poorpredictor of the microsites occupied by different plantgroups or individual species with two exceptions. Seedsof N-fixers were on average heavier than seeds of non-N-fixers (unpaired t24 = 3.13; P = 0.005) and seeds ofwind-dispersed plants were lighter than those of water-dispersed plants (unpaired t22 = 3.35; P = 0.003). Seedmass did not differ among growth forms (ANOVA,F2,23 = 0.15, P = 0.86) or between native and non-native species (unpaired t24 = 0.95; P = 0.035). Inaddition, species with heavy seeds were more likely tooccur on sand (particle size < 2 mm; r2 = 0.26, P =0.008) than on larger particle sizes.

Discussion

Prediction of favourable microsites for plant coloni-zation during early primary succession was more suc-cessful at the level of individual species than for speciesgrouped by four widely-used characteristics (growthform, dispersal mode, native or not, N-fixer or not).Similar results have been found on volcanoes (del Moral& Wood 1993; Jones & del Moral 2005) and glacialmoraines (Jumpponen et al. 1999); but see Whittaker(1993). Models that contrasted plant groups or speciespredicted small but significant differences in micrositepreference. Microsite preferences in our study ac-counted for a maximum of only 11% of the variance inspecies presence or absence when classification byplant characteristics was used but up to 48% at theindividual species level (Table 2). These results sup-port the contention that, at least in severe and unpre-dictable habitats, coarse categories of species charac-teristics are inadequate to predict the complex processof colonization (Belyea & Lancaster 1999; Walker &del Moral 2003).

While we examined microsite colonization with re-spect to categorical plant characteristics, another ap-proach is to consider plant characteristics as continuousvariables (e.g. specific leaf area, relative growth rate,foliar nitrogen; Cornelissen et al. 2003) as in other

studies of plant assembly (e.g. Stubbs & Wilson 2004).Such an approach may resolve the differences in micrositecolonization between plants within our coarse catego-ries, but even continuous variables can vary more acrossfunctional plant types than within them (Wright et al.2005), complicating their predictive value. We expectthat in early primary succession microsite availabilityand other variables such as dispersal (Wood & delMoral 2000; Lewin et al. 2003), site amelioration (Birks1980; Hodkinson et al. 2002) or stochastic factors(Walker & del Moral 2003; del Moral & Ellis 2004) willdominate over plant-based characteristics (Fig. 1).

Abiotic (e.g. stability, fertility, drought) and biotic(e.g. competition, herbivory) factors that vary acrossgradients of environmental severity limit microsite avail-ability in the colonization phase of primary succession(Walker & Chapin 1987; Burrows 1990; Walker & delMoral 2003). Under such severe conditions as thosefound in this study, fine scale abiotic filters dominate(Fig. 1) so that there are few microsites available forcolonization. As a result, generalizations about speciescharacteristics that affect colonization are unlikely un-der these conditions. We expect that most successfulpredictions about community assembly during coloni-zation will come from relatively favourable habitatswhere biotic filters sort plant species by their particularcharacteristics (Fig. 1; J.B. Wilson 1999).

The colonization process of plants integrates disper-sal, germination and seedling survival (Harper 1977)and incorporates both abiotic and biotic filters in severehabitats such as early primary succession (Matthews1992; Walker & del Moral 2003). Microsites that wereconcave and close to large rocks were those most likelyto be colonized and this supports earlier results fromstudies of volcanoes (Titus & Tsuyuzaki 2003) andglacial moraines (Jumpponen et al. 1999; Niederfriniger-Schlag & Erschbamer 2000). Concave surfaces can trapseeds (Matlack 1989, Chambers et al. 1991), protectseedlings from frost (Jumpponen et al. 1999) and wind(Niederfriniger-Schlag & Erschbamer 2000; Walker etal. 2003) and reduce mortality from drought (Chapin1993; Walker & Powell 1999). Large rocks can havesimilar effects (Jumpponen et al. 1999) and triggereddies for downstream water movement or upstreamwinds that favour seed deposition. Despite favourabletemperatures and precipitation for germination and seed-ling growth at our site during most of the year, the sandysoils can dry out quickly, and strong winds can destabilizethe surface particles.

Plants of different growth forms did show somepreferences among microsites. The preferential coloni-zation of grasses on fine soil particles supports Grubb’s(1986) hypothesis, probably because of particle sizeimpacts on moisture retention in primary succession


(Jumpponen et al. 1999; Elmarsdottir et al. 2003). Incontrast, shrubs and forbs did not show any clear patternof microsite preference. There were no significant dif-ferences in microsite distribution among our other threetypes of plant characteristics with models that removedthe null points (App. 3). The lack of any micrositepreferences between wind- and water-dispersed seedssuggests that differentiation among species occurs laterin succession (Walker & del Moral 2003).

We found no evidence to support our assertion thatnon-native species dominate this floodplain by estab-lishing in different microsite types than native species.Individual native species were generally more predict-able in their microsite use than non-natives (higher U-values, Table 2) but this pattern disappeared whenmicrosite use by native species was compared as a groupto that of non-native species. The dominance of non-native plants in this and other New Zealand floodplains(Williams & Wiser 2004) is likely due to their moreprolific seed production, more widespread dispersal,faster maturation and growth rates and perhaps also tobetter competitive abilities than native plants (Rejmánek& Richardson 1996; Williamson & Fitter 1996; but seecautionary note by Vilà & Weiner 2004). For example,62% of the non-natives but only 3% of the natives werereproductive within 2 years, suggesting that highpropagule pressure explains the high densities of non-natives at our site. Introduced grazers may also havereduced abundance, hence seed output, of nativefloodplain colonists (Williams 1989), resulting in domi-nance by non-native species in the vicinity of open areasand hence pre-emption of microsites by non-native seeds.Colonization and germination can be higher in a differ-ent set of microsites than those that favour later growthand reproduction (Jones & del Moral 2005), yet in ourstudy system, non-native annuals tend to dominate un-less the native N-fixing shrub Coriaria establishes andsubsequently suppresses them (Bellingham et al. 2005).

Nitrogen-fixing plants were not, as a group, found indifferent microsites from non-N-fixers, although theywere represented by both native and non-nativecolonizers. There are several potential explanations forthis: first, potential N-fixers often do not actually fix N(Sprent & Sprent 1990), perhaps explaining the lack ofany distinct habitat preference (e.g. survival in lowernutrient sites than non-fixers). Second, microsite fertil-ity may be the most important determinant of N-fixersuccess (Grubb 1986), although Walker (1993) foundno correlation between dominance by N-fixers and ini-tial levels of soil N. Early successional soils on theKowhai River have very low N-levels but do providemoderate levels of soil P (Bellingham et al. 2005), asituation that might favour widespread colonization byN-fixers (Chapin et al. 1994).

Community assembly is a mix of stochastic (e.g.,disturbance, dispersal) and deterministic (e.g., facilita-tion, competition) events (Temperton & Hobbs 2004),and both biotic and abiotic filters; these processes aredynamic, changing in time as species and developingecosystems interact with them (Fattorini & Halle 2004).For example, microsite preferences for colonization canvary from year to year depending on the dryness of theclimate (Titus & del Moral 1998). Most evidence forgeneralizations about how species combine to formcommunities (assembly rules; Belyea & Lancaster 1999)applies where biotic filters predominate. Attempts tofind such rules in the early colonization phase of pri-mary succession have not been successful (Walker &del Moral 2003; del Moral & Lacher 2005). Instead,stochastic factors such as the availability of microsites,as demonstrated in this study, or dispersal of propagules(Levin et al. 2003; Dirnböck & Dullinger 2004), appearto override the importance of biotic interactions. To theextent that colonization dynamics determine the subse-quent development of ecosystems, multiple trajectoriesare possible at any given site. As a consequence, suc-cessful predictions or manipulations of succession (as inrestoration) are difficult and will only be those withbroadly defined targets of structure and function ratherthan those that focus on detailed species composition.

Acknowledgements. We thank Bianca Heidecker and ChrisMorse for field assistance, Gaye Rattray for data entry, PeterWilliams for assistance with seed data and the New ZealandDepartment of Conservation for access to study sites. AriJumpponen, Jan Lepš, Roger del Moral, Jonathan Titus, DavidWardle, Peter Williams and several anonymous reviewersgreatly improved the manuscript with their comments. TrishLockington (Environment Canterbury) kindly provided rain-fall data. The New Zealand Foundation for Research, Scienceand Technology supported this research with contractC09X0502. Further support was provided by the InternationalScience and Technology Fund of the Royal Society of NewZealand and by a sabbatical leave to Lawrence Walker fromthe University of Nevada.

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Received 9 November 2005;Accepted 27 March 2006.

Co-ordinating Editor: J. Lepš.

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